trtsel_measures {TreatmentSelection}

a simple function to estimate performance measures for a rule used to select treatment.

Description

Provides point estimates for summary measures to evaluate a rule used to select treatment.

Usage

trtsel_measures(event, trt, trt.rule, trt.effect, time,
  default.trt = c("trt all", "trt none"), prediction.time = NULL,
  silent = FALSE)

Arguments

event

vector for adverse event. Can be binary (1 is bad, 0 is good) or continuous (large numbers are worse). If failure time variable 'time' is set, event is used as the adverse event status indicator.

trt

binary trt status 1 for "treated" and 0 for "un-treated."

trt.rule

a binary treatment rule used to recommend treatment where 1 means recommend treatment and 0 means recommend no treatment.

trt.effect

estimated treatment effects.

time

the failure time for survival outcomes.

default.trt

The default treatment assignment to compare with marker-based treatment. Can either be set at "trt all" (default) or "trt none". Use "trt all" if everyone is treated and the aim is to discover those who would benefit from no treatment, but use "trt none" if the common practice is to treat no-one and the goal is to discover those who would benefit from treatment.

prediction.time

a landmark prediction time used only when the 'time' variable is set.

silent

suppress messages

Examples

data(tsdata)
#The user must specify a vector of clinical outcomes, 
#a vector of treatment assigments, and a vector of 
#marker-based treatment recommendations based on the pre-specified rule.

#Here we let Y1_disc represent a user-specified treatment 
#rule and evaluate its performance.

trtsel_measures(event = tsdata$event, trt = tsdata$trt, trt.rule = 1- tsdata$Y1_disc )

#We can also fit our own risk model using GLM, use this model
#to develop a marker-based treatment recommendation, and evaluate its performance. 
#This allows us to obtain model-based estimates of performance:

mod <- glm(event~trt*Y1_disc,  data = tsdata, family = binomial())

tsdata.0 <- tsdata; 
tsdata.0$trt = 0 
tsdata.1 <- tsdata;
tsdata.1$trt = 1
delta.hat <- predict(mod, 
                    newdata= tsdata.0,
                    type = "response") - 
            predict(mod,
                    newdata= tsdata.1, 
                    type = "response")

trtsel_measures(event = tsdata$event, trt = tsdata$trt, 
               trt.rule = 1- tsdata$Y1_disc, trt.effect = delta.hat )