deft_prepare

0th

Percentile

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

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, distribution = c("N", "t"), conf_level = 0.05,
df = Inf, var = FALSE)
Arguments
data

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

distribution

a character specify distribution. 'N' for normal, 't' for student distribution. Default is N for normal distribution.

conf_level

a number specify confidence level, default is 0.05.

df

a number specify degree of freedom for t distribution

var

default is FALSE. If TRUE, the sampling variance will be computed.

a data.frame

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).

• deft_prepare
Examples
# NOT RUN {
### 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)
### 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,
stringsAsFactors = FALSE
)

deft_prepare(wang2019)
# }
Documentation reproduced from package metawho, version 0.1.0, License: GPL-3

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