Authors: Haiqun Lin¹; Daniel O. Scharfstein²; Robert A. Rosenheck³

Source: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 66, Number 3, August 2004 , pp. 791-813(23)

Publisher: Wiley-Blackwell

Abstract:

Summary.

A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow-up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates. Building on the work of Robins, Rotnitzky and Zhao, we propose a class of inverse intensity-of-visit process-weighted estimators in marginal regression models for longitudinal responses that may be observed in continuous time. This allows us to handle arbitrary patterns of missing data as embedded in a subject's visit process. We derive the large sample distribution for our inverse visit-intensity-weighted estimators and investigate their finite sample behaviour by simulation. Our approach is illustrated with a data set from a health services research study in which homeless people with mental illness were randomized to three different treatments and measures of homelessness (as percentage days homeless in the past 3 months) and other auxiliary factors were recorded at follow-up times that are not fixed by design.

Keywords: Counting process; Drop-out; Health service evaluation; Intermittent missingness; Longitudinal data; Non-Gaussian data; Semiparametric estimators; Sequential ignorability; Visit process; Weighted generalized estimating equations

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2004.b5543.x

Affiliations: 1: Yale University, New Haven, USA 2: Johns Hopkins Bloomberg School of Public Health, Baltimore, USA 3: Veterans Affairs Northeast Program Evaluation Center and Yale University, West Haven, USA

Publication date: 2004-08-01

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