Analysis of covariance with incomplete data via semiparametric model transformations

Matteo Grigoletto, Michael G. Akritas

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

We propose a method for fitting semiparametric models such as the proportional hazards (PH), additive risks (AR), and proportional odds (PO) models. Each of these semiparametric models implies that some transformation of the conditional cumulative hazard function (at each t) depends linearly on the covariates. The proposed method is based on nonparametric estimation of the conditional cumulative hazard function, forming a weighted average over a range of t-values, and subsequent use of least squares to estimate the parameters suggested by each model. An approximation to the optimal weight function is given. This allows semiparametric models to be fitted even in incomplete data cases where the partial likelihood fails (e.g,, left censoring, right truncation). However, the main advantage of this method rests in the fact that neither the interpretation of the parameters nor the validity of the analysis depend on the appropriateness of the PH or any the other semiparametric models. In fact, we propose an integrated method for data analysis where the role of the various semiparametric models is to suggest the best fitting transformation. A single continuous covariate and several categorical covariates (factors) are allowed. Simulation studies indicate that the test statistics and confidence intervals have good small- sample performance. A real data set is analyzed.

Original languageEnglish (US)
Pages (from-to)1177-1187
Number of pages11
JournalBiometrics
Volume55
Issue number4
DOIs
StatePublished - Jan 1 1999

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Analysis of Covariance
Incomplete Data
Semiparametric Model
Model Transformation
Data Model
Cumulative Hazard Function
Covariates
Proportional Hazards
Hazards
Left Censoring
Proportional Odds Model
Partial Likelihood
Least-Squares Analysis
Weighted Average
Nonparametric Estimation
Small Sample
Truncation
Categorical
Weight Function
Test Statistic

All Science Journal Classification (ASJC) codes

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

Cite this

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Analysis of covariance with incomplete data via semiparametric model transformations. / Grigoletto, Matteo; Akritas, Michael G.

In: Biometrics, Vol. 55, No. 4, 01.01.1999, p. 1177-1187.

Research output: Contribution to journalArticle

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