Methods for a longitudinal quantitative outcome with a multivariate Gaussian distribution multi‐dimensionally censored by therapeutic intervention‡. (21st November 2013)

• Record Type: Journal Article
• Authors: Sun, Wanjie; Larsen, Michael D.; Lachin, John M.
• Is Part Of: Statistics in medicine. Volume 33:Number 8(2014)
• Page Start: 1288
• Page End: 1306
• Publication Date: 2013-11-21
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6037
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6037-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6037-para-0002">In longitudinal studies, a quantitative outcome (such as blood pressure) may be altered during follow‐up by the administration of a non‐randomized, non‐trial intervention (such as anti‐hypertensive medication) that may seriously bias the study results. Current methods mainly address this issue for cross‐sectional studies. For longitudinal data, the current methods are either restricted to a specific longitudinal data structure or are valid only under special circumstances. We propose two new methods for estimation of covariate effects on the underlying (untreated) general longitudinal outcomes: a single imputation method employing a modified expectation–maximization (EM)‐type algorithm and a multiple imputation (MI) method utilizing a modified Monte Carlo EM‐MI algorithm. Each method can be implemented as one‐step, two‐step, and full‐iteration algorithms. They combine the advantages of the current statistical methods while reducing their restrictive assumptions and generalizing them to realistic scenarios. The proposed methods replace intractable numerical integration of a multi‐dimensionally censored MVN posterior distribution with a simplified, sufficiently accurate approximation. It is particularly attractive when outcomes reach a plateau after intervention due to various reasons. Methods are studied via simulation and applied to data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study of treatment for type 1 diabetes. Methods proved to be robust to high dimensions, large amounts of censored data, low within‐subject correlation, and when subjects receive non‐trial intervention to treat the underlying condition only (with high <italic>Y</italic>), or for treatment in the majority of subjects (with high <italic>Y</italic>) in combination with prevention for a small fraction of subjects (with normal <italic>Y</italic>). Copyright © 2013 John Wiley & Sons, Ltd.</p> </abstract>