bayesian_boot_irrd: Obtaining Bayesian Bootstrap Sample for Individual Risk...

In semicmprskcoxmsm: Use Inverse Probability Weighting to Estimate Treatment Effect for Semi Competing Risks Data

Obtaining Bayesian Bootstrap Sample for Individual Risk Difference and Risk Ratio.

Description

bayesian_boot_irrd provides the bootstrap sample for individual risk difference and risk ratio, it can be used for further inferences.

Usage

bayesian_boot_irrd(dat2,B,sigma_2_0, EM_initial, varlist, t1_star,t)

Arguments

dat2	The dataset, includes non-terminal events, terminal events as well as event indicator.
B	Number of bootstraps that the user want to run, typically we use B = 500.
sigma_2_0	Initial value for sigma_2 for the general Markov model
EM_initial	Initial value for the EM algorithm, the output of OUT_em_weights.
varlist	Confounder list for the propensity score model.
t1_star	Fixed non-terminal event time for estimating risk difference/ratio for terminal event following the non-terminal event.
t	Fixed time point of interest to compare the individual risk difference / ratio.

Details

For each bootstrap sample:

1. Generate n standard exponential (mean and variance 1) random variates : u_1, u_2,..., u_n;

2. The weights for the Bayesian bootstrap are: w_{i}^{boot} = u_i / \bar{u}, where \bar{u} = n^{-1}∑_{i=1}^{n} u_i;

3. Calculate the propensity score and IP weights w_{i}^{IPW} based on Bayesian bootstrap weighted data, and assigned the weights for fitting the MSM general Markov model as w_i = w_{i}^{boot} * w_{i}^{IPW}.

4. After obtaining \hat{θ} and \hat{b}_i, for each individual i, calculate the IRR and IRD by plugging \hat{θ}, \hat{b}_i and a=0, a=1 separately at time t.

The 95% prediction intervals (PI) cam be obtained by the normal approximation using bootstrap standard error.

Value

RD1_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk difference for time to non-terminal event at time t.
RD2_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk difference for time to terminal event without non-terminal event at time t.
RD3_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk difference for time to terminal event following non-terminal event by t1_start at time t.
RR1_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk ratio for time to non-terminal event at time t.
RR2_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk ratio for time to terminal event without non-terminal event at time t.
RR3_boot	A n times B matrix as the Bayesian bootstrap sample for each data point. The sample is for individual risk ratio for time to terminal event following non-terminal event by t1_start at time t.

semicmprskcoxmsm documentation built on April 30, 2022, 1:08 a.m.