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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Functional win-fractions regression models for composite outcomes.

Abhisek Chakraborty1, Abhishek Mandal2

  • 1Global Statistical Sciences, Eli Lilly and Company, IN, 46285, Indianapolis, USA. abhisek.chakraborty@lilly.com.

Lifetime Data Analysis
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible win fraction regression framework using splines to accurately analyze complex covariate effects in clinical trials. This method improves treatment effect estimation for composite outcomes, enhancing reliability and predictive accuracy.

Keywords:
[Formula: see text]-splinesBreast cancerCardio-vascular clinical trialsChronic kidney diseaseGeneralized method of momentsMoment condition modelPrioritized composite outcomesWin statistic

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Modeling

Background:

  • Win ratios are common for evaluating interventions with composite outcomes in clinical trials.
  • Adjusting win ratios for covariates improves treatment effect estimation.
  • Traditional parametric models struggle with complex, non-linear covariate effects on composite outcomes.

Purpose of the Study:

  • To introduce a flexible win fraction regression framework using splines to model complex covariate effects.
  • To develop an efficient computational algorithm for inference using the generalized method of moments (GMM).
  • To provide a large-sample test for assessing the significance of functional covariates.

Main Methods:

  • Development of a win fraction regression framework utilizing B-splines.
  • Application of the moment condition model framework and GMM for inference.
  • Derivation of a large-sample test based on the asymptotic distribution of spline coefficients.

Main Results:

  • The proposed spline-based framework adaptively assesses the extent and nature of covariate effects.
  • Simulations demonstrate favorable operating characteristics compared to existing methods.
  • The methodology shows practical utility in analyzing composite time-to-event data.

Conclusions:

  • The flexible win fraction regression framework effectively captures complex, non-linear covariate effects.
  • This approach enhances the accuracy and precision of treatment effect estimates in clinical trials.
  • The method offers a valuable tool for analyzing composite time-to-event outcomes in cardiovascular and breast cancer trials.