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A joint modeling approach for multivariate survival data with random length.

Shuling Liu1, Amita K Manatunga1, Limin Peng1

  • 1Department of Biotatistics and Bioinformatics, Emory University, Atlanta, Georgia, U.S.A.

Biometrics
|October 6, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel copula-based joint model for correlated biomedical data, relaxing normality assumptions. The method effectively analyzes repeated measurements and random lengths, demonstrated in a fertility study.

Keywords:
Approximate EM algorithmClayton-Oakes modelJoint modelsMenstrual cycle lengthRandom length dataSemi-parametric transformation modelTime-to-pregnancy

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Area of Science:

  • Biostatistics
  • Reproductive Health Research
  • Statistical Modeling

Background:

  • Correlated data with repeated measurements and variable numbers of observations (multivariate random length data) are common in biomedical studies.
  • Existing methods often assume multivariate normality for measurements, limiting their applicability.
  • Handling both correlated outcomes and the random number of observations simultaneously is a statistical challenge.

Purpose of the Study:

  • To propose a new copula-based joint model for multivariate random length data that does not require the normality assumption.
  • To flexibly model marginal distributions using semi-parametric transformation models.
  • To apply the novel method to analyze fertility data from a prospective cohort study.

Main Methods:

  • Utilized a Clayton-Oakes copula model for the correlated multiple measurements.
  • Specified marginal distributions using semi-parametric transformation models.
  • Modeled the random length using a generalized linear model.
  • Developed an approximate Expectation-Maximization (EM) algorithm for parameter estimation.
  • Employed bootstrapping for standard error estimation.
  • Evaluated finite-sample performance via simulation studies.

Main Results:

  • The proposed copula-based joint model successfully accommodates non-normal correlated data.
  • The method demonstrated robust performance in simulation studies.
  • Application to the Mount Sinai Study of Women Office Workers (MSSWOW) provided insights into fertility patterns.

Conclusions:

  • The novel copula-based joint model offers a flexible and effective approach for analyzing multivariate random length data in biomedical research.
  • This method relaxes restrictive normality assumptions, broadening the scope of applicable statistical techniques.
  • The approach is valuable for studies involving repeated measures and variable observation counts, such as fertility research.