Help for package hdcuremodels

Title:

Penalized Mixture Cure Models for High-Dimensional Data

Version:

0.0.1

Date:

2024-06-11

Description:

Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

License:

MIT + file LICENSE

Encoding:

UTF-8

Depends:

R (≥ 4.2.0)

Imports:

doParallel, flexsurv, flexsurvcure, foreach, ggplot2, ggpubr, glmnet, knockoff, mvnfast, parallel, plyr, methods, survival

RoxygenNote:

7.3.1

Suggests:

knitr, rmarkdown

VignetteBuilder:

knitr

LazyData:

true

NeedsCompilation:

Packaged:

2024-06-11 14:12:41 UTC; archer.43

Author:

Han Fu [aut], Kellie J. Archer

[aut, cre]

Maintainer:

Kellie J. Archer <archer.43@osu.edu>

Repository:

CRAN

Date/Publication:

2024-06-13 10:10:06 UTC

AUC for cure prediction using mean score imputation

Description

This function calculates the AUC for cure prediction using the mean score imputation (MSI) method proposed by Asano et al.

Usage

AUC(object, newdata, cure_cutoff = 5, model.select = "AIC")

Arguments

object

a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, cv_cureem.

newdata

an optional data.frame that minimally includes the incidence and/or latency variables to use for predicting the response. If omitted, the training data are used.

cure_cutoff

cutoff value for cure, used to produce a proxy for the unobserved cure status; default is 5.

model.select

for models fit using curegmifs or cureem any step along the solution path can be selected. The default is model.select = "AIC" which calculates the predicted values using the coefficients from the model having the lowest AIC. Other options are model.select = "mAIC" for the modified AIC, model.select = "cAIC" for the corrected AIC, model.select = "BIC", model.select = "mBIC" for the modified BIC, model.select = "EBIC" for the extended BIC, model.select = "logLik" for the step that maximizes the log-likelihood, or any numeric value from the solution path. This option has no effect for objects fit using cv_curegmifs or cv_cureem.

Value

Returns the AUC value for cure prediction using the mean score imputation (MSI) method.

References

Asano, J., Hirakawa, H., Hamada, C. (2014) Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data. Pharmaceutical Statistics, 13:357–363.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
testing <- temp$Testing
fit <- curegmifs(Surv(Time, Censor) ~ .,
                  data = training, x.latency = training,
                  model = "weibull", thresh = 1e-4, maxit = 2000,
                  epsilon = 0.01, verbose = FALSE)
AUC(fit)
AUC(fit, newdata = testing)

AML test data

Description

Duration of complete response for 40 cytogenetically normal AML patients and a subset of 320 transcript expression from RNA-sequencing.

Usage

amltest

Format

A data frame with 40 rows (subjects) and 322 columns:

cryr: duration of complete response in years
relapse.death: censoring indicator: 1 = relapsed or died; 0 = alive at last follow=up
ENSG00000001561: normalized expression for indicated transcript
ENSG00000005249: normalized expression for indicated transcript
ENSG00000006757: normalized expression for indicated transcript
ENSG00000007062: normalized expression for indicated transcript
ENSG00000007968: normalized expression for indicated transcript
ENSG00000008283: normalized expression for indicated transcript
ENSG00000008405: normalized expression for indicated transcript
ENSG00000008441: normalized expression for indicated transcript
ENSG00000010295: normalized expression for indicated transcript
ENSG00000011028: normalized expression for indicated transcript
ENSG00000011198: normalized expression for indicated transcript
ENSG00000012779: normalized expression for indicated transcript
ENSG00000012817: normalized expression for indicated transcript
ENSG00000013306: normalized expression for indicated transcript
ENSG00000013725: normalized expression for indicated transcript
ENSG00000018189: normalized expression for indicated transcript
ENSG00000022267: normalized expression for indicated transcript
ENSG00000023171: normalized expression for indicated transcript
ENSG00000023909: normalized expression for indicated transcript
ENSG00000029639: normalized expression for indicated transcript
ENSG00000047634: normalized expression for indicated transcript
ENSG00000049192: normalized expression for indicated transcript
ENSG00000053524: normalized expression for indicated transcript
ENSG00000058056: normalized expression for indicated transcript
ENSG00000060138: normalized expression for indicated transcript
ENSG00000061918: normalized expression for indicated transcript
ENSG00000065809: normalized expression for indicated transcript
ENSG00000065923: normalized expression for indicated transcript
ENSG00000068489: normalized expression for indicated transcript
ENSG00000069020: normalized expression for indicated transcript
ENSG00000070404: normalized expression for indicated transcript
ENSG00000071894: normalized expression for indicated transcript
ENSG00000072422: normalized expression for indicated transcript
ENSG00000073605: normalized expression for indicated transcript
ENSG00000076555: normalized expression for indicated transcript
ENSG00000080823: normalized expression for indicated transcript
ENSG00000089723: normalized expression for indicated transcript
ENSG00000090382: normalized expression for indicated transcript
ENSG00000090975: normalized expression for indicated transcript
ENSG00000100068: normalized expression for indicated transcript
ENSG00000100077: normalized expression for indicated transcript
ENSG00000100299: normalized expression for indicated transcript
ENSG00000100376: normalized expression for indicated transcript
ENSG00000100418: normalized expression for indicated transcript
ENSG00000100448: normalized expression for indicated transcript
ENSG00000100596: normalized expression for indicated transcript
ENSG00000100916: normalized expression for indicated transcript
ENSG00000102409: normalized expression for indicated transcript
ENSG00000102760: normalized expression for indicated transcript
ENSG00000104689: normalized expression for indicated transcript
ENSG00000104946: normalized expression for indicated transcript
ENSG00000105518: normalized expression for indicated transcript
ENSG00000105808: normalized expression for indicated transcript
ENSG00000106367: normalized expression for indicated transcript
ENSG00000106526: normalized expression for indicated transcript
ENSG00000106546: normalized expression for indicated transcript
ENSG00000106780: normalized expression for indicated transcript
ENSG00000107104: normalized expression for indicated transcript
ENSG00000107742: normalized expression for indicated transcript
ENSG00000107798: normalized expression for indicated transcript
ENSG00000107816: normalized expression for indicated transcript
ENSG00000107957: normalized expression for indicated transcript
ENSG00000109674: normalized expression for indicated transcript
ENSG00000110076: normalized expression for indicated transcript
ENSG00000110237: normalized expression for indicated transcript
ENSG00000110492: normalized expression for indicated transcript
ENSG00000110799: normalized expression for indicated transcript
ENSG00000111275: normalized expression for indicated transcript
ENSG00000112773: normalized expression for indicated transcript
ENSG00000113504: normalized expression for indicated transcript
ENSG00000114268: normalized expression for indicated transcript
ENSG00000114737: normalized expression for indicated transcript
ENSG00000115183: normalized expression for indicated transcript
ENSG00000115414: normalized expression for indicated transcript
ENSG00000115457: normalized expression for indicated transcript
ENSG00000115525: normalized expression for indicated transcript
ENSG00000116574: normalized expression for indicated transcript
ENSG00000117480: normalized expression for indicated transcript
ENSG00000119280: normalized expression for indicated transcript
ENSG00000120594: normalized expression for indicated transcript
ENSG00000120675: normalized expression for indicated transcript
ENSG00000120832: normalized expression for indicated transcript
ENSG00000120913: normalized expression for indicated transcript
ENSG00000121005: normalized expression for indicated transcript
ENSG00000121039: normalized expression for indicated transcript
ENSG00000121274: normalized expression for indicated transcript
ENSG00000123080: normalized expression for indicated transcript
ENSG00000123836: normalized expression for indicated transcript
ENSG00000124019: normalized expression for indicated transcript
ENSG00000124882: normalized expression for indicated transcript
ENSG00000126822: normalized expression for indicated transcript
ENSG00000127152: normalized expression for indicated transcript
ENSG00000129824: normalized expression for indicated transcript
ENSG00000130702: normalized expression for indicated transcript
ENSG00000131188: normalized expression for indicated transcript
ENSG00000131370: normalized expression for indicated transcript
ENSG00000132122: normalized expression for indicated transcript
ENSG00000132530: normalized expression for indicated transcript
ENSG00000132819: normalized expression for indicated transcript
ENSG00000132849: normalized expression for indicated transcript
ENSG00000133401: normalized expression for indicated transcript
ENSG00000133619: normalized expression for indicated transcript
ENSG00000134531: normalized expression for indicated transcript
ENSG00000134897: normalized expression for indicated transcript
ENSG00000135074: normalized expression for indicated transcript
ENSG00000135245: normalized expression for indicated transcript
ENSG00000135272: normalized expression for indicated transcript
ENSG00000135362: normalized expression for indicated transcript
ENSG00000135363: normalized expression for indicated transcript
ENSG00000135916: normalized expression for indicated transcript
ENSG00000136026: normalized expression for indicated transcript
ENSG00000136193: normalized expression for indicated transcript
ENSG00000136231: normalized expression for indicated transcript
ENSG00000136997: normalized expression for indicated transcript
ENSG00000137193: normalized expression for indicated transcript
ENSG00000137198: normalized expression for indicated transcript
ENSG00000138722: normalized expression for indicated transcript
ENSG00000139318: normalized expression for indicated transcript
ENSG00000140287: normalized expression for indicated transcript
ENSG00000144036: normalized expression for indicated transcript
ENSG00000144647: normalized expression for indicated transcript
ENSG00000144677: normalized expression for indicated transcript
ENSG00000145476: normalized expression for indicated transcript
ENSG00000145545: normalized expression for indicated transcript
ENSG00000146243: normalized expression for indicated transcript
ENSG00000146373: normalized expression for indicated transcript
ENSG00000147044: normalized expression for indicated transcript
ENSG00000147180: normalized expression for indicated transcript
ENSG00000148444: normalized expression for indicated transcript
ENSG00000148484: normalized expression for indicated transcript
ENSG00000149131: normalized expression for indicated transcript
ENSG00000150760: normalized expression for indicated transcript
ENSG00000150782: normalized expression for indicated transcript
ENSG00000151135: normalized expression for indicated transcript
ENSG00000151208: normalized expression for indicated transcript
ENSG00000151458: normalized expression for indicated transcript
ENSG00000152409: normalized expression for indicated transcript
ENSG00000152580: normalized expression for indicated transcript
ENSG00000152767: normalized expression for indicated transcript
ENSG00000152778: normalized expression for indicated transcript
ENSG00000153563: normalized expression for indicated transcript
ENSG00000154217: normalized expression for indicated transcript
ENSG00000154743: normalized expression for indicated transcript
ENSG00000154760: normalized expression for indicated transcript
ENSG00000154874: normalized expression for indicated transcript
ENSG00000156381: normalized expression for indicated transcript
ENSG00000157107: normalized expression for indicated transcript
ENSG00000157240: normalized expression for indicated transcript
ENSG00000157873: normalized expression for indicated transcript
ENSG00000157978: normalized expression for indicated transcript
ENSG00000158691: normalized expression for indicated transcript
ENSG00000159339: normalized expression for indicated transcript
ENSG00000159403: normalized expression for indicated transcript
ENSG00000159788: normalized expression for indicated transcript
ENSG00000160685: normalized expression for indicated transcript
ENSG00000160781: normalized expression for indicated transcript
ENSG00000161509: normalized expression for indicated transcript
ENSG00000162433: normalized expression for indicated transcript
ENSG00000162614: normalized expression for indicated transcript
ENSG00000162676: normalized expression for indicated transcript
ENSG00000163412: normalized expression for indicated transcript
ENSG00000163702: normalized expression for indicated transcript
ENSG00000163814: normalized expression for indicated transcript
ENSG00000164086: normalized expression for indicated transcript
ENSG00000164172: normalized expression for indicated transcript
ENSG00000164442: normalized expression for indicated transcript
ENSG00000165272: normalized expression for indicated transcript
ENSG00000166165: normalized expression for indicated transcript
ENSG00000166435: normalized expression for indicated transcript
ENSG00000166987: normalized expression for indicated transcript
ENSG00000167291: normalized expression for indicated transcript
ENSG00000167565: normalized expression for indicated transcript
ENSG00000167851: normalized expression for indicated transcript
ENSG00000168026: normalized expression for indicated transcript
ENSG00000168209: normalized expression for indicated transcript
ENSG00000168502: normalized expression for indicated transcript
ENSG00000168939: normalized expression for indicated transcript
ENSG00000169203: normalized expression for indicated transcript
ENSG00000169247: normalized expression for indicated transcript
ENSG00000169504: normalized expression for indicated transcript
ENSG00000169860: normalized expression for indicated transcript
ENSG00000169991: normalized expression for indicated transcript
ENSG00000170035: normalized expression for indicated transcript
ENSG00000170180: normalized expression for indicated transcript
ENSG00000170456: normalized expression for indicated transcript
ENSG00000170522: normalized expression for indicated transcript
ENSG00000170909: normalized expression for indicated transcript
ENSG00000171121: normalized expression for indicated transcript
ENSG00000171222: normalized expression for indicated transcript
ENSG00000171476: normalized expression for indicated transcript
ENSG00000171813: normalized expression for indicated transcript
ENSG00000171962: normalized expression for indicated transcript
ENSG00000172197: normalized expression for indicated transcript
ENSG00000172236: normalized expression for indicated transcript
ENSG00000173083: normalized expression for indicated transcript
ENSG00000173530: normalized expression for indicated transcript
ENSG00000173926: normalized expression for indicated transcript
ENSG00000174059: normalized expression for indicated transcript
ENSG00000174080: normalized expression for indicated transcript
ENSG00000174130: normalized expression for indicated transcript
ENSG00000174738: normalized expression for indicated transcript
ENSG00000175265: normalized expression for indicated transcript
ENSG00000175352: normalized expression for indicated transcript
ENSG00000176597: normalized expression for indicated transcript
ENSG00000179222: normalized expression for indicated transcript
ENSG00000179630: normalized expression for indicated transcript
ENSG00000179639: normalized expression for indicated transcript
ENSG00000179820: normalized expression for indicated transcript
ENSG00000180096: normalized expression for indicated transcript
ENSG00000180596: normalized expression for indicated transcript
ENSG00000180902: normalized expression for indicated transcript
ENSG00000181104: normalized expression for indicated transcript
ENSG00000182866: normalized expression for indicated transcript
ENSG00000182871: normalized expression for indicated transcript
ENSG00000183087: normalized expression for indicated transcript
ENSG00000183091: normalized expression for indicated transcript
ENSG00000184371: normalized expression for indicated transcript
ENSG00000185129: normalized expression for indicated transcript
ENSG00000185201: normalized expression for indicated transcript
ENSG00000185245: normalized expression for indicated transcript
ENSG00000185291: normalized expression for indicated transcript
ENSG00000185304: normalized expression for indicated transcript
ENSG00000185710: normalized expression for indicated transcript
ENSG00000185883: normalized expression for indicated transcript
ENSG00000185986: normalized expression for indicated transcript
ENSG00000186130: normalized expression for indicated transcript
ENSG00000186854: normalized expression for indicated transcript
ENSG00000187010: normalized expression for indicated transcript
ENSG00000187627: normalized expression for indicated transcript
ENSG00000187653: normalized expression for indicated transcript
ENSG00000187837: normalized expression for indicated transcript
ENSG00000188002: normalized expression for indicated transcript
ENSG00000188107: normalized expression for indicated transcript
ENSG00000188211: normalized expression for indicated transcript
ENSG00000188636: normalized expression for indicated transcript
ENSG00000188738: normalized expression for indicated transcript
ENSG00000188856: normalized expression for indicated transcript
ENSG00000189164: normalized expression for indicated transcript
ENSG00000189223: normalized expression for indicated transcript
ENSG00000196155: normalized expression for indicated transcript
ENSG00000196189: normalized expression for indicated transcript
ENSG00000196415: normalized expression for indicated transcript
ENSG00000196565: normalized expression for indicated transcript
ENSG00000197081: normalized expression for indicated transcript
ENSG00000197121: normalized expression for indicated transcript
ENSG00000197253: normalized expression for indicated transcript
ENSG00000197256: normalized expression for indicated transcript
ENSG00000197321: normalized expression for indicated transcript
ENSG00000197561: normalized expression for indicated transcript
ENSG00000197728: normalized expression for indicated transcript
ENSG00000197860: normalized expression for indicated transcript
ENSG00000197937: normalized expression for indicated transcript
ENSG00000197951: normalized expression for indicated transcript
ENSG00000198743: normalized expression for indicated transcript
ENSG00000198838: normalized expression for indicated transcript
ENSG00000199347: normalized expression for indicated transcript
ENSG00000200243: normalized expression for indicated transcript
ENSG00000201801: normalized expression for indicated transcript
ENSG00000203872: normalized expression for indicated transcript
ENSG00000204172: normalized expression for indicated transcript
ENSG00000205571: normalized expression for indicated transcript
ENSG00000205593: normalized expression for indicated transcript
ENSG00000208772: normalized expression for indicated transcript
ENSG00000213085: normalized expression for indicated transcript
ENSG00000213261: normalized expression for indicated transcript
ENSG00000213626: normalized expression for indicated transcript
ENSG00000213722: normalized expression for indicated transcript
ENSG00000213906: normalized expression for indicated transcript
ENSG00000213967: normalized expression for indicated transcript
ENSG00000214016: normalized expression for indicated transcript
ENSG00000214425: normalized expression for indicated transcript
ENSG00000216316: normalized expression for indicated transcript
ENSG00000220008: normalized expression for indicated transcript
ENSG00000223345: normalized expression for indicated transcript
ENSG00000224080: normalized expression for indicated transcript
ENSG00000225138: normalized expression for indicated transcript
ENSG00000226471: normalized expression for indicated transcript
ENSG00000227097: normalized expression for indicated transcript
ENSG00000227191: normalized expression for indicated transcript
ENSG00000227615: normalized expression for indicated transcript
ENSG00000228049: normalized expression for indicated transcript
ENSG00000229153: normalized expression for indicated transcript
ENSG00000230076: normalized expression for indicated transcript
ENSG00000231160: normalized expression for indicated transcript
ENSG00000231721: normalized expression for indicated transcript
ENSG00000233927: normalized expression for indicated transcript
ENSG00000233974: normalized expression for indicated transcript
ENSG00000234883: normalized expression for indicated transcript
ENSG00000236876: normalized expression for indicated transcript
ENSG00000237298: normalized expression for indicated transcript
ENSG00000237892: normalized expression for indicated transcript
ENSG00000238160: normalized expression for indicated transcript
ENSG00000239437: normalized expression for indicated transcript
ENSG00000241399: normalized expression for indicated transcript
ENSG00000241489: normalized expression for indicated transcript
ENSG00000241529: normalized expression for indicated transcript
ENSG00000244405: normalized expression for indicated transcript
ENSG00000247627: normalized expression for indicated transcript
ENSG00000249592: normalized expression for indicated transcript
ENSG00000250116: normalized expression for indicated transcript
ENSG00000250251: normalized expression for indicated transcript
ENSG00000251079: normalized expression for indicated transcript
ENSG00000253210: normalized expression for indicated transcript
ENSG00000253276: normalized expression for indicated transcript
ENSG00000254415: normalized expression for indicated transcript
ENSG00000259276: normalized expression for indicated transcript
ENSG00000260727: normalized expression for indicated transcript
ENSG00000261377: normalized expression for indicated transcript
ENSG00000264885: normalized expression for indicated transcript
ENSG00000264895: normalized expression for indicated transcript
ENSG00000267136: normalized expression for indicated transcript
ENSG00000267551: normalized expression for indicated transcript
ENSG00000267702: normalized expression for indicated transcript
ENSG00000268001: normalized expression for indicated transcript
ENSG00000268573: normalized expression for indicated transcript
ENSG00000270554: normalized expression for indicated transcript
ENSG00000270562: normalized expression for indicated transcript
ENSG00000271646: normalized expression for indicated transcript
ENSG00000273018: normalized expression for indicated transcript
ENSG00000273033: normalized expression for indicated transcript

Source

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11068580/

AML training data

Description

Duration of complete response for 306 cytogenetically normal AML patients and a subset of 320 transcript expression from RNA-sequencing.

Usage

amltrain

Format

A data frame with 306 rows (subjects) and 322 columns:

cryr: duration of complete response in years
relapse.death: censoring indicator: 1 = relapsed or died; 0 = alive at last follow=up
ENSG00000001561: normalized expression for indicated transcript
ENSG00000005249: normalized expression for indicated transcript
ENSG00000006757: normalized expression for indicated transcript
ENSG00000007062: normalized expression for indicated transcript
ENSG00000007968: normalized expression for indicated transcript
ENSG00000008283: normalized expression for indicated transcript
ENSG00000008405: normalized expression for indicated transcript
ENSG00000008441: normalized expression for indicated transcript
ENSG00000010295: normalized expression for indicated transcript
ENSG00000011028: normalized expression for indicated transcript
ENSG00000011198: normalized expression for indicated transcript
ENSG00000012779: normalized expression for indicated transcript
ENSG00000012817: normalized expression for indicated transcript
ENSG00000013306: normalized expression for indicated transcript
ENSG00000013725: normalized expression for indicated transcript
ENSG00000018189: normalized expression for indicated transcript
ENSG00000022267: normalized expression for indicated transcript
ENSG00000023171: normalized expression for indicated transcript
ENSG00000023909: normalized expression for indicated transcript
ENSG00000029639: normalized expression for indicated transcript
ENSG00000047634: normalized expression for indicated transcript
ENSG00000049192: normalized expression for indicated transcript
ENSG00000053524: normalized expression for indicated transcript
ENSG00000058056: normalized expression for indicated transcript
ENSG00000060138: normalized expression for indicated transcript
ENSG00000061918: normalized expression for indicated transcript
ENSG00000065809: normalized expression for indicated transcript
ENSG00000065923: normalized expression for indicated transcript
ENSG00000068489: normalized expression for indicated transcript
ENSG00000069020: normalized expression for indicated transcript
ENSG00000070404: normalized expression for indicated transcript
ENSG00000071894: normalized expression for indicated transcript
ENSG00000072422: normalized expression for indicated transcript
ENSG00000073605: normalized expression for indicated transcript
ENSG00000076555: normalized expression for indicated transcript
ENSG00000080823: normalized expression for indicated transcript
ENSG00000089723: normalized expression for indicated transcript
ENSG00000090382: normalized expression for indicated transcript
ENSG00000090975: normalized expression for indicated transcript
ENSG00000100068: normalized expression for indicated transcript
ENSG00000100077: normalized expression for indicated transcript
ENSG00000100299: normalized expression for indicated transcript
ENSG00000100376: normalized expression for indicated transcript
ENSG00000100418: normalized expression for indicated transcript
ENSG00000100448: normalized expression for indicated transcript
ENSG00000100596: normalized expression for indicated transcript
ENSG00000100916: normalized expression for indicated transcript
ENSG00000102409: normalized expression for indicated transcript
ENSG00000102760: normalized expression for indicated transcript
ENSG00000104689: normalized expression for indicated transcript
ENSG00000104946: normalized expression for indicated transcript
ENSG00000105518: normalized expression for indicated transcript
ENSG00000105808: normalized expression for indicated transcript
ENSG00000106367: normalized expression for indicated transcript
ENSG00000106526: normalized expression for indicated transcript
ENSG00000106546: normalized expression for indicated transcript
ENSG00000106780: normalized expression for indicated transcript
ENSG00000107104: normalized expression for indicated transcript
ENSG00000107742: normalized expression for indicated transcript
ENSG00000107798: normalized expression for indicated transcript
ENSG00000107816: normalized expression for indicated transcript
ENSG00000107957: normalized expression for indicated transcript
ENSG00000109674: normalized expression for indicated transcript
ENSG00000110076: normalized expression for indicated transcript
ENSG00000110237: normalized expression for indicated transcript
ENSG00000110492: normalized expression for indicated transcript
ENSG00000110799: normalized expression for indicated transcript
ENSG00000111275: normalized expression for indicated transcript
ENSG00000112773: normalized expression for indicated transcript
ENSG00000113504: normalized expression for indicated transcript
ENSG00000114268: normalized expression for indicated transcript
ENSG00000114737: normalized expression for indicated transcript
ENSG00000115183: normalized expression for indicated transcript
ENSG00000115414: normalized expression for indicated transcript
ENSG00000115457: normalized expression for indicated transcript
ENSG00000115525: normalized expression for indicated transcript
ENSG00000116574: normalized expression for indicated transcript
ENSG00000117480: normalized expression for indicated transcript
ENSG00000119280: normalized expression for indicated transcript
ENSG00000120594: normalized expression for indicated transcript
ENSG00000120675: normalized expression for indicated transcript
ENSG00000120832: normalized expression for indicated transcript
ENSG00000120913: normalized expression for indicated transcript
ENSG00000121005: normalized expression for indicated transcript
ENSG00000121039: normalized expression for indicated transcript
ENSG00000121274: normalized expression for indicated transcript
ENSG00000123080: normalized expression for indicated transcript
ENSG00000123836: normalized expression for indicated transcript
ENSG00000124019: normalized expression for indicated transcript
ENSG00000124882: normalized expression for indicated transcript
ENSG00000126822: normalized expression for indicated transcript
ENSG00000127152: normalized expression for indicated transcript
ENSG00000129824: normalized expression for indicated transcript
ENSG00000130702: normalized expression for indicated transcript
ENSG00000131188: normalized expression for indicated transcript
ENSG00000131370: normalized expression for indicated transcript
ENSG00000132122: normalized expression for indicated transcript
ENSG00000132530: normalized expression for indicated transcript
ENSG00000132819: normalized expression for indicated transcript
ENSG00000132849: normalized expression for indicated transcript
ENSG00000133401: normalized expression for indicated transcript
ENSG00000133619: normalized expression for indicated transcript
ENSG00000134531: normalized expression for indicated transcript
ENSG00000134897: normalized expression for indicated transcript
ENSG00000135074: normalized expression for indicated transcript
ENSG00000135245: normalized expression for indicated transcript
ENSG00000135272: normalized expression for indicated transcript
ENSG00000135362: normalized expression for indicated transcript
ENSG00000135363: normalized expression for indicated transcript
ENSG00000135916: normalized expression for indicated transcript
ENSG00000136026: normalized expression for indicated transcript
ENSG00000136193: normalized expression for indicated transcript
ENSG00000136231: normalized expression for indicated transcript
ENSG00000136997: normalized expression for indicated transcript
ENSG00000137193: normalized expression for indicated transcript
ENSG00000137198: normalized expression for indicated transcript
ENSG00000138722: normalized expression for indicated transcript
ENSG00000139318: normalized expression for indicated transcript
ENSG00000140287: normalized expression for indicated transcript
ENSG00000144036: normalized expression for indicated transcript
ENSG00000144647: normalized expression for indicated transcript
ENSG00000144677: normalized expression for indicated transcript
ENSG00000145476: normalized expression for indicated transcript
ENSG00000145545: normalized expression for indicated transcript
ENSG00000146243: normalized expression for indicated transcript
ENSG00000146373: normalized expression for indicated transcript
ENSG00000147044: normalized expression for indicated transcript
ENSG00000147180: normalized expression for indicated transcript
ENSG00000148444: normalized expression for indicated transcript
ENSG00000148484: normalized expression for indicated transcript
ENSG00000149131: normalized expression for indicated transcript
ENSG00000150760: normalized expression for indicated transcript
ENSG00000150782: normalized expression for indicated transcript
ENSG00000151135: normalized expression for indicated transcript
ENSG00000151208: normalized expression for indicated transcript
ENSG00000151458: normalized expression for indicated transcript
ENSG00000152409: normalized expression for indicated transcript
ENSG00000152580: normalized expression for indicated transcript
ENSG00000152767: normalized expression for indicated transcript
ENSG00000152778: normalized expression for indicated transcript
ENSG00000153563: normalized expression for indicated transcript
ENSG00000154217: normalized expression for indicated transcript
ENSG00000154743: normalized expression for indicated transcript
ENSG00000154760: normalized expression for indicated transcript
ENSG00000154874: normalized expression for indicated transcript
ENSG00000156381: normalized expression for indicated transcript
ENSG00000157107: normalized expression for indicated transcript
ENSG00000157240: normalized expression for indicated transcript
ENSG00000157873: normalized expression for indicated transcript
ENSG00000157978: normalized expression for indicated transcript
ENSG00000158691: normalized expression for indicated transcript
ENSG00000159339: normalized expression for indicated transcript
ENSG00000159403: normalized expression for indicated transcript
ENSG00000159788: normalized expression for indicated transcript
ENSG00000160685: normalized expression for indicated transcript
ENSG00000160781: normalized expression for indicated transcript
ENSG00000161509: normalized expression for indicated transcript
ENSG00000162433: normalized expression for indicated transcript
ENSG00000162614: normalized expression for indicated transcript
ENSG00000162676: normalized expression for indicated transcript
ENSG00000163412: normalized expression for indicated transcript
ENSG00000163702: normalized expression for indicated transcript
ENSG00000163814: normalized expression for indicated transcript
ENSG00000164086: normalized expression for indicated transcript
ENSG00000164172: normalized expression for indicated transcript
ENSG00000164442: normalized expression for indicated transcript
ENSG00000165272: normalized expression for indicated transcript
ENSG00000166165: normalized expression for indicated transcript
ENSG00000166435: normalized expression for indicated transcript
ENSG00000166987: normalized expression for indicated transcript
ENSG00000167291: normalized expression for indicated transcript
ENSG00000167565: normalized expression for indicated transcript
ENSG00000167851: normalized expression for indicated transcript
ENSG00000168026: normalized expression for indicated transcript
ENSG00000168209: normalized expression for indicated transcript
ENSG00000168502: normalized expression for indicated transcript
ENSG00000168939: normalized expression for indicated transcript
ENSG00000169203: normalized expression for indicated transcript
ENSG00000169247: normalized expression for indicated transcript
ENSG00000169504: normalized expression for indicated transcript
ENSG00000169860: normalized expression for indicated transcript
ENSG00000169991: normalized expression for indicated transcript
ENSG00000170035: normalized expression for indicated transcript
ENSG00000170180: normalized expression for indicated transcript
ENSG00000170456: normalized expression for indicated transcript
ENSG00000170522: normalized expression for indicated transcript
ENSG00000170909: normalized expression for indicated transcript
ENSG00000171121: normalized expression for indicated transcript
ENSG00000171222: normalized expression for indicated transcript
ENSG00000171476: normalized expression for indicated transcript
ENSG00000171813: normalized expression for indicated transcript
ENSG00000171962: normalized expression for indicated transcript
ENSG00000172197: normalized expression for indicated transcript
ENSG00000172236: normalized expression for indicated transcript
ENSG00000173083: normalized expression for indicated transcript
ENSG00000173530: normalized expression for indicated transcript
ENSG00000173926: normalized expression for indicated transcript
ENSG00000174059: normalized expression for indicated transcript
ENSG00000174080: normalized expression for indicated transcript
ENSG00000174130: normalized expression for indicated transcript
ENSG00000174738: normalized expression for indicated transcript
ENSG00000175265: normalized expression for indicated transcript
ENSG00000175352: normalized expression for indicated transcript
ENSG00000176597: normalized expression for indicated transcript
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Source

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11068580/

Extract model coefficients from a fitted mixture cure object

Description

coef.mixturecure is a generic function which extracts the model coefficients from a fitted mixture cure model object fit using curegmifs, cureem, cv_curegmifs, or cv_cureem.

Usage

## S3 method for class 'mixturecure'
coef(object, model.select = "AIC", ...)

Arguments

object

a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, or cv_cureem.

model.select

...

other arguments.

Value

a list of estimated parameters extracted from the model object using the model selection criterion

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- curegmifs(Surv(Time, Censor) ~ .,
                      data = training, x.latency = training,
                      model = "weibull", thresh = 1e-4, maxit = 2000, epsilon = 0.01,
                      verbose = FALSE)
coef(fit)

C-statistic for mixture cure models

Description

This function calculates the C-statistic using the cure status weighting (CSW) method proposed by Asano and Hirakawa.

Usage

concordance_mcm(object, newdata, cure_cutoff = 5, model.select = "AIC")

Arguments

object

a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, cv_cureem.

newdata

an optional data.frame that minimally includes the incidence and/or latency variables to use for predicting the response. If omitted, the training data are used.

cure_cutoff

cutoff value for cure, used to produce a proxy for the unobserved cure status; default is 5.

model.select

Value

value of C-statistic for the cure models.

References

Asano, J. and Hirakawa, H. (2017) Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data. Journal of Biopharmaceutical Statistics, 27:6, 918–932.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
testing <- temp$Testing
fit <- curegmifs(Surv(Time, Censor) ~ .,
                  data = training, x.latency = training,
                  model = "weibull", thresh = 1e-4, maxit = 2000,
                  epsilon = 0.01, verbose = FALSE)
concordance_mcm(fit)
concordance_mcm(fit, newdata = testing)

Estimate cured fraction

Description

Estimates the cured fraction using a Kaplan-Meier fitted object.

Usage

cure_estimate(object)

Arguments

object

a survfit object.

Value

estimated proportion of cured observations

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
km.fit <- survfit(Surv(Time, Censor) ~ 1, data = training)
cure_estimate(km.fit)

Fit penalized mixture cure model using the E-M algorithm

Description

Fits a penalized parametric and semi-parametric mixture cure model (MCM) using the E-M algorithm with user-specified penalty parameters. The lasso (L1), MCP, and SCAD penalty is supported for the Cox MCM while only lasso is currently supported for parametric MCMs.

Usage

cureem(
  formula,
  data,
  subset,
  x.latency = NULL,
  model = "cox",
  penalty = "lasso",
  penalty.factor.inc = NULL,
  penalty.factor.lat = NULL,
  thresh = 0.001,
  scale = TRUE,
  maxit = NULL,
  inits = NULL,
  lambda.inc = 0.1,
  lambda.lat = 0.1,
  gamma.inc = 3,
  gamma.lat = 3,
  ...
)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The response must be a survival object as returned by the Surv function while the variables on the right side of the formula are the covariates that are included in the incidence portion of the model.

data

a data.frame in which to interpret the variables named in the formula or in the subset argument.

subset

an optional expression indicating which subset of observations to be used in the fitting process, either a numeric or factor variable should be used in subset, not a character variable. All observations are included by default.

x.latency

specifies the variables to be included in the latency portion of the model and can be either a matrix of predictors, a model formula with the right hand side specifying the latency variables, or the same data.frame passed to the data parameter. Note that when using the model formula syntax for x.latency it cannot handle x.latency = ~ ..

model

type of regression model to use for the latency portion of mixture cure model. Can be "cox", "weibull", or "exponential" (default is "cox").

penalty

type of penalty function. Can be "lasso", "MCP", or "SCAD" (default is "lasso").

penalty.factor.inc

vector of binary indicators representing the penalty to apply to each incidence coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all incidence variables.

penalty.factor.lat

vector of binary indicators representing the penalty to apply to each latency coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all latency variables.

thresh

small numeric value. The iterative process stops when the differences between successive expected penalized complete-data log-likelihoods for both incidence and latency components are less than this specified level of tolerance (default is 10^-3).

scale

logical, if TRUE the predictors are centered and scaled.

maxit

integer specifying the maximum number of passes over the data for each lambda. If not specified, 100 is applied when penalty = "lasso" and 1000 is applied when penalty = "MCP" or penalty = "SCAD".

inits

an optional list specifiying the initial value for the incidence intercept (itct), a numeric vector for the unpenalized incidence coefficients (b_u), and a numeric vector for unpenalized latency coefficients (beta_u). For parametric models, it should also include a numeric value for the rate parameter (lambda) when model = "weibull" or model = "exponential", and a numeric value for the shape parameter (alpha) when model = "weibull". When model = "cox", it should also include a numeric vector for the latency survival probabilities S_u(t_i|w_i) for i=1,...,N (survprob). Penalized coefficients are initialized to zero. If inits is not specified or improperly specified, initialization is automatically provided by the function.

lambda.inc

numeric value for the penalization parameter \lambda for variables in the incidence portion of the model.

lambda.lat

numeric value for the penalization parameter \lambda for variables in the latency portion of the model.

gamma.inc

numeric value for the penalization parameter \gamma for variables in the incidence portion of the model when penalty = "MCP" or penalty = "SCAD" (default is 3).

gamma.lat

numeric value for the penalization parameter \gamma for variables in the latency portion of the model when penalty = "MCP" or penalty = "SCAD" (default is 3).

...

additional arguments.

Value

b_path

Matrix representing the solution path of the coefficients in the incidence portion of the model. Row is step and column is variable.

beta_path

Matrix representing the solution path of lthe coefficients in the latency portion of the model. Row is step and column is variable.

b0_path

Vector representing the solution path of the intercept in the incidence portion of the model.

logLik.inc

Vector representing the expected penalized complete-data log-likelihood for the incidence portion of the model for each step in the solution path.

logLik.lat

Vector representing the expected penalized complete-data log-likelihood for the latency portion of the model for each step in the solution path.

x.incidence

Matrix representing the design matrix of the incidence predictors.

x.latency

Matrix representing the design matrix of the latency predictors.

y

Vector representing the survival object response as returned by the Surv function

model

Character string indicating the type of regression model used for the latency portion of mixture cure model ("weibull" or "exponential").

scale

Logical value indicating whether the predictors were centered and scaled.

method

Character string indicating the EM alogoritm was used in fitting the mixture cure model.

rate_path

Vector representing the solution path of the rate parameter for the Weibull or exponential density in the latency portion of the model.

alpha_path

Vector representing the solution path of the shape parameter for the Weibull density in the latency portion of the model.

call

the matched call.

References

Archer, K. J., Fu, H., Mrozek, K., Nicolet, D., Mims, A. S., Uy, G. L., Stock, W., Byrd, J. C., Hiddemann, W., Braess, J., Spiekermann, K., Metzeler, K. H., Herold, T., Eisfeld, A.-K. (2024) Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model. Journal of Hematology & Oncology, 17:28.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 80, J = 100, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- cureem(Surv(Time, Censor) ~ ., data = training, x.latency = training,
                 model = "cox", penalty = "lasso",
                 lambda.inc = 0.1, lambda.lat = 0.1, gamma.inc = 6, gamma.lat = 10)

Fit penalized parametric mixture cure model using the GMIFS algorithm

Description

Fits a penalized Weibull or exponential mixture cure model using the generalized monotone incremental forward stagewise (GMIFS) algorithm and yields solution paths for parameters in the incidence and latency portions of the model.

Usage

curegmifs(
  formula,
  data,
  subset,
  x.latency = NULL,
  model = "weibull",
  penalty.factor.inc = NULL,
  penalty.factor.lat = NULL,
  epsilon = 0.001,
  thresh = 1e-05,
  scale = TRUE,
  maxit = 10000,
  inits = NULL,
  verbose = TRUE,
  ...
)

Arguments

formula

data

a data.frame in which to interpret the variables named in the formula or in the subset argument.

subset

x.latency

model

type of regression model to use for the latency portion of mixture cure model. Can be "weibull" or "exponential"; default is "weibull".

penalty.factor.inc

vector of binary indicators representing the penalty to apply to each incidence coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all incidence variables.

penalty.factor.lat

vector of binary indicators representing the penalty to apply to each latency coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all latency variables.

epsilon

small numeric value reflecting the incremental value used to update a coefficient at a given step (default is 0.001).

thresh

scale

logical, if TRUE the predictors are centered and scaled.

maxit

integer specifying the maximum number of steps to run in the iterative algorithm (default is 10^4).

inits

verbose

logical, if TRUE running information is printed to the console (default is FALSE).

...

additional arguments.

Value

b_path

Matrix representing the solution path of the coefficients in the incidence portion of the model. Row is step and column is variable.

beta_path

Matrix representing the solution path of lthe coefficients in the latency portion of the model. Row is step and column is variable.

b0_path

Vector representing the solution path of the intercept in the incidence portion of the model.

rate_path

Vector representing the solution path of the rate parameter for the Weibull or exponential density in the latency portion of the model.

logLik

Vector representing the log-likelihood for each step in the solution path.

x.incidence

Matrix representing the design matrix of the incidence predictors.

x.latency

Matrix representing the design matrix of the latency predictors.

y

Vector representing the survival object response as returned by the Surv function

model

Character string indicating the type of regression model used for the latency portion of mixture cure model ("weibull" or "exponential").

scale

Logical value indicating whether the predictors were centered and scaled.

alpha_path

Vector representing the solution path of the shape parameter for the Weibull density in the latency portion of the model.

call

the matched call.

References

Fu, H., Nicolet, D., Mrozek, K., Stone, R. M., Eisfeld, A. K., Byrd, J. C., Archer, K. J. (2022) Controlled variable selection in Weibull mixture cure models for high-dimensional data. Statistics in Medicine, 41(22), 4340–4366.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training

fit <- curegmifs(Surv(Time, Censor) ~ .,
         data = training, x.latency = training,
         model = "weibull", thresh = 1e-4, maxit = 2000, epsilon = 0.01,
         verbose = FALSE)

Fit penalized mixture cure model using the E-M algorithm with cross-validation for parameter tuning

Description

Fits a penalized parametric and semi-parametric mixture cure model (MCM) using the E-M algorithm with with k-fold cross-validation for parameter tuning. The lasso (L1), MCP and SCAD penalty are supported for the Cox MCM while only lasso is currently supported for parametric MCMs. When FDR controlled variable selection is used, the model-X knockoffs method is applied and indices of selected variables are returned.

Usage

cv_cureem(
  formula,
  data,
  subset,
  x.latency = NULL,
  model = "cox",
  penalty = "lasso",
  penalty.factor.inc = NULL,
  penalty.factor.lat = NULL,
  fdr.control = FALSE,
  fdr = 0.2,
  grid.tuning = FALSE,
  thresh = 0.001,
  scale = TRUE,
  maxit = NULL,
  inits = NULL,
  lambda.inc.list = NULL,
  lambda.lat.list = NULL,
  nlambda.inc = NULL,
  nlambda.lat = NULL,
  gamma.inc = 3,
  gamma.lat = 3,
  lambda.min.ratio.inc = 0.1,
  lambda.min.ratio.lat = 0.1,
  n_folds = 5,
  measure.inc = "c",
  one.se = FALSE,
  cure_cutoff = 5,
  parallel = FALSE,
  seed = NULL,
  verbose = TRUE,
  ...
)

Arguments

formula

data

a data.frame in which to interpret the variables named in the formula or in the subset argument.

subset

x.latency

model

type of regression model to use for the latency portion of mixture cure model. Can be "cox", "weibull", or "exponential" (default is "cox").

penalty

type of penalty function. Can be "lasso", "MCP", or "SCAD" (default is "lasso").

penalty.factor.inc

vector of binary indicators representing the penalty to apply to each incidence coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all incidence variables.

penalty.factor.lat

vector of binary indicators representing the penalty to apply to each latency coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all latency variables.

fdr.control

logical, if TRUE, model-X knockoffs are used for FDR-controlled variable selection and indices of selected variables are returned (default is FALSE).

fdr

numeric value in (0, 1) range specifying the target FDR level to use for variable selection when fdr.control=TRUE (default is 0.2).

grid.tuning

logical, if TRUE a 2-D grid tuning approach is used to select the optimal pair of \lambda_b and \lambda_{\beta} penalty parameters for the incidence and latency portions of the model, respectively. Otherwise the \lambda_b and \lambda_{\beta} are selected from a 1-D sequence and are equal to one another (default is FALSE).

thresh

scale

logical, if TRUE the predictors are centered and scaled.

maxit

maximum number of passes over the data for each lambda. If not specified, 100 is applied when penalty = "lasso" and 1000 is applied when penalty = "MCP" or penalty = "SCAD".

inits

lambda.inc.list

a numeric vector used to search for the optimal \lambda_b tuning parameter. If not supplied, the function computes a \lambda_b sequence based on nlambda.inc and lambda.min.ratio.inc. If grid.tuning=FALSE, the same sequence should be used for both \lambda_b and \lambda_{\beta}.

lambda.lat.list

a numeric vector used to search for the optimal \lambda_{\beta} tuning parameter. If not supplied, the function computes a \lambda_{\beta} sequence based on nlambda.lat and lambda.min.ratio.lat. If grid.tuning=FALSE, the same sequence should be used for both \lambda_b and \lambda_{\beta}.

nlambda.inc

an integer specifying the number of values to search for the optimal \lambda_b tuning parameter; default is 10 if grid.tuning=TRUE and 50 otherwise.

nlambda.lat

an integer specifying the number of values to search for the optimal \lambda_{\beta} tuning parameter; default is 10 if grid.tuning=TRUE and 50 otherwise.

gamma.inc

numeric value for the penalization parameter \gamma for variables in the incidence portion of the model when penalty = "MCP" or penalty = "SCAD" (default is 3).

gamma.lat

numeric value for the penalization parameter \gamma for variables in the latency portion of the model when penalty = "MCP" or penalty = "SCAD" (default is 3).

lambda.min.ratio.inc

numeric value in (0,1) representing the smallest value for \lambda_b as a fraction of lambda.max.inc, the data-derived entry value at which essentially all penalized variables in the incidence portion of the model have a coefficient estimate of 0 (default is 0.1).

lambda.min.ratio.lat

numeric value in (0.1) representing the smallest value for \lambda_{\beta} as a fraction of lambda.max.lat, the data-derived entry value at essentially all penalized variables in the latency portion of the model have a coefficient estimate of 0 (default is 0.1).

n_folds

an integer specifying the number of folds for the k-fold cross-valiation procedure (default is 5).

measure.inc

character string specifying the evaluation criterion used in selecting the optimal \lambda_b. Can be "c" or "auc"; default is "c". If measure.inc="c", the C-statistic using the cure status weighting (CSW) method proposed by Asano and Hirakawa (2017) is used to select both \lambda_b and \lambda_{\beta}. If measure.inc="auc", the AUC for cure prediction using the mean score imputation (MSI) method proposed by Asano et al. (2014) is used to select \lambda_b while the C-statistic with CSW is used for \lambda_{\beta}.

one.se

logical, if TRUE then the one standard error rule is applied for selecting the optimal parameters. The one standard error rule selects the most parsimonious model having evaluation criterion no more than one standard error worse than that of the best evaluation criterion (default is FALSE).

cure_cutoff

numeric value representing the cutoff time value that represents subjects not experiencing the event by this time are cured. This value is used to produce a proxy for the unobserved cure status when calculating C-statistic and AUC (default is 5 representing 5 years). Users should be careful to note the time scale of their data and adjust this according to the time scale and clinical application.

parallel

logical. If TRUE, parallel processing is performed for K-fold CV using foreach and the doMC package is required.

seed

optional integer representing the random seed. Setting the random seed fosters reproducibility of the results.

verbose

logical, if TRUE running information is printed to the console (default is FALSE).

...

additional arguments.

Value

b0

Estimated intercept for the incidence portion of the model.

b

Estimated coefficients for the incidence portion of the model.

beta

Estimated coefficients for the latency portion of the model.

alpha

Estimated shape parameter if the Weibull model is fit.

rate

Estimated rate parameter if the Weibull or exponential model is fit.

logLik.inc

Expected penalized complete-data log-likelihood for the incidence portion of the model.

logLik.lat

Expected penalized complete-data log-likelihood for the latency portion of the model.

selected.lambda.inc

Value of \lambda_b selected using cross-validation. NULL when fdr.control is TRUE.

selected.lambda.lat

Value of \lambda_{\beta} selected using cross-validation. NULL when fdr.control is TRUE.

max.c

Maximum C-statistic achieved.

max.auc

Maximum AUC for cure prediction achieved; only output when measure.inc="auc".

selected.index.inc

Indices of selected variables for the incidence portion of the model when fdr.control=TRUE. If no variables are selected, int(0) will be returned.

selected.index.lat

Indices of selected variables for the latency portion of the model when fdr.control=TRUE. If no variables are selected, int(0) will be returned.

call

the matched call.

References

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 200, J = 25, nTrue = 5, A = 1.8)
training <- temp$Training
fit.cv <- cv_cureem(Surv(Time, Censor) ~ ., data = training,
                 x.latency = training, fdr.control = FALSE,
                 grid.tuning = FALSE, nlambda.inc = 10, nlambda.lat = 10,
                 n_folds = 2, seed = 23, verbose = TRUE)
fit.cv.fdr <- cv_cureem(Surv(Time, Censor) ~ ., data = training,
                 x.latency = training, model = "weibull", penalty = "lasso",
                 fdr.control = TRUE, grid.tuning = FALSE, nlambda.inc = 10,
                 nlambda.lat = 10, n_folds = 2, seed = 23, verbose = TRUE)

Fit a penalized parametric mixture cure model using the GMIFS algorithm with cross-validation for model selection

Description

Fits a penalized Weibull or exponential mixture cure model using the generalized monotone incremental forward stagewise (GMIFS) algorithm with k-fold cross-validation to select the optimal iteration step along the solution path. When FDR controlled variable selection is used, the model-X knockoffs method is applied and indices of selected variables are returned.

Usage

cv_curegmifs(
  formula,
  data,
  subset,
  x.latency = NULL,
  model = "weibull",
  penalty.factor.inc = NULL,
  penalty.factor.lat = NULL,
  fdr.control = FALSE,
  fdr = 0.2,
  epsilon = 0.001,
  thresh = 1e-05,
  scale = TRUE,
  maxit = 10000,
  inits = NULL,
  n_folds = 5,
  measure.inc = "c",
  one.se = FALSE,
  cure_cutoff = 5,
  parallel = FALSE,
  seed = NULL,
  verbose = TRUE,
  ...
)

Arguments

formula

data

a data.frame in which to interpret the variables named in the formula or in the subset argument.

subset

x.latency

model

type of regression model to use for the latency portion of mixture cure model. Can be "weibull" or "exponential"; default is "weibull".

penalty.factor.inc

vector of binary indicators representing the penalty to apply to each incidence coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all incidence variables.

penalty.factor.lat

vector of binary indicators representing the penalty to apply to each latency coefficient: 0 implies no shrinkage and 1 implies shrinkage. If not supplied, 1 is applied to all latency variables.

fdr.control

logical, if TRUE, model-X knockoffs are used for FDR-controlled variable selection and indices of selected variables are returned (default is FALSE).

fdr

numeric value in (0, 1) range specifying the target FDR level to use for variable selection when fdr.control=TRUE (default is 0.2).

epsilon

small numeric value reflecting incremental value used to update a coefficient at a given step (default is 0.001).

thresh

scale

logical, if TRUE the predictors are centered and scaled.

maxit

integer specifying the maximum number of steps to run in the iterative algorithm (default is 10^4).

inits

n_folds

an integer specifying the number of folds for the k-fold cross-valiation procedure (default is 5).

measure.inc

one.se

cure_cutoff

parallel

logical. If TRUE, parallel processing is performed for K-fold CV using foreach and the doMC package is required.

seed

optional integer representing the random seed. Setting the random seed fosters reproducibility of the results.

verbose

logical, if TRUE running information is printed to the console (default is FALSE).

...

additional arguments.

Value

b0

Estimated intercept for the incidence portion of the model.

b

Estimated coefficients for the incidence portion of the model.

beta

Estimated coefficients for the latency portion of the model.

alpha

Estimated shape parameter if the Weibull model is fit.

rate

Estimated rate parameter if the Weibull or exponential model is fit.

logLik

Log-likelihood value.

selected.step.inc

Iteration step selected for the incidence portion of the model using cross-validation. NULL when fdr.control is TRUE.

selected.step.lat

Iteration step selected for the latency portion of the model using cross-validation. NULL when fdr.control is TRUE.

max.c

Maximum C-statistic achieved

max.auc

Maximum AUC for cure prediction achieved; only output when measure.inc="auc".

selected.index.inc

Indices of selected variables for the incidence portion of the model when fdr.control=TRUE. If none selected, int(0) will be returned.

selected.index.lat

Indices of selected variables for the latency portion of the model when fdr.control=TRUE. If none selected, int(0) will be returned.

call

the matched call.

References

Examples

library(survival)
set.seed(123)
temp <- generate_cure_data(N = 100, J = 15, nTrue = 3, A = 1.8, rho = 0.2)
training <- temp$Training

fit.cv <- cv_curegmifs(Surv(Time, Censor) ~ ., data = training,
                      x.latency = training, fdr.control = FALSE,
                      maxit = 450, epsilon = 0.01,
                      n_folds = 2, seed = 23, verbose = TRUE)

Simulate data under a mixture cure model

Description

Simulate data under a mixture cure model

Usage

generate_cure_data(
  N = 400,
  J = 500,
  nonp = 2,
  train.prop = 3/4,
  nTrue = 10,
  A = 1,
  rho = 0.5,
  itct_mean = 0.5,
  cens_ub = 20,
  alpha = 1,
  lambda = 2,
  same_signs = FALSE,
  model = "weibull"
)

Arguments

N

an integer denoting the total sample size.

J

an integer denoting the number of penalized predictors which is the same for both the incidence and latency portions of the model.

nonp

an integer less than J denoting the number of unpenalized predictors (which is the same for both the incidence and latency portions of the model.

train.prop

a numeric value in 0, 1 representing the fraction of N to be used in forming the Training dataset.

nTrue

an integer denoting the number of variables truly associated with the outcome (i.e., the number of covariates with nonzero parameter values) among the penalized predictors.

A

a numeric value denoting the effect size which is the same for both the incidence and latency portions of the model.

rho

a numeric value in 0, 1 representing the correlation between adjacent covariates in the same block. See details below.

itct_mean

a numeric value representing the expectation of the incidence intercept which controls the cure rate.

cens_ub

a numeric value representing the upper bound on the censoring time distribition which follows a uniform distribution on 0, cens_ub.

alpha

a numeric value representing the shape parameter in the Weibull density.

lambda

a numeric value representing the rate parameter in the Weibull density.

same_signs

logical, if TRUE the incidence and latency coefficients have the same signs.

model

type of regression model to use for the latency portion of mixture cure model. Can be "weibull", "GG", "Gompertz", "nonparametric", or "GG_baseline".

Value

Training

Training data.frame which includes Time, Censor, and covariates.

Testing

Testing data.frame which includes Time, Censor, and covariates.

parameters

A list including: the indices of true incidence signals (nonzero_b), indices of true latency signals (nonzero_beta), unpenalized incidence parameter values (b_u), unpenalized latency parameter values (beta_u), parameter values for the true incidence signals among penalized covariates (b_p_nz), parameter values for the true latency signals among penalized covariates (beta_p_nz), parameter value for the incidence intercept (itct)

Examples

library(survival)
set.seed(1234)
data <- generate_cure_data(N = 200, J = 50, nTrue = 10, A = 1.8, rho = 0.2)
training <- data$Training
testing <- data$Testing
fit <- cureem(Surv(Time, Censor) ~ ., data = training,
              x.latency = training, model = "cox", penalty = "lasso",
              lambda.inc = 0.05, lambda.lat = 0.05,
              gamma.inc = 6, gamma.lat = 10)

Non-parametric pest for a non-zero cured fraction

Description

Tests the null hypothesis that the proportion of observations susceptible to the event = 1 against the alternative that the proportion of observations susceptible to the event is < 1. If the null hypothesis is rejected, there is a significant cured fraction.

Usage

nonzerocure_test(object, Reps = 1000, seed = NULL, plot = FALSE, B = NULL)

Arguments

object

a survfit object.

Reps

number of simulations on which to base the p-value (default = 1000).

seed

optional random seed.

plot

logical. If TRUE a histogram of the estimated susceptible proportions over all simulations is produced.

B

optional. If specified the maximum observed time for the uniform distribution for generating the censoring times. If not specified, an exponential model is used for generating the censoring times (default).

Value

proportion_susceptible

estimated proportion of susceptibles

proportion_cured

estimated proportion of those cured

p.value

p-value testing the null hypothesis that the proportion of susceptibles = 1 (cured fraction = 0) against the alternative that the proportion of susceptibles < 1 (non-zero cured fraction)

time_95_percent_of_events

estimated time at which 95% of events should have occurred

References

Maller, R. A. and Zhou, X. (1996) Survival Analysis with Long-Term Survivors. John Wiley & Sons.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
km.fit <- survfit(Surv(Time, Censor) ~ 1, data = training)
nonzerocure_test(km.fit)

Plot fitted mixture cure model

Description

This function plots either the coefficient path, the AIC, the cAIC, the BIC, or the log-likelihood for a fitted curegmifs or cureem object. This function produces a lollipop plot of the coefficient estimates for a fitted cv_curegmifs or cv_cureem object.

Usage

## S3 method for class 'mixturecure'
plot(x, type = "trace", xlab = NULL, ylab = NULL, main = NULL, ...)

Arguments

x

a mixturecure object resulting from curegmifs or cureem, cv_curegmifs or cv_cureem.

type

default is "trace" which plots the coefficient path for the fitted object. Also available are "AIC", "cAIC", "mAIC", "BIC", "mBIC", "EBIC", and "logLik". This option has no effect for objects fit using cv_curegmifs or cv_cureem.

xlab

a default x-axis label will be used which can be changed by specifying a user-defined x-axis label.

ylab

a default y-axis label will be used which can be changed by specifying a user-defined y-axis label.

main

a default main title will be used which can be changed by specifying a user-defined main title. This option is not used for cv_curegmifs or cv_cureem fitted objects.

...

other arguments.

Value

this function has no returned value but is called for its side effects

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- curegmifs(Surv(Time, Censor) ~ .,
                   data = training, x.latency = training,
                   model = "weibull", thresh = 1e-4, maxit = 2000,
                   epsilon = 0.01, verbose = FALSE)
plot(fit)

Predicted probabilities for susceptibles, linear predictor for latency, and risk class for latency for mixture cure fit

Description

This function returns a list the includes the predicted probabilities for susceptibles as well as the linear predictor for the latency distribution and a dichotomous risk for latency for a curegmifs, cureem, cv_curegmifs or cv_cureem fitted object.

Usage

## S3 method for class 'mixturecure'
predict(object, newdata, model.select = "AIC", ...)

Arguments

object

a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, cv_cureem.

newdata

an optional data.frame that minimally includes the incidence and/or latency variables to use for predicting the response. If omitted, the training data are used.

model.select

...

other arguments

Value

p.uncured

a vector of probabilities from the incidence portion of the fitted model representing the P(uncured).

linear.latency

a vector for the linear predictor from the latency portion of the model.

latency.risk

a dichotomous class representing low (below the median) versus high risk for the latency portion of the model.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- curegmifs(Surv(Time, Censor) ~ .,
                  data = training, x.latency = training,
                  model = "weibull", thresh = 1e-4, maxit = 2000,
                  epsilon = 0.01, verbose = FALSE)
predict.train <- predict(fit)
names(predict.train)
testing <- temp$Testing
predict.test <- predict(fit, newdata = testing)

Print the contents of a mixture cure fitted object

Description

This function prints the names of the list objects from a curegmifs, cureem, cv_cureem, or cv_curegmifs fitted model.

Usage

## S3 method for class 'mixturecure'
print(x, ...)

Arguments

x

a mixturecure object resulting from curegmifs, cureem, cv_cureem, or cv_curegmifs.

...

other arguments.

Value

names of the objects in a mixturecure object fit using cureem, curegmifs, cv_cureem, or cv_curegmifs.

Note

The contents of an mixturecure fitted object differ depending upon whether the EM (cureem) or GMIFS (curegmifs) algorithm is used for model fitting. Also, the output differs depending upon whether x.latency is specified in the model (i.e., variables are included in the latency portion of the model fit) or only terms on the right hand side of the equation are included (i.e., variables are included in the incidence portion of the model).

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- curegmifs(Surv(Time, Censor) ~ .,
                       data = training, x.latency = training,
                       model = "weibull", thresh = 1e-4, maxit = 2000,
                       epsilon = 0.01, verbose = FALSE)
print(fit)

Test for sufficient follow-up

Description

Tests for sufficient follow-up using a Kaplan-Meier fitted object.

Usage

sufficient_fu_test(object)

Arguments

object

a survfit object.

Value

p.value

p-value from testing the null hypothesis that there was not sufficient follow-up against the alternative that there was sufficient follow-up

Nn

total number of events that occurred at time > pmax(0, 2*(last observed event time)-(last observed time)) and < the last observed event time

N

number of observations in the dataset

References

Maller, R. A. and Zhou, X. (1996) Survival Analysis with Long-Term Survivors. John Wiley & Sons.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
km.fit <- survfit(Surv(Time, Censor) ~ 1, data = training)
sufficient_fu_test(km.fit)

Summarize a Fitted Mixture Cure Object.

Description

summary method for a mixturecure object fit using curegmifs, cureem, cv_curegmifs, or cv_cureem.

Usage

## S3 method for class 'mixturecure'
summary(object, ...)

Arguments

object

a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, or cv_cureem.

...

other arguments.

Value

prints the following items extracted from the object fit using curegmifs or cureem: the step and value that maximizes the log-likelihood; the step and value that minimizes the AIC, modified AIC (mAIC), corrected AIC (cAIC), BIC, modified BIC (mBIC), and extended BIC (EBIC). Returns log-likelihood, AIC, and BIC if the object was fit using cv_curegmifs or cv_cureem at the optimal cross-validated values if no FDR control; the number of non-zero incidence and latency variables is returned when cross-validation is used together with FDR control.

Examples

library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
fit <- curegmifs(Surv(Time, Censor) ~ .,
                       data = training, x.latency = training,
                       model = "weibull", thresh = 1e-4, maxit = 2000,
                       epsilon = 0.01, verbose = FALSE)
summary(fit)

AUC for cure prediction using mean score imputation

Description

Usage

Arguments

Value

References

See Also

Examples

AML test data

Description

Usage

Format

Source

AML training data

Description

Usage

Format

Source

Extract model coefficients from a fitted mixture cure object

Description

Usage

Arguments

Value

See Also

Examples

C-statistic for mixture cure models

Description

Usage

Arguments

Value

References

See Also

Examples

Estimate cured fraction

Description

Usage

Arguments

Value

See Also

Examples

Fit penalized mixture cure model using the E-M algorithm

Description

Usage

Arguments

Value

References

See Also

Examples

Fit penalized parametric mixture cure model using the GMIFS algorithm

Description

Usage

Arguments

Value

References

See Also

Examples

Fit penalized mixture cure model using the E-M algorithm with cross-validation for parameter tuning

Description

Usage

Arguments

Value

References

See Also

Examples

Fit a penalized parametric mixture cure model using the GMIFS algorithm with cross-validation for model selection

Description

Usage

Arguments

Value

References

See Also

Examples

Simulate data under a mixture cure model

Description

Usage

Arguments

Value

Examples

Non-parametric pest for a non-zero cured fraction

Description