A class of partially linear transformation models for recurrent gap times

ABSTRACT

In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.

KEYWORDS:

• Estimating equations
• Gap times
• Local polynomials
• Partially linear transformation models
• Recurrent events.

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62N02
• Secondary 62G02, 62N01.

Funding

This research was partly supported by the National Natural Science Foundation of China (Grant Nos. 11601307, 11231010 and 11690015), Key Laboratory of RCSDS, CAS (No. 2008DP173182), BCMIIS, and IRTSHUFE.