pated: Prognostic Variables Assisted Treatment Effect Detection

In multipleOutcomes: Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes

Prognostic Variables Assisted Treatment Effect Detection

Description

pated is a wrapper function of multipleOutcomes for testing treatment effect in randomized clinical trials. It assumes that prognostic variables are fully randomized. This assumption can help enhancing statistical power of conventional approaches in detecting the treatment effect. Specifically, the sensitivity of the conventional models specified in ... are improved by pated.

Usage

pated(..., family, data)

Arguments

...	formulas of models to be fitted, or moment functions for gmm.
family	a character vector of families to be used in the models. Currently only gaussian, binomial, coxph and gmm are supported. long for longitudinal data may be supported in the future. family can be of length 1 if all models are fitted in thesame family; otherwise family should be specified for each of the models in ....
data	a data frame if all models are fitted on the same dataset; otherwise a list of data frames for fitting models in .... Note that a dataset can be used to fit multiple models, thus, length(data) is unnecessary to be equal to the number of models in .... The row names in a data frame are treated as sample IDs. Consequently, for any two records in different data frames that correspond to the same sample, their row names should be consistent.

Value

a data frame of testing results.

Examples

# see vignettes

multipleOutcomes documentation built on June 22, 2024, 10:25 a.m.