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Related Experiment Video

Updated: May 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Risk-stratified imputation in survival analysis.

Richard E Kennedy¹, Kofi P Adragni, Hemant K Tiwari

¹Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA.

Clinical Trials (London, England)

|July 3, 2013

Summary

Risk-stratified imputation improves survival analysis by addressing withdrawal bias in clinical trials. This method provides accurate treatment effect estimates and variance, outperforming traditional imputation techniques.

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Last Updated: May 10, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Area of Science:

Biostatistics
Clinical Trials
Survival Analysis

Background:

Covariate-dependent censoring in randomized trials can bias treatment effect estimates due to altered recruitment and eligibility criteria aimed at minimizing withdrawals.
Existing imputation methods for survival analysis may offer unbiased treatment effect estimates but can inaccurately estimate variance based on the selected imputation pool.

Purpose of the Study:

To introduce risk-stratified imputation, an enhanced method for addressing withdrawal bias related to event risk in time-to-event analyses.
To provide a robust alternative for handling withdrawals in clinical trials where event risk influences patient dropout.

Main Methods:

The proposed algorithm imputes outcomes from a risk-stratified sample, matching subjects based on treatment and relevant covariates.
Stratification prior to imputation ensures censored observations are representative within their risk group, aligning with time-to-event analysis requirements.
Comparison with case deletion and bootstrap imputation was conducted using simulated data and a clinical trial example.

Main Results:

Risk-stratified imputation yielded treatment effect estimates comparable to bootstrap and auxiliary variable imputation in simulations.
Crucially, it avoided the variance estimation inaccuracies associated with bootstrap and auxiliary variable imputation.
Similar performance was observed when analyzing clinical trial data, confirming its practical utility.

Conclusions:

Risk-stratified imputation is particularly beneficial for clinical trials with treatment-related withdrawal rate differences between groups.
While effective for categorical covariates, its application with continuous covariates may require careful consideration of the matching window width.
This method offers a valuable tool for improving the analysis of clinical trials facing withdrawal challenges.