Simulating Survival Data Using the <b>simsurv</b> <i>R</i> Package

Simulating competing risks data in survival analysis.

Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, ...

sgr: A Package for Simulating Conditional Fake Ordinal Data

Many self-report measures of attitudes, beliefs, personality, and pathology include items that can be easily manipulated by respondents. For example, an individual may deliberately attempt to manipulate or distort responses to simulate grossly exaggerated physical or psychological symptoms in order to reach specific goals such as, for example, obtaining financial compensation, avoiding being ch...

Survival of Dialysis Patients Using Random Survival Forest Model in Low-Dimensional Data with Few-Events

Background:Dialysis is a process for eliminating extra uremic fluids of patients with chronic renal failure. The present study aimed to determine the variables that influence the survival of dialysis patients using random survival forest model (RSFM) in low-dimensional data with low events per variable (EPV). Methods:In this historical cohort study, infor...

Simulating Clustered Survival Data with Proportional Hazards Margins

RCASPAR: A package for survival time analysis using high-dimensional data

RCASPAR allows the utilization of high-dimensional biological data as covariates for survival time prediction based on the step-wise Cox model. The data is usually that obtained from microarray measurements, but it can be any sort of data thought reasonable for this purpose by the user. Preprocessing must be done by the user, and the final values needed for use in the program are what should be...