Analysis of covariance with incomplete data via semiparametric model transformations

Biometrics
M Grigoletto, M G Akritas

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 of 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. Simul...Continue Reading

References

Apr 1, 1983·Statistics in Medicine·S Bennett

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Citations

Apr 1, 2009·Journal of Statistical Planning and Inference·Rajeshwari Sundaram
Oct 1, 2013·Journal of Gastrointestinal and Liver Diseases : JGLD·Giovanni MaconiGianluca Sampietro

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