winr (version 1.0.0)

adj_winodds: Adjusted win odds

Description

Randomization-based adjustment of the win odds for baseline covariates and strata.

Usage

adj_winodds(
  data,
  pid,
  baseline = NULL,
  outcome,
  covars = NULL,
  strata = NULL,
  arm,
  method = "small",
  sig.level = 0.05
)

Value

A dataframe containing:

logWR

natural log-transformed win ratio

SE_logWR

standard error of log-transformed win ratio

Var_logWR

sample variance of log-transformed win ratio

Chi_Square

Pearson's Chi-squared test statistic corresponding to logWR

p_value

p-value corresponding to the Pearson's Chi-squared test

WR

win ratio

LCL_WR

lower bound of \((1-\alpha/2)\times 100\%\) CI for WR

UCL_WR

upper bound of \((1-\alpha/2)\times 100\%\) CI for WR

Arguments

data

a dataframe or matrix containing the analysis data. Must be in wide format such that a participant's repeated responses are in a single row, and each response is in a separate column.

pid

a string indicating the name of the variable corresponding to participant ID.

baseline

a string indicating the name of the outcome measured at baseline. If not specified, defaults to NULL, and no baseline adjustment is employed.

outcome

a vector of strings indicating the names of the outcomes measured at each visit. Baseline, if specified, will be concatenated to this vector within the code. The outcomes must have at least an ordinal measurement scale with larger values being better than smaller values. Thus, the outcome can be ordered categories or continuous measurements.

covars

a vector of strings indicating the names of the covariates (measured at baseline) used for adjustment. These covariates must be numeric and can be measured on a binary, categorical, ordered categorical, or continuous scale. If not specified, defaults to NULL and no covariate adjustment is employed.

strata

a string indicating the name of the variable used for stratification. If not specified, defaults to NULL and no stratification adjustment is utilized.

arm

a string indicating the name of the variable for treatment arm. Treatment arm must be a positive integer such that the test treatment arm is ALWAYS higher in value than the control arm.

method

a string "small" or "large" used to denote the sample size method employed. The small sample size method is recommended unless within-stratum sample size is reasonably large (e.g., >= 50), number of visits is small (e.g., <=6), and number of covariates is small (e.g., <=4). Defaults to "small."

sig.level

significance level (Type I error probability). Defaults to 0.05.

Examples

#--------------------------
# Respiratory example
#--------------------------

# Since IDs repeat at centers 1 and 2, create a new, unique ID
resp$UniqID<-resp$Center*100+resp$ID

# Convert treatment arm to binary
resp$Trt<-1*(resp$Treatment=="T")

# Indicator for male
resp$SexNum<-1*(resp$Sex=="M")

adj_winodds(data=resp,
            pid="UniqID",
            baseline="Baseline",
            outcome=c("Visit1","Visit2","Visit3","Visit4"),
            covars= c("SexNum","Age"),
            strata="Center",
            arm="Trt",
            method="small",
            sig.level=0.05)

#----------------------
# Dermatology example
#----------------------

#Generate indicators for stage 4 and 5
skin$Stage4 = (skin$STAGE == 4)*1
skin$Stage5 = (skin$STAGE == 5)*1

# Generate treatment center
skin$center<-ifelse(skin$INV==5,1,
  ifelse(skin$INV==6,2,
    ifelse(skin$INV==8,3,
      ifelse(skin$INV==9,4,
        ifelse(skin$INV==10,5,6)))))

# Generate treatment center that pools centers 3 and 4 due to small sample size
skin$center2 = skin$center
skin$center2<-ifelse(skin$center == 4, 3, skin$center)

# Generate participant IDs
skin$ID<-1:nrow(skin)

adj_winodds(data=skin,
            pid="ID",
            baseline=NULL,
            outcome=c("R1","R2","R3"),
            covars= c("Stage4","Stage5"),
            strata="center2",
            arm="TRT",
            method="small",
            sig.level=0.05)