Maximum likelihood estimation of semiparametric mixture component models for competing risks data

Sangbum Choi, Xuelin Huang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma.

Original languageEnglish
Pages (from-to)588-598
Number of pages11
JournalBiometrics
Volume70
Issue number3
DOIs
Publication statusPublished - 2014 Sep 1
Externally publishedYes

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Competing Risks
Maximum likelihood estimation
Component Model
Mixture Model
Maximum Likelihood Estimation
Cumulative Incidence Function
Proportional Odds Model
Observed Information
Time-dependent Covariates
Semiparametric Regression
Multinomial Model
Mixture Modeling
Follicular Lymphoma
Information Matrix
Proportional Hazards Model
Logistic Model
Semiparametric Model
Incidence
Statistical Inference
Asymptotic Normality

Keywords

  • Cumulative incidence
  • Cure model
  • Joint model
  • Martingale
  • Nonparametric likelihood
  • Transformation model

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Maximum likelihood estimation of semiparametric mixture component models for competing risks data. / Choi, Sangbum; Huang, Xuelin.

In: Biometrics, Vol. 70, No. 3, 01.09.2014, p. 588-598.

Research output: Contribution to journalArticle

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