data.preproc.surv {precmed}

Data preprocessing Apply at the beginning of catefitcount(), catecvcount(), catefitsurv(), and catecvsurv(), after arg.checks()

Description

Data preprocessing Apply at the beginning of catefitcount(), catecvcount(), catefitsurv(), and catecvsurv(), after arg.checks()

Usage

data.preproc.surv(
  fun,
  cate.model,
  ps.model,
  ipcw.model = NULL,
  tau0 = NULL,
  data,
  prop.cutoff = NULL,
  prop.multi = NULL,
  ps.method,
  initial.predictor.method = NULL,
  response = "count"
)

Arguments

fun

A function for which argument check is needed; "catefit" for catefitcount() and catefitsurv(), "crossv" for catecvcount() and catecvsurv(), and "drinf" for drcount.inference() and drsurv.inference(). No default.

cate.model

A formula describing the outcome model to be fitted. The outcome must appear on the left-hand side.

ps.model

A formula describing the propensity score model to be fitted. The treatment must appear on the left-hand side. The treatment must be a numeric vector coded as 0/1. If data are from a RCT, specify ps.model as an intercept-only model.

ipcw.model

A formula describing inverse probability of censoring weighting(IPCW) model to be fitted. If covariates are the same as outcome model, set ipcw.model = NULL. Otherwise, the left-hand side must be empty and the right-hand side is a covariates model.

tau0

The truncation time for defining restricted mean time lost. Default is NULL, which corresponds to setting the truncation time as the maximum survival time in the data

data

A data frame containing the variables in the outcome, propensity score, and IPCW models; a data frame with n rows (1 row per observation).

prop.cutoff

A vector of numerical values (in (0, 1]) specifying percentiles of the estimated log CATE scores to define nested subgroups. Each element represents the cutoff to separate observations in nested subgroups (below vs above cutoff). The length of prop.cutoff is the number of nested subgroups. An equally-spaced sequence of proportions ending with 1 is recommended. Default is seq(0.5, 1, length = 6).

prop.multi

A vector of numerical values (in [0, 1]) specifying percentiles of the estimated log CATE scores to define mutually exclusive subgroups. It should start with 0, end with 1, and be of length(prop.multi) > 2. Each element represents the cutoff to separate the observations into length(prop.multi) - 1 mutually exclusive subgroups. Default is c(0, 1/3, 2/3, 1).

ps.method

A character value for the method to estimate the propensity score. Allowed values include one of: 'glm' for logistic regression with main effects only (default), or 'lasso' for a logistic regression with main effects and LASSO penalization on two-way interactions (added to the model if interactions are not specified in ps.model). Relevant only when ps.model has more than one variable.

initial.predictor.method

A character vector for the method used to get initial outcome predictions conditional on the covariates. Only applies when score.method includes 'twoReg' or 'contrastReg'. Allowed values include one of 'randomForest' (survival outcomes only), 'boosting', 'logistic' (survival outcomes only, fast), 'poisson' (count outcomes only, fast), and 'gam' (count outcomes only). Default is NULL, which assigns 'boosting' for count outcomes and 'randomForest' for survival outcomes.

response

The type of response variables; count (default) or survival.

Value

A list of elements: - y: outcome; vector of length n (observations) - d : the event indicator; vector of length n; only if respone = "survival" - trt: binary treatment; vector of length n - x.ps: matrix of p.ps baseline covariates specified in the propensity score model (plus intercept); dimension n by p.ps + 1 - x.cate: matrix of p.cate baseline covariates specified in the outcome model; dimension n by p.cate - x.ipcw: matrix of p.ipw baseline covarites specified in inverse probability of censoring weighting model; dimension n by p.ipw - time: offset; vector of length n; only if response = "count" - if fun = "catefit": - prop: formatted prop.cutoff - prop.no1: formatted prop.cutoff with 1 removed if applicable; otherwise prop.no1 is the same as prop - if fun = "crossv" - prop.onlyhigh: formatted prop.cutoff with 0 removed if applicable - prop.bi; formatted prop.cutoff with 0 and 1 removed if applicable - prop.multi: formatted prop.multi, starting with 0 and ending with 1