Help for package metawho

Type:

Package

Title:

Meta-Analytical Implementation to Identify Who Benefits Most from Treatments

Version:

0.2.0

Description:

A tool for implementing so called 'deft' approach (see Fisher, David J., et al. (2017) <doi:10.1136/bmj.j573>) and model visualization.

License:

GPL-3

URL:

https://github.com/ShixiangWang/metawho

BugReports:

https://github.com/ShixiangWang/metawho/issues

Depends:

metafor, R (≥ 3.5)

Imports:

dplyr, forestmodel, magrittr, purrr, rlang (≥ 0.1.2), stats

Suggests:

covr, knitr, rmarkdown, roxygen2, testthat

VignetteBuilder:

knitr

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.0.0

NeedsCompilation:

Packaged:

2019-12-06 14:56:21 UTC; Shixiang

Author:

Shixiang Wang

[aut, cre]

Maintainer:

Shixiang Wang <w_shixiang@163.com>

Repository:

CRAN

Date/Publication:

2019-12-06 16:00:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Implement deft method

Description

'deft' method is a meta-analytical approach to pool conclusion from multiple studies. More details please see references.

Usage

deft_do(prepare, group_level, method = "FE")

Arguments

prepare

a result data.frame from deft_prepare function or a data.frame contains at least 'trial', 'subgroup', 'yi' and 'sei' these four columns.

group_level

level of subgroup, should be a character vector with length 2 and the reference should put in the first. For example, if you have 'Male' and 'Female' groups and want compare 'Female' with 'Male', then should set c('Male', 'Female').

method

character string specifying whether a fixed- or a random/mixed-effects model should be fitted. A fixed-effects model (with or without moderators) is fitted when using method="FE". Random/mixed-effects models are fitted by setting method equal to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", or "GENQ". Default is "REML". See ‘Details’.

Details

About model fit, please see metafor::rma().

Value

a list which class is 'deft'.

Author(s)

Shixiang Wang w_shixiang@163.com

References

Fisher, David J., et al. "Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?." bmj 356 (2017): j573.

Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).

Examples

data("wang2019")
deft_do(wang2019, group_level = c("Male", "Female"))

Prepare log transformation data for effect size estimation according to confidence level and distribution

Description

A variety of different outcome measures which used in meta-analysis as input are in the form of log, such as hazard ratio (HR). This function is used to do log transformation to calculate effect size and standard error. Then the result can be easier used for model fit.

Usage

deft_prepare(data, conf_level = 0.05)

Arguments

data

a data.frame contains at least columns 'trial', 'hr', 'ci.lb', 'ci.ub' and 'ni'.

conf_level

a number specify confidence level, default is 0.05.

Value

a data.frame

Author(s)

Shixiang Wang w_shixiang@163.com

References

Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).

Examples

### specify hazard ratios (hr)
hr <- c(0.30, 0.11, 1.25, 0.63, 0.90, 0.28)
### specify lower bound for hr confidence intervals
ci.lb <- c(0.09, 0.02, 0.82, 0.42, 0.41, 0.12)
### specify upper bound for hr confidence intervals
ci.ub <- c(1.00, 0.56, 1.90, 0.95, 1.99, 0.67)
### specify sample number
ni <- c(16L, 18L, 118L, 122L, 37L, 38L)
### trials
trial <- c(
  "Rizvi 2015", "Rizvi 2015",
  "Rizvi 2018", "Rizvi 2018",
  "Hellmann 2018", "Hellmann 2018"
)
### subgroups
subgroup <- rep(c("Male", "Female"), 3)

entry <- paste(trial, subgroup, sep = "-")
### combine as data.frame

wang2019 <-
  data.frame(
    entry = entry,
    trial = trial,
    subgroup = subgroup,
    hr = hr,
    ci.lb = ci.lb,
    ci.ub = ci.ub,
    ni = ni,
    stringsAsFactors = FALSE
  )

deft_prepare(wang2019)

Show deft result

Description

Show deft result

Usage

deft_show(
  deft,
  element,
  study_labels = NULL,
  headings = list(study = ifelse(element == "all", "Study-subgroup", "Study"), n = "N",
    measure = NULL, ci = "HR (95% CI)"),
  trans = base::exp,
  show_model = ifelse(element == "all", FALSE, TRUE),
  show_stats = list(`I^2` = rlang::quo(sprintf("%0.1f%%", I2)), p =
    rlang::quo(format.pval(QEp, digits = 2))),
  ...
)

Arguments

deft

result from deft_do.

element

'all' or 'subgroup'.

study_labels

labels for studies.

headings

a list for controlling plot headings.

trans

an optional transform function used on the numeric data for plotting the axes

show_model

a logical value, if TRUE, show model result, otherwise only show forest plots for studies

show_stats

a list of stats to show at the bottom of the forest plot for e.g. heterogeneity

...

other arguments except 'panels', 'trans', 'study_labels', and 'show_stats' passed to forestmodel::forest_rma().

Value

a ggplot object

Author(s)

ShixiangWang w_shixiang@163.com

Examples

data("wang2019")
res <- deft_do(wang2019, group_level = c("Male", "Female"))

p1 <- deft_show(res, "all")
p1

p2 <- deft_show(res, "subgroup")
p2

Tidy eval helpers

Description

sym() creates a symbol from a string and syms() creates a list of symbols from a character vector.
enquo() and enquos() delay the execution of one or several function arguments. enquo() returns a single quoted expression, which is like a blueprint for the delayed computation. enquos() returns a list of such quoted expressions.
expr() quotes a new expression locally. It is mostly useful to build new expressions around arguments captured with enquo() or enquos(): expr(mean(!!enquo(arg), na.rm = TRUE)).
as_name() transforms a quoted variable name into a string. Supplying something else than a quoted variable name is an error.

That's unlike as_label() which also returns a single string but supports any kind of R object as input, including quoted function calls and vectors. Its purpose is to summarise that object into a single label. That label is often suitable as a default name.

If you don't know what a quoted expression contains (for instance expressions captured with enquo() could be a variable name, a call to a function, or an unquoted constant), then use as_label(). If you know you have quoted a simple variable name, or would like to enforce this, use as_name().

To learn more about tidy eval and how to use these tools, visit https://tidyeval.tidyverse.org and the Metaprogramming section of Advanced R.

Hazard ratio (HR) for disease progression analysis comparing TMB-high with TMB-low in three NSCLC datasets

Description

Hazard ratio (HR) for disease progression analysis comparing TMB-high with TMB-low in three NSCLC datasets

Format

a data.frame

Source

Wang, Shixiang, et al. "The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients' sex." International journal of cancer (2019).

Examples

data("wang2019")