PostMeanSurvPLLH

Computes the posterior mean survival probabilities for a vector x for the Piecewise Linear Log Hazard model (PLLH)

Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.

Andrew Chapple

BayesReversePLLH

Fits the Bayesian Piecewise Linear Log-Hazard Model

PostMeanSurvPLLH function

<dl><dt>x</dt>
<dd>Vector of times to compute the posterior mean survival probability.</dd>
<dt>G1</dt>
<dd>List of posterior samples from the BayesPiecewiseLinearLogHazard function.</dd></dl>

Arguments

Computes the posterior mean survival probabilities for a vector x for the Piecewise Linear Log Hazard model (PLLH) — PostMeanSurvPLLH

<dl>

<dt>x</dt>
<dd>Vector of times to compute the posterior mean survival probability.</dd>


<dt>G1</dt>
<dd>List of posterior samples from the BayesPiecewiseLinearLogHazard function.</dd>

</dl>

PostMeanSurvPLLH: Computes the posterior mean survival probabilities for a vector x for the Piecewise Linear Log Hazard model (PLLH)

Description

Usage

Value

Arguments