A joint modeling approach for multivariate survival data with random length. Issue 2 (4th October 2016)

• Record Type: Journal Article
• Authors: Liu, Shuling; Manatunga, Amita K.; Peng, Limin; Marcus, Michele
• Is Part Of: Biometrics. Volume 73:Issue 2(2017)
• Page Start: 666
• Page End: 677
• Publication Date: 2016-10-04
• Subjects: Approximate EM algorithm -- Clayton–Oakes model -- Joint models -- Menstrual cycle length -- Random length data -- Semi‐parametric transformation model -- Time‐to‐pregnancy; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12588
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: In many biomedical studies that involve correlated data, an outcome is often repeatedly measured for each individual subject along with the number of these measurements, which is also treated as an observed outcome. This type of data has been referred as multivariate random length data by Barnhart and Sampson (1995). A common approach to handling such type of data is to jointly model the multiple measurements and the random length. In previous literature, a key assumption is the multivariate normality for the multiple measurements. Motivated by a reproductive study, we propose a new copula‐based joint model which relaxes the normality assumption. Specifically, we adopt the Clayton–Oakes model for multiple measurements with flexible marginal distributions specified as semi‐parametric transformation models. The random length is modeled via a generalized linear model. We develop an approximate EM algorithm to derive parameter estimators and standard errors of the estimators are obtained through bootstrapping procedures and the finite‐sample performance of the proposed method is investigated using simulation studies. We apply our method to the Mount Sinai Study of Women Office Workers (MSSWOW), where women were prospectively followed for 1 year for studying fertility.