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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing

Jun Park1, Giorgos Bakoyannis2, Ying Zhang3

  • 1Department of Biostatistics, Indiana University, Indianapolis, IN 46202, USA and Merck & Co., Inc., NorthWales, PA 19454, USA.

Biostatistics (Oxford, England)
|January 8, 2021
PubMed
Summary

This study introduces a new statistical method to analyze competing risk data when event times are interval-censored and event types are missing. The augmented inverse probability weighted sieve maximum likelihood estimator handles missing data robustly, improving analysis accuracy.

Keywords:
Augmented inverse probability weightingInterval censoringMissing dataR package

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Competing risk data often involves interval censoring, where event times are not exact but fall within intervals.
  • Missing event types are a common challenge in analyzing such data, complicating accurate risk assessment.
  • Existing methods may struggle with the combined complexities of interval censoring and missing event types.

Purpose of the Study:

  • To develop a robust statistical estimator for interval-censored competing risk data with missing event types.
  • To address limitations of current methods by incorporating auxiliary variables and relaxing missingness assumptions.
  • To provide a reliable tool for analyzing complex health outcome data.

Main Methods:

  • Proposed an augmented inverse probability weighted sieve maximum likelihood estimator.
  • Developed a doubly robust method consistent under misspecification of either missingness or event type models.
  • Incorporated auxiliary variables to improve handling of missing event types under weaker assumptions.

Main Results:

  • The proposed estimator demonstrated good performance in Monte Carlo simulations, even with substantial missing event types.
  • The method proved to be doubly robust, offering consistent results under model misspecification.
  • The approach was successfully illustrated using HIV cohort data from sub-Saharan Africa.

Conclusions:

  • The new estimator effectively analyzes interval-censored competing risk data with missing event types.
  • The doubly robust nature and use of auxiliary variables enhance its applicability in real-world epidemiological studies.
  • The method is accessible through the R package intccr's function ciregic_aipw.