PosteriorEDTRProbs {SMARTbayesR}

Convert Treatment Sequence Draws into Embedded Dynamic Treatment Regime Draws

Description

Apply Robins' G-computation formula to compute the embedded dynamic treatment regime draws from as a weighted average of treatment sequence and stage-1 response probability draws.

If design is "design-1", then compute for Design 1 SMART with 6 embedded treatment sequences and 4 embedded dynamic treatment regimes.

If design is "general", then compute for General SMART with 8 embedded treatment sequences and 8 embedded dynamic treatment regimes.

If design is "design-3", then compute for Design 3 SMART with 9 embedded treatment sequences and 6 embedded dynamic treatment regimes.

Usage

PosteriorEDTRProbs(x, design = "design-1")

Arguments

x

A data frame consisting of draws from the posterior of the end of study response probabilities of each treatment sequence and of stage-1 response probabilities for each stage-1 treatment

design

Which SMART design to compute the posterior draws for: "design-1" or "general" or "design-3".

Details

For the General SMART design, x should have columns p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, s1, and s2.

For the Design-1 SMART, x should have columns p_1, p_2, p_3, p_4, p_5, p_6, s1, and s2.

For the design-3 SMART, x should have columns

p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, p_9 s1, s2, and s3

These are the posterior draws of the response probabilities for each treatment sequence and stage-1 response probability draws.

s1 contains the draws of the stage-1 response probability for the first treatment, s2 is analogous for the second treatment and s3 for the third treatment.

Value

Matrix of EDTR specific posterior response probability draws at the end of the study There will be 4 columns for design-1, 8 columns for design general, and 6 columns for design-3 each corresponding to an EDTR. The number of rows will be the same as that of x.

Examples

dat <- SimDesign1(sample_size=250,
                  response_prob = c(0.5,0.9,0.3,0.7,0.5,0.8),
                  stage_one_trt_one_response_prob = 0.7,
                  stage_one_trt_two_response_prob = 0.4)

x <- PosteriorTrtSeqProb(niter = 1000, dat, design = "design-1")

PosteriorEDTRProbs(x, design = "design-1")