causalCmprsk (version 2.0.0)

get.numAtRisk: Number-at-risk in raw and weighted data

Description

Obtaining time-varying number-at-risk statistic in both raw and weighted data

Usage

get.numAtRisk(df, X, E, A, C = NULL, wtype = "unadj", cens = 0)

Value

A list with two fields:

• trt.0 a matrix with three columns, time, num and sample corresponding to the treatment arm with A=0. The results for both weighted and unadjusted number-at-risk are returnd in a long-format matrix. The column time is a vector of time points at which we calculate the number-at-risk. The column num is the number-at-risk. The column sample is a factor variable that gets one of two values, "Weighted" or "Unadjusted". The estimated number-at-risk in the weighted sample corresponds to the rows with sample="Weighted".

• trt.1 a matrix with three columns, time, num and sample corresponding to the treatment arm with A=1. The results for both weighted and unadjusted number-at-risk are returnd in a long-format matrix. The column time is a vector of time points at which we calculate the number-at-risk. The column num is the number-at-risk. The column sample is a factor variable that gets one of two values, "Weighted" or "Unadjusted". The estimated number-at-risk in the weighted sample corresponds to the rows with sample="Weighted".

Arguments

df

a data frame that includes time-to-event X, type of event E, a treatment indicator A and covariates C.

X

a character string specifying the name of the time-to-event variable in df.

E

a character string specifying the name of the "event type" variable in df.

A

a character specifying the name of the treatment/exposure variable. It is assumed that A is a numeric binary indicator with 0/1 values, where A=1 is assumed a treatment group, and A=0 a control group.

C

a vector of character strings with variable names (potential confounders) in the logistic regression model for Propensity Scores, i.e. P(A=1|C=c). The default value of C is NULL corresponding to wtype="unadj" that will estimate treatment effects in the raw (observed) data.

wtype

a character string variable indicating the type of weights that will define the target population for which the ATE will be estimated. The default is "unadj" - this will not adjust for possible treatment selection bias and will not use propensity scores weighting. It can be used, for example, in data from a randized controlled trial (RCT) where there is no need for emulation of baseline randomization. Other possible values are "stab.ATE", "ATE", "ATT", "ATC" and "overlap". See Table 1 from Li, Morgan, and Zaslavsky (2018). "stab.ATE" is defined as P(A=a)/P(A=a|C=c) - see Hernán et al. (2000).

cens

an integer value in E that corresponds to censoring times recorded in X. By default fit.nonpar assumes cens=0

See Also

get.weights, get.pointEst, causalCmprsk

Examples

# create a data set
n <- 1000
set.seed(7)
c1 <- runif(n)
c2 <- as.numeric(runif(n)< 0.2)
set.seed(77)
cf.m.T1 <- rweibull(n, shape=1, scale=exp(-(-1 + 2*c1)))
cf.m.T2 <-  rweibull(n, shape=1, scale=exp(-(1 + 1*c2)))
cf.m.T <- pmin( cf.m.T1, cf.m.T2)
cf.m.E <- rep(0, n)
cf.m.E[cf.m.T1<=cf.m.T2] <- 1
cf.m.E[cf.m.T2