Analysis of longitudinal data with irregular, outcome-dependent follow-up

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 66; no. 3; pp. 791 - 813
• Main Authors: Lin, Haiqun, Scharfstein, Daniel O., Rosenheck, Robert A.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing 01.08.2004; Blackwell Publishers; Blackwell; Royal Statistical Society; Oxford University Press
• Series: Journal of the Royal Statistical Society Series B
• Subjects: Confidence interval; Consistent estimators; Counting process; Data analysis; Data collection; Distribution theory; Drop-out; Estimation; Estimation bias; Estimators; Evaluation; Exact sciences and technology; Factor analysis; Health service evaluation; Health services; Homeless people; Homelessness; Intermittent missingness; Linear inference, regression; Longitudinal data; Longitudinal studies; Mathematics; Maximum likelihood estimation; Modeling; Multivariate analysis; Non-Gaussian data; Parametric models; Probability and statistics; Probability theory and stochastic processes; Regression analysis; Sample size; Sciences and techniques of general use; Semiparametric estimators; Sequential ignorability; Simulation; Statistical methods; Statistics; Studies; Visit process; Weighted generalized estimating equations; Data analysis; Finite sample; Large sample; Response model; Continuous time; Regression analysis; Statistical method; Missing data; Correlation analysis; Follow up study; Experimental design; Marginal distribution; Observation data; Biased estimation
• DOI: 10.1111/j.1467-9868.2004.b5543.x
• ISSN: 1369-7412

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.