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

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 fact...