A data set from Carpenter (2002).

Description

A data set from Carpenter (2002).

Format

A data set with 408 observations and 32 variables.

Source

Carpenter, Daniel P. 2002. ”Groups, the Media, Agency Waiting Costs, and FDA Drug Approval.” American Journal of Political Science 46(3): 490-505.

Luke Keele, "Replication data for: Proportionally Difficult: Testing for Nonproportional Hazards In Cox Models". doi:10.7910/DVN/VJAHRG. V1 [Version].

A data set from Golub & Steunenberg (2007)

Description

A data set from Golub & Steunenberg (2007)

Format

A data set with 3001 observations and 17 variables.

Source

Golub, Jonathan, and Bernard Steunenberg. 2007. ”How Time Affects EU Decision-Making.” European Union Politics 8(4): 555-66.

Amanda A. Licht, 2011, "Replication data for: Change Comes with Time". doi:10.7910/DVN/VJAHRG. IQSS Dataverse Network [Distributor] V3 [Version].

Transform the simulation object to include only the min and max of the constricted intervals, as well as the lower and upper bounds of the middle 50 percent of the constricted intervals

Description

MinMaxLines is an internal function to transform the simulation object to include only the min and max of the intervals set by ci in the coxsim command, as well as the lower and upper bounds of the middle 50 percent of these intervals. It also returns the medians.

Usage

MinMaxLines(df, byVars = "Xj", hr = FALSE, strata = FALSE, clean = FALSE)

Arguments

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal +
           deathrt1 + acutediz + hosp01  + hhosleng +
           mandiz01 + femdiz01 + peddiz01 + orphdum +
           vandavg3 + wpnoavg3 + condavg3 + orderent +
           stafcder, data = CarpenterFdaData)

 # Simulate Hazard Ratios
 Sim1 <- coxsimLinear(M1, b = "stafcder",
                      Xj = c(1237, 1600),
                      Xl = c(1000, 1000),
                      qi = "Hazard Ratio",
                      spin = TRUE, ci = 0.99)

# Find summary statistics of the constricted interval
Sum <- MinMaxLines(Sim1, clean = TRUE)

Convert a data frame of non-equal interval continuous observations into equal interval continuous observations

Description

SurvExpand convert a data frame of non-equal interval continuous observations into equal interval continuous observations. This is useful for creating time-interactions with tvc.

Usage

SurvExpand(
  data,
  GroupVar,
  Time,
  Time2,
  event,
  PartialData = TRUE,
  messages = TRUE
)

Arguments

Details

The function primarily prepares data from the creation of accurate time-interactions with the tvc command. Note: the function will work best if your original time intervals are recorded in whole numbers. It also currently does not support repeated events data.

Value

Returns a data frame where observations have been expanded into equally spaced time intervals.

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

See Also

tvc

Examples

# Load Golub & Steunenberg (2007) Data
data("GolubEUPData")

# Subset PURELY TO SPEED UP THE EXAMPLE
GolubEUPData <- GolubEUPData[1:500, ]

# Expand data
GolubEUPDataExpand <- SurvExpand(GolubEUPData, GroupVar = 'caseno',
                       Time = 'begin', Time2 = 'end', event = 'event')

Convert a coxsim class object into a data frame

Description

Convert a coxsim class object into a data frame

Usage

## S3 method for class 'coxsim'
as.data.frame(x, ...)

Arguments

Simulate quantities of interest for linear multiplicative interactions from Cox Proportional Hazards models

Description

coxsimInteract simulates quantities of interest for linear multiplicative interactions using multivariate normal distributions. These can be plotted with simGG.

Usage

coxsimInteract(
  obj,
  b1,
  b2,
  qi = "Marginal Effect",
  X1 = NULL,
  X2 = NULL,
  means = FALSE,
  expMarg = TRUE,
  nsim = 1000,
  ci = 0.95,
  spin = FALSE,
  extremesDrop = TRUE
)

Arguments

Details

Simulates marginal effects, first differences, hazard ratios, and hazard rates for linear multiplicative interactions. Marginal effects are calculated as in Brambor et al. (2006) with the addition that we take the exponent, so that it resembles a hazard ratio. You can choose not to take the exponent by setting the argument expMarg = FALSE. For an interaction between variables X and Z the marginal effect for X is:

ME_{X} = e^(\beta_{X} + \beta_{XZ}Z)

Note that for First Differences the comparison is not between two values of the same variable but two values of the constitute variable and 0 for the two variables.

Value

a siminteract class object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. ”Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14(1): 63-82.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ”Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347-61.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. http://arxiv.org/pdf/1302.2142v1.

See Also

simGG, strata, and coxph,

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ lethal*prevgenx,
            data = CarpenterFdaData)

# Simulate Marginal Effect of lethal for multiple
# values of prevgenx
Sim1 <- coxsimInteract(M1, b1 = "lethal", b2 = "prevgenx",
                       X2 = seq(2, 115, by = 5), spin = TRUE)

## Not run: 
# Change the order of the covariates to make a more easily
# interpretable relative hazard graph.
M2 <- coxph(Surv(acttime, censor) ~ prevgenx*lethal +
             orphdum, data = CarpenterFdaData)

# Simulate Hazard Ratio of lethal for multiple
# values of prevgenx
Sim2 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                    X1 = seq(2, 115, by = 2),
                    X2 = c(0, 1),
                    qi = "Hazard Ratio", ci = 0.9)

# Simulate First Difference
Sim3 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                       X1 = seq(2, 115, by = 2),
                       X2 = c(0, 1),
                       qi = "First Difference", spin = TRUE)

# Simulate Hazard Rate
Sim4 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                       X1 = 90, X2 = 1, qi = "Hazard Rate",
                       means = TRUE)

## End(Not run)

# Example with a categorical variable
# Download data
data(hmohiv)

# Create category lables
hmohiv$drug <- factor(hmohiv$drug, labels = c('not treated', 'treated'))

M3 <- coxph(Surv(time,censor) ~ drug*age, data = hmohiv)

# Note: Use relevant coefficient name as shown in model summary, e.g.
# 'drugtreated'.
Sim5 <- coxsimInteract(M3, b1 = "drugtreated", b2 = 'age', X2 = 20:54)

Simulate quantities of interest for covariates from Cox Proportional Hazards models that are not interacted with time or nonlinearly transformed

Description

Simulates relative hazards, first differences, hazard ratios, and hazard rates for linear, non-time interacted covariates from Cox Proportional Hazard models. These can be plotted with simGG.

Usage

coxsimLinear(
  obj,
  b,
  qi = "Relative Hazard",
  Xj = NULL,
  Xl = NULL,
  means = FALSE,
  nsim = 1000,
  ci = 0.95,
  spin = FALSE,
  extremesDrop = TRUE
)

Arguments

Details

coxsimLinear simulates relative hazards, first differences, and hazard ratios for linear covariates that are not interacted with time or nonlinearly transformed from models estimated with coxph using the multivariate normal distribution. These can be plotted with simGG.

Value

a simlinear, coxsim object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Licht, Amanda A. 2011. ”Change Comes with Time: Substantive Interpretation of Nonproportional Hazards in Event History Analysis.” Political Analysis 19: 227-43.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ”Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347-61.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. http://arxiv.org/pdf/1302.2142v1.

See Also

simGG.simlinear strata, and coxph

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal +
            deathrt1 + acutediz + hosp01  + hhosleng +
            mandiz01 + femdiz01 + peddiz01 + orphdum +
            vandavg3 + wpnoavg3 + condavg3 + orderent +
            stafcder, data = CarpenterFdaData)

# Simulate Hazard Ratios
Sim1 <- coxsimLinear(M1, b = "stafcder",
                     Xj = c(1237, 1600),
                     Xl = c(1000, 1000),
                     qi = "Hazard Ratio",
                     spin = TRUE, ci = 0.99)

## Not run: 
# Simulate Hazard Rates
Sim2 <- coxsimLinear(M1, b = "stafcder",
                      Xj = 1237,
                      ci = 0.99)

## End(Not run)

Simulate quantities of interest for a range of values for a polynomial nonlinear effect from Cox Proportional Hazards models

Description

coxsimPoly simulates quantities of interest for polynomial covariate effects estimated from Cox Proportional Hazards models. These can be plotted with simGG.

Usage

coxsimPoly(
  obj,
  b = NULL,
  qi = "Relative Hazard",
  pow = 2,
  Xj = NULL,
  Xl = NULL,
  nsim = 1000,
  ci = 0.95,
  spin = FALSE,
  extremesDrop = TRUE
)

Arguments

Details

Simulates quantities of interest for polynomial covariate effects. For example if a nonlinear effect is modeled with a second order polynomial–i.e. \beta_{1}x_{i} + \beta_{2}x_{i}^{2}–we can draw n simulations from the multivariate normal distribution for both \beta_{1} and \beta_{2}. Then we simply calculate quantities of interest for a range of values and plot the results as before. For example, we find the first difference for a second order polynomial with:

\%\triangle h_{i}(t) = (\mathrm{e}^{\beta_{1}x_{j-1} + \beta_{2}x_{j-l}^{2}} - 1) * 100

where x_{j-l} = x_{j} - x_{l}.

Note, you must use I to create the polynomials.

Value

a simpoly, coxsim object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Keele, Luke. 2010. ”Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models.” Political Analysis 18(2): 189-205.

Carpenter, Daniel P. 2002. ”Groups, the Media, Agency Waiting Costs, and FDA Drug Approval.” American Journal of Political Science 46(3): 490-505.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ”Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347-61.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. http://arxiv.org/pdf/1302.2142v1.

See Also

simGG.simpoly, survival, strata, and coxph

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal + deathrt1 +
            acutediz + hosp01  + hhosleng + mandiz01 + femdiz01 +
            peddiz01 + orphdum + natreg + I(natreg^2) +
            I(natreg^3) + vandavg3 + wpnoavg3 +
            condavg3 + orderent + stafcder, data = CarpenterFdaData)

# Simulate simpoly First Difference
Sim1 <- coxsimPoly(M1, b = "natreg", qi = "First Difference",
                pow = 3, Xj = seq(1, 150, by = 5), nsim = 100)

## Not run: 
# Simulate simpoly Hazard Ratio with spin probibility interval
Sim2 <- coxsimPoly(M1, b = "natreg", qi = "Hazard Ratio",
              pow = 3, Xj = seq(1, 150, by = 5), spin = TRUE)

## End(Not run)

Simulate quantities of interest for penalized splines from Cox Proportional Hazards models

Description

coxsimSpline simulates quantities of interest from penalized splines using multivariate normal distributions.

Usage

coxsimSpline(
  obj,
  bspline,
  bdata,
  qi = "Relative Hazard",
  Xj = 1,
  Xl = 0,
  nsim = 1000,
  ci = 0.95,
  spin = FALSE,
  extremesDrop = TRUE
)

Arguments

Details

Simulates relative hazards, first differences, hazard ratios, and hazard rates for penalized splines from Cox Proportional Hazards models. These can be plotted with simGG. A Cox PH model with one penalized spline is given by:

h(t|\mathbf{X}_{i})=h_{0}(t)\mathrm{e}^{g(x)}

where g(x) is the penalized spline function. For our post-estimation purposes g(x) is basically a series of linearly combined coefficients such that:

g(x) = \beta_{k_{1}}(x)_{1+} + \beta_{k_{2}}(x)_{2+} + \beta_{k_{3}}(x)_{3+} + \ldots + \beta_{k_{n}}(x)_{n+}

where k are the equally spaced spline knots with values inside of the range of observed x and n is the number of knots.

We can again draw values of each \beta_{k_{1}},\: \ldots \beta_{k_{n}} from the multivariate normal distribution described above. We then use these simulated coefficients to estimates quantities of interest for a range covariate values. For example, the first difference between two values x_{j} and x_{l} is:

FD = (e^{g(x_{j}) - g(x_{l})} - 1) * 100

Relative hazards and hazard ratios can be calculated by extension.

Currently coxsimSpline does not support simulating hazard rates form multiple stratified models.

Value

a simspline object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Luke Keele, "Replication data for: Proportionally Difficult: Testing for Nonproportional Hazards In Cox Models", 2010, doi:10.7910/DVN/VJAHRG V1 [Version].

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ”Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347-61.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. http://arxiv.org/pdf/1302.2142v1.

See Also

simGG, survival, strata, and coxph

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
# From Keele (2010) replication data
M1 <- coxph(Surv(acttime, censor) ~  prevgenx + lethal + deathrt1 +
           acutediz + hosp01  + pspline(hospdisc, df = 4) +
           pspline(hhosleng, df = 4) + mandiz01 + femdiz01 + peddiz01 +
           orphdum + natreg + vandavg3 + wpnoavg3 +
           pspline(condavg3, df = 4) + pspline(orderent, df = 4) +
           pspline(stafcder, df = 4), data = CarpenterFdaData)

## Not run: 
# Simulate Relative Hazards for orderent
Sim1 <- coxsimSpline(M1, bspline = "pspline(stafcder, df = 4)",
                    bdata = CarpenterFdaData$stafcder,
                    qi = "Hazard Ratio",
                    Xj = seq(1100, 1700, by = 10),
                    Xl = seq(1099, 1699, by = 10), spin = TRUE)


## End(Not run)
# Simulate Hazard Rates for orderent
Sim2 <- coxsimSpline(M1, bspline = "pspline(orderent, df = 4)",
                       bdata = CarpenterFdaData$orderent,
                       qi = "Hazard Rate",
                       Xj = seq(2, 53, by = 3), nsim = 100)

Simulate time-interactive quantities of interest from Cox Proportional Hazards models

Description

coxsimtvc simulates time-interactive relative hazards, first differences, and hazard ratios from models estimated with coxph using the multivariate normal distribution. These can be plotted with simGG.

Usage

coxsimtvc(
  obj,
  b,
  btvc,
  qi = "Relative Hazard",
  Xj = NULL,
  Xl = NULL,
  tfun = "linear",
  pow = NULL,
  nsim = 1000,
  from,
  to,
  by = 1,
  ci = 0.95,
  spin = FALSE,
  extremesDrop = TRUE
)

Arguments

Details

Simulates time-varying relative hazards, first differences, and hazard ratios using parameter estimates from coxph models. Can also simulate hazard rates for multiple strata.

Relative hazards are found using:

RH = e^{\beta_{1} + \beta_{2}f(t)}

where f(t) is the function of time.

First differences are found using:

FD = (e^{(X_{j} - X_{l}) (\beta_{1} + \beta_{2}f(t))} - 1) * 100

where X_{j} and X_{l} are some values of X to contrast.

Hazard ratios are calculated using:

FD = e^{(X_{j} - X_{l}) (\beta_{1} + \beta_{2}f(t))}

When simulating non-stratifed time-varying harzards coxsimtvc uses the point estimates for a given coefficient \hat{\beta}_{x} and its time interaction \hat{\beta}_{xt} along with the variance matrix (\hat{V}(\hat{\beta})) estimated from a coxph model. These are used to draw values of \beta_{1} and \beta_{2} from the multivariate normal distribution N(\hat{\beta},\: \hat{V}(\hat{\beta})).

When simulating stratified time-varying hazard rates H for a given strata k, coxsimtvc uses:

H_{kxt} = \hat{\beta}_{k0t}e^{\hat{\beta_{1}} + \beta_{2}f(t)}

The resulting simulation values can be plotted using simGG.

Value

a simtvc object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Golub, Jonathan, and Bernard Steunenberg. 2007. ”How Time Affects EU Decision-Making.” European Union Politics 8(4): 555-66.

Licht, Amanda A. 2011. ”Change Comes with Time: Substantive Interpretation of Nonproportional Hazards in Event History Analysis.” Political Analysis 19: 227-43.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ”Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347-61.

Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. http://arxiv.org/pdf/1302.2142v1.

See Also

simGG, survival, strata, and coxph

Examples

## Not run: 
# Load Golub & Steunenberg (2007) Data
data("GolubEUPData")

# Load survival package
library(survival)

# Expand data (not run to speed processing time, but should be run)
GolubEUPData <- SurvExpand(GolubEUPData, GroupVar = 'caseno',
                     Time = 'begin', Time2 = 'end', event = 'event')

# Create time interactions
BaseVars <- c('qmv', 'backlog', 'coop', 'codec', 'qmvpostsea', 'thatcher')
GolubEUPData <- tvc(GolubEUPData, b = BaseVars, tvar = 'end', tfun = 'log')

# Run Cox PH Model
M1 <- coxph(Surv(begin, end, event) ~ qmv + qmvpostsea + qmvpostteu +
                coop + codec + eu9 + eu10 + eu12 + eu15 + thatcher +
                agenda + backlog + qmv_log + qmvpostsea_log + coop_log +
                codec_log + thatcher_log + backlog_log,
            data = GolubEUPData, ties = "efron")

# Create simtvc object for Relative Hazard
Sim1 <- coxsimtvc(obj = M1, b = "qmv", btvc = "qmv_log",
                   tfun = "log", from = 80, to = 2000,
                   Xj = 1, by = 15, ci = 0.99, nsim = 100)

# Create simtvc object for First Difference
Sim2 <- coxsimtvc(obj = M1, b = "qmv", btvc = "qmv_log",
                 qi = "First Difference", Xj = 1,
                 tfun = "log", from = 80, to = 2000,
                 by = 15, ci = 0.95)

# Create simtvc object for Hazard Ratio
Sim3 <- coxsimtvc(obj = M1, b = "backlog", btvc = "backlog_log",
                  qi = "Hazard Ratio", Xj = c(191, 229),
                  Xl = c(0, 0),
                  tfun = "log", from = 80, to = 2000,
                  by = 15, ci = 0.5)

## End(Not run)

Graph fitted stratified survival curves from Cox Proportional Hazards models

Description

This function largely improves plot.survfit. It plots the curves using ggplot2 rather than base R graphics. One major advantage is the ability to split the survival curves into multiple plots and arrange them in a grid. This makes it easier to examine many strata at once. Otherwise they can be very bunched up.

Note: the strata legend labels need to be changed manually (see revalue) in the survfit object with the strata component.

Usage

ggfitStrata(
  obj,
  byStrata = FALSE,
  xlab = "",
  ylab = "",
  title = "",
  lcolour = "#2C7FB8",
  rcolour = "#2C7FB8"
)

Arguments

Details

ggfitStrata graphs fitted survival curves created with survfit using ggplot2.

See Also

survfit, ggplot2 and strata

Examples

# Load packages
library(survival)
library(simPH)

# Subset data
bladder1 <- bladder[bladder$enum < 5, ]

# Estimate coxph model (note that this model is for code illustration only)
M1 <- coxph(Surv(stop, event) ~ (rx + size + number) + strata(enum) +
                cluster(id), bladder1)

# Find predicted values
M1Fit <- survfit(M1)

# Plot strata in a grid
ggfitStrata(M1Fit, byStrata = TRUE)

# Plot all in one
ggfitStrata(M1Fit, byStrata = FALSE)

Simulated HIV patient data from UCLA IDRE

Description

Simulated HIV patient data from UCLA IDRE

Usage

hmohiv

Format

An object of class data.frame with 100 rows and 7 columns.

Source

UCLA IDRE https://stats.oarc.ucla.edu/r/examples/asa/r-applied-survival-analysis-ch-1/

Create a sequence of Xl values

Description

setXl creates a sequence of Xl values given a sequence of Xj values and a fixed difference.

Usage

setXl(Xj, diff = 1)

Arguments

Value

a vector

Examples

# Set Xj
setXj = seq(1100, 1700, by = 10)

# Find Xl that are 1 less than Xj
setXl(Xj = setXj, diff = 1)

A method for plotting simulation objects created by simPH

Description

simGG a method for ploting simulation objects created by simPH.

Usage

simGG(obj, ...)

Arguments

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

See Also

simGG.siminteract, simGG.simtvc, simGG.simlinear, simGG.simpoly, simGG.simspline

Examples

## Not run: 
 # Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ lethal*prevgenx,
            data = CarpenterFdaData)

# Simulate Marginal Effect of lethal for multiple
# values of prevgenx
Sim1 <- coxsimInteract(M1, b1 = "lethal", b2 = "prevgenx",
                       X2 = seq(2, 115, by = 5), spin = TRUE)

# Plot simulations
simGG(Sim1)

## End(Not run)

Plot simulated linear multiplicative interactions from Cox Proportional Hazards Models

Description

simGG.siminteract uses ggplot2 to plot the quantities of interest from siminteract objects, including marginal effects, first differences, hazard ratios, and hazard rates.

Usage

## S3 method for class 'siminteract'
simGG(
  obj,
  from = NULL,
  rug = TRUE,
  rug_position = "identity",
  to = NULL,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  method = "auto",
  spalette = "Set1",
  legend = "legend",
  leg.name = "",
  lcolour = "#2B8CBE",
  lsize = 1,
  pcolour = "#A6CEE3",
  psize = 1,
  alpha = 0.2,
  type = "ribbons",
  ...
)

Arguments

Details

Uses ggplot2 to plot the quantities of interest from siminteract objects, including marginal effects, first differences, hazard ratios, and hazard rates. If there are multiple strata, the quantities of interest will be plotted in a grid by strata.

Note: A dotted line is created at y = 1 (0 for first difference), i.e. no effect, for time-varying hazard ratio graphs. No line is created for hazard rates.

Note: if qi = "Hazard Ratio" or qi = "First Difference" then you need to have choosen more than one fitted value for X1 in coxsimInteract.

Value

a gg ggplot class object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. ”Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14(1): 63-82.

Keele, Luke. 2010. ”Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models.” Political Analysis 18(2): 189-205.

Carpenter, Daniel P. 2002. ”Groups, the Media, Agency Waiting Costs, and FDA Drug Approval.” American Journal of Political Science 46(3): 490-505.

See Also

coxsimInteract, simGG.simlinear, and ggplot2

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ lethal*prevgenx,
            data = CarpenterFdaData)

# Simulate Marginal Effect of lethal for multiple values of prevgenx
Sim1 <- coxsimInteract(M1, b1 = "lethal", b2 = "prevgenx",
                     X2 = seq(2, 115, by = 2), nsim = 100)

# Plot quantities of interest
simGG(Sim1)
simGG(Sim1, rug_position = 'jitter')

## Not run: 
# Change the order of the covariates to make a more easily
# interpretable hazard ratio graph.
M2 <- coxph(Surv(acttime, censor) ~ prevgenx*lethal,
             data = CarpenterFdaData)

# Simulate Hazard Ratio of lethal for multiple values of prevgenx
Sim2 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                    X1 = seq(2, 115, by = 2),
                    X2 = c(0, 1),
                    qi = "Hazard Ratio", ci = 0.9)

# Simulate First Difference
Sim3 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                       X1 = seq(2, 115, by = 2),
                       X2 = c(0, 1),
                       qi = "First Difference", spin = TRUE)

# Simulate Hazard Rate
Sim4 <- coxsimInteract(M2, b1 = "prevgenx", b2 = "lethal",
                       X1 = 100, X2 = 1, qi = "Hazard Rate")

# Plot quantities of interest
simGG(Sim1, xlab = "\nprevgenx",
       ylab = "Marginal Effect of lethal\n")
simGG(Sim2, type = 'ribbons', rug_position = 'jitter')
simGG(Sim3)
simGG(Sim4, to = 150, type = 'lines', legend = FALSE)

## End(Not run)

Plot simulated linear, non-time interacted quantities of interest from Cox Proportional Hazards Models

Description

simGG.simlinear uses ggplot2 to plot the quantities of interest from simlinear objects, including relative hazards, first differences, hazard ratios, and hazard rates.

Usage

## S3 method for class 'simlinear'
simGG(
  obj,
  from = NULL,
  to = NULL,
  rug = TRUE,
  rug_position = "identity",
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  method = "auto",
  spalette = "Set1",
  legend = "legend",
  leg.name = "",
  lcolour = "#2B8CBE",
  lsize = 1,
  pcolour = "#A6CEE3",
  psize = 1,
  alpha = 0.2,
  type = "ribbons",
  ...
)

Arguments

Details

Uses ggplot2 to plot the quantities of interest from simlinear objects, including relative hazards, first differences, hazard ratios, and hazard rates. If there are multiple strata, the quantities of interest will be plotted in a grid by strata. Note: A dotted line is created at y = 1 (0 for first difference), i.e. no effect, for time-varying hazard ratio graphs. No line is created for hazard rates.

Value

a gg ggplot class object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Licht, Amanda A. 2011. ”Change Comes with Time: Substantive Interpretation of Nonproportional Hazards in Event History Analysis.” Political Analysis 19: 227-43.

Keele, Luke. 2010. ”Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models.” Political Analysis 18(2): 189-205.

Carpenter, Daniel P. 2002. ”Groups, the Media, Agency Waiting Costs, and FDA Drug Approval.” American Journal of Political Science 46(3): 490-505.

See Also

coxsimLinear, simGG.simtvc, and ggplot2

Examples

# Load survival package
library(survival)
# Load Carpenter (2002) data
data("CarpenterFdaData")

# Estimate basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal +
            deathrt1 + acutediz + hosp01  + hhosleng +
            mandiz01 + femdiz01 + peddiz01 + orphdum +
            vandavg3 + wpnoavg3 + condavg3 + orderent +
            stafcder, data = CarpenterFdaData)

# Simulate and plot Hazard Ratios for stafcder variable
Sim1 <- coxsimLinear(M1, b = "stafcder",
                     Xj = c(1237, 1600),
                     Xl = c(1000, 1000),
                     qi = "Hazard Ratio",
                     spin = TRUE, ci = 0.99)
simGG(Sim1, method = 'lm', rug_position = 'jitter')
simGG(Sim1, rug_position = 'jitter')

## Not run: 
# Simulate and plot Hazard Rate for stafcder variable
Sim2 <- coxsimLinear(M1, b = "stafcder", nsim = 100,
                      qi = "Hazard Rate",
                      Xj = c(1237, 1600))
simGG(Sim2, type = 'points')
simGG(Sim2, type = 'lines')

## End(Not run)

Plot simulated polynomial quantities of interest from Cox Proportional Hazards Models

Description

simGG.simpoly uses ggplot2 to plot simulated relative quantities of interest from a simpoly class object.

Usage

## S3 method for class 'simpoly'
simGG(
  obj,
  from = NULL,
  to = NULL,
  rug = TRUE,
  rug_position = "identity",
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  method = "auto",
  spalette = "Set1",
  legend = "legend",
  leg.name = "",
  lcolour = "#2B8CBE",
  lsize = 1,
  pcolour = "#A6CEE3",
  psize = 1,
  alpha = 0.2,
  type = "ribbons",
  ...
)

Arguments

Details

Uses ggplot2 to plot the quantities of interest from simpoly objects.

Value

a gg ggplot class object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

See Also

coxsimPoly and ggplot2

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal +
       deathrt1 + acutediz + hosp01  + hhosleng + mandiz01 +
       femdiz01 + peddiz01 + orphdum + natreg +
       I(natreg^2) + I(natreg^3) + vandavg3 + wpnoavg3 +
       condavg3 + orderent + stafcder, data = CarpenterFdaData)

# Simulate simpoly First Difference
Sim1 <- coxsimPoly(M1, b = "natreg", qi = "First Difference",
           pow = 3, Xj = seq(1, 150, by = 5), nsim = 100)

# Plot simulations
simGG(Sim1, rug_position = 'jitter')

## Not run: 
# Simulate simpoly Hazard Ratio with spin probibility interval
Sim2 <- coxsimPoly(M1, b = "natreg", qi = "Hazard Ratio",
          pow = 3, Xj = seq(1, 150, by = 5), spin = TRUE,
          nsim = 100)

# Plot simulations
simGG(Sim2, type = 'ribbons', rug_position = 'jitter')

Sim3 <- coxsimPoly(M1, b = "natreg", qi = "Hazard Rate",
           pow = 3, Xj = c(1, 150), nsim = 100)

# Plot simulations
simGG(Sim3, type = 'lines')

## End(Not run)

Plot simulated penalised spline hazards from Cox Proportional Hazards Models

Description

simGG.simspline uses ggplot2 to plot quantities of interest from simspline objects, including relative hazards, first differences, hazard ratios, and hazard rates.

Usage

## S3 method for class 'simspline'
simGG(
  obj,
  SmoothSpline = TRUE,
  FacetTime = NULL,
  from = NULL,
  to = NULL,
  rug = TRUE,
  rug_position = "identity",
  xlab = NULL,
  ylab = NULL,
  zlab = NULL,
  title = NULL,
  method = "auto",
  lcolour = "#2B8CBE",
  lsize = 1,
  pcolour = "#A6CEE3",
  psize = 1,
  alpha = 0.2,
  type = "ribbons",
  ...
)

Arguments

Details

Uses ggplot2 to plot the quantities of interest from simspline objects, including relative hazards, first differences, hazard ratios, and hazard rates. If currently does not support hazard rates for multiple strata.

You can to plot hazard rates for a range of values of Xj in two dimensional plots at specific points in time. Each plot is arranged in a facet grid.

Note: A dotted line is created at y = 1 (0 for first difference), i.e. no effect, for time-varying hazard ratio graphs. No line is created for hazard rates.

Value

a gg ggplot class object.

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

See Also

coxsimLinear, simGG.simtvc, ggplot2

Examples

# Load Carpenter (2002) data
data("CarpenterFdaData")

# Load survival package
library(survival)

# Run basic model
# From Keele (2010) replication data
M1 <- coxph(Surv(acttime, censor) ~  prevgenx + lethal + deathrt1 +
                acutediz + hosp01  + pspline(hospdisc, df = 4) +
                pspline(hhosleng, df = 4) + mandiz01 + femdiz01 +
                peddiz01 + orphdum + natreg + vandavg3 + wpnoavg3 +
                pspline(condavg3, df = 4) + pspline(orderent, df = 4) +
                pspline(stafcder, df = 4), data = CarpenterFdaData)

# Simulate Relative Hazards for orderent
Sim1 <- coxsimSpline(M1, bspline = "pspline(stafcder, df = 4)",
                    bdata = CarpenterFdaData$stafcder,
                    qi = "Hazard Ratio",
                    Xj = seq(1100, 1700, by = 10),
                    Xl = seq(1099, 1699, by = 10), spin = TRUE, nsim = 100)

# Plot relative hazard
simGG(Sim1, alpha = 0.5)

## Not run: 
# Simulate Hazard Rate for orderent
Sim2 <- coxsimSpline(M1, bspline = "pspline(orderent, df = 4)",
                    bdata = CarpenterFdaData$orderent,
                    qi = "Hazard Rate",
                    Xj = seq(1, 30, by = 2), ci = 0.9, nsim = 10)

# Create a time grid plot
# Find all points in time where baseline hazard was found
unique(Sim2$sims$Time)

# Round time values so they can be exactly matched with FacetTime
Sim2$sims$Time <- round(Sim2$sims$Time, digits = 2)

# Create plot
simGG(Sim2, FacetTime = c(6.21, 25.68, 100.64, 202.36),
       type = 'ribbons', alpha = 0.5)

# Simulated Fitted Values of stafcder
Sim3 <- coxsimSpline(M1, bspline = "pspline(stafcder, df = 4)",
                    bdata = CarpenterFdaData$stafcder,
                    qi = "Hazard Ratio",
                    Xj = seq(1100, 1700, by = 10),
                    Xl = seq(1099, 1699, by = 10), ci = 0.90)

# Plot simulated Hazard Ratios
simGG(Sim3, xlab = "\nFDA Drug Review Staff", type = 'lines', alpha = 0.2)
simGG(Sim3, xlab = "\nFDA Drug Review Staff", alpha = 0.2,
      SmoothSpline = TRUE, type = 'points')

## End(Not run)

Plot simulated time-interactive hazard ratios or stratified time-interactive hazard rates from Cox Proportional Hazards Models

Description

simGG.simtvc uses ggplot2 to plot the simulated hazards from a simtvc class object created by coxsimtvc using ggplot2.

Usage

## S3 method for class 'simtvc'
simGG(
  obj,
  from = NULL,
  to = NULL,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  method = "auto",
  spalette = "Set1",
  legend = "legend",
  leg.name = "",
  lsize = 1,
  psize = 1,
  alpha = 0.2,
  type = "ribbons",
  ...
)

Arguments

Details

Plots either a time-interactive hazard ratios, first differences, and relative hazards, or the hazard rates for multiple strata. Currently the strata legend labels need to be changed manually (see revalue in the plyr package) in the simtvc object with the strata component. Also, currently the x-axis tick marks and break labels must be adjusted manually for non-linear functions of time. Note: A dotted line is created at y = 1 (0 for first difference), i.e. no effect, for time-varying hazard ratio graphs. No line is created for hazard rates.

Value

a gg ggplot class object

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Licht, Amanda A. 2011. ”Change Comes with Time: Substantive Interpretation of Nonproportional Hazards in Event History Analysis.” Political Analysis 19: 227-43.

Examples

## Not run: 
# Load Golub & Steunenberg (2007) Data
data("GolubEUPData")

# Load survival package
library(survival)

# Expand data
GolubEUPData <- SurvExpand(GolubEUPData, GroupVar = 'caseno',
                     Time = 'begin', Time2 = 'end', event = 'event')

# Create time interactions
BaseVars <- c('qmv', 'backlog', 'coop', 'codec', 'qmvpostsea', 'thatcher')
GolubEUPData <- tvc(GolubEUPData, b = BaseVars, tvar = 'end', tfun = 'log')

# Run Cox PH Model
M1 <- coxph(Surv(begin, end, event) ~ qmv + qmvpostsea + qmvpostteu +
                coop + codec + eu9 + eu10 + eu12 + eu15 + thatcher +
                agenda + backlog + qmv_log + qmvpostsea_log + coop_log +
                codec_log + thatcher_log + backlog_log,
            data = GolubEUPData, ties = "efron")

# Create simtvc object for Relative Hazard
Sim1 <- coxsimtvc(obj = M1, b = "qmv", btvc = "qmv_log",
                   tfun = "log", from = 80, to = 2000,
                   Xj = 1, by = 15, ci = 0.99, nsim = 100)

# Create plot
simGG(Sim1, legend = FALSE)

# Create simtvc object for First Difference
Sim2 <- coxsimtvc(obj = M1, b = "qmv", btvc = "qmv_log",
                 qi = "First Difference", Xj = 1,
                 tfun = "log", from = 80, to = 2000,
                 by = 15, ci = 0.95)

# Create simtvc object for Hazard Ratio
Sim3 <- coxsimtvc(obj = M1, b = "backlog", btvc = "backlog_log",
                  qi = "Hazard Ratio", Xj = c(191, 229),
                  Xl = c(0, 0),
                  tfun = "log", from = 100, to = 2000,
                  by = 15, ci = 0.99)

# Create plots
simGG(Sim2, type = 'points')
simGG(Sim3, leg.name = "Comparision", from = 1200, type = 'lines')

## End(Not run)

An R package for simulating and plotting quantities of interest from Cox Proportional Hazard models.

Description

An R package for simulating and plotting quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models.

The package includes the following simulation functions:

coxsimLinear

a function for simulating relative hazards, first differences, hazard ratios, and hazard rates for linear, non-time interacted covariates from Cox Proportional Hazard (coxph) models.

coxsimtvc

a function for simulating time-interactive hazards (relative hazards, first differences, and hazard ratios) for covariates from Cox Proportional Hazard models. The function will calculate time-interactive hazard ratios for multiple strata estimated from a stratified Cox Proportional Hazard model.

coxsimSpline

a function for simulating quantities of interest from penalised splines using multivariate normal distributions. Currently does not support simulating hazard rates from stratified models. Note: be extremely careful about the number of simulations you ask the function to find. It is very easy to ask for more than your computer can handle.

coxsimPoly

a function for simulating quantities of interest for a range of values for a polynomial nonlinear effect from Cox Proportional Hazard models.

coxsimInteract

a function for simulating quantities of interest for linear multiplicative interactions, including marginal effects and hazard rates.

Results from these functions can be plotted using the simGG method.

The package also includes two functions that make it easier to create time interactions:

SurvExpand

a function to convert a data frame of non-equal interval continuous observations into equal interval continuous observations.

tvc

a function to create time interaction variables that can be used in a coxph model (or any other model with time interactions).

setXl

a function for setting valid Xl values given a sequence of fitted Xj values. This makes it more intituitive to find hazard ratios and first differences for comparisons between some Xj fitted values and Xl values other than 0.

References

Gandrud, Christopher. 2015. simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.

Create a time interaction variable

Description

tvc creates a time interaction variable that can be used in a coxph model (or any other model with time interactions)

Usage

tvc(data, b, tvar, tfun = "linear", pow = NULL, vector = FALSE)

Arguments

Details

Interacts a variable with a specified function of time. Possible functions of time include 'linear', natural 'log', and exponentiated ('power').

Value

a data frame or vector. If a data frame is returned it will include all of the original variables as well as the interactions denoted by a variable name 'bn_tfun', where bn is one variable name from b and tfun as entered into the function.

See Also

SurvExpand, simGG.simtvc, coxsimtvc, and coxph

Examples

# Load Golub & Steunenberg (2007) Data
data('GolubEUPData')

# Subset PURELY TO SPEED UP THE EXAMPLE
GolubEUPData <- GolubEUPData[1:500, ]

# Expand data into equally spaced time intervals
GolubEUPData <- SurvExpand(GolubEUPData, GroupVar = 'caseno',
                  Time = 'begin', Time2 = 'end', event = 'event')

# Create natural log time interaction with the qmv variable
GolubEUPData$Lqmv <- tvc(GolubEUPData, b = 'qmv', tvar = 'end', tfun = 'log',
                         vector = TRUE)

# Create interactions for a vector of variables
BaseVars <- c('qmv', 'backlog', 'coop', 'codec', 'qmvpostsea', 'thatcher')

Test <- tvc(GolubEUPData, b = BaseVars, tvar = 'end', tfun = 'log')