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Methods for multivariate recurrent event data with measurement error and informative censoring.

Hsiang Yu1, Yu-Jen Cheng1, Ching-Yun Wang2

  • 1Institute of Statistics, National Tsing-Hua University, Hsin-Chu 300, Taiwan.

Biometrics
|February 15, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods to accurately analyze recurrent event data, correcting for informative censoring and measurement errors. These approaches enhance the reliability of findings in complex health studies, like cancer recurrence.

Keywords:
Generalized method of momentsInformative censoringInstrumental variableMeasurement errorMultivariate recurrent event dataSurrogate covariate

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Recurrent event data analysis is often complicated by informative censoring and covariate measurement errors.
  • Existing methods may not adequately address these dual challenges in multivariate settings.
  • Accurate statistical modeling is crucial for understanding disease progression and treatment effects.

Purpose of the Study:

  • To develop and evaluate non-parametric methods for simultaneously correcting informative censoring and measurement errors in multivariate recurrent event data.
  • To provide robust analytical tools for observational studies with complex data structures.
  • To assess the impact of selenium on cancer recurrence using these novel methods.

Main Methods:

  • Two non-parametric correction approaches were developed: one using a calibration sample and another employing the generalized method of moments.
  • A shared frailty model was utilized to handle informative censoring and dependencies between event types.
  • The methods avoid restrictive assumptions on event processes, frailty distributions, and covariate/measurement error distributions.

Main Results:

  • Both proposed methods demonstrated good performance in correcting for informative censoring and measurement errors.
  • The generalized method of moments approach showed improved statistical efficiency.
  • Application to the Nutritional Prevention of Cancer trial provided insights into selenium's effect on specific cancer recurrences.

Conclusions:

  • The developed non-parametric methods offer effective solutions for analyzing multivariate recurrent event data with informative censoring and measurement errors.
  • These methods provide a flexible and robust framework applicable to various epidemiological and clinical studies.
  • The findings highlight the utility of these techniques in real-world data analysis, such as evaluating cancer prevention strategies.