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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9030

Title: Assessing and interpreting treatment effects in longitudinal clinical trials with missing data
Authors: Mallinckrodt, Craig H.
Sanger, Todd M.
Dubé, Sanjay
DeBrota, David J.
Molenberghs, Geert
Carroll, Raymond J.
Potter, William Z.
Tollefson, Gary D.
Issue Date: 2003
Citation: BIOLOGICAL PSYCHIATRY, 53(8), p. 754-760.
Series/Report: Biological Psychiatry
Abstract: Treatment effects are often evaluated by comparing change over time in outcome measures; however, valid analyses of longitudinal data can be problematic, particularly if some data are missing. For decades, the last observation carried forward (LOCF) approach has been a common method of handling missing data. Considerable advances in statistical methodology and our ability to implement those methods have been made in recent years. Thus, it is appropriate to reconsider analytic approaches for longitudinal data. This review examines the following from a clinical perspective: I) the characteristics of missing data that influence analytic choices; 2) the attributes of common methods of handling missing data; and 3) the use of the data characteristics and the attributes of the various methods, along with empirical evidence, to develop a robust approach for the analysis and interpretation of data from longitudinal clinical trials. We propose that, in many settings, the primary efficacy analysis should use a repeated measures, likelihood-based, mixed-effects modeling approach, with LOCF used as a secondary, composite measure of efficacy, safety, and tolerability. We illustrate how repeated-measures analyses can be used to enhance decision-making, and we review the caveats that remain regarding the use of LOCF as a composite measure.
Notes: Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA. Univ Pittsburgh, Western Psychiat Inst & Clin, Pittsburgh, PA 15213 USA. Limburgs Univ Ctr, Ctr Stat, Diepenbeek, Belgium. Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA.
URI: http://hdl.handle.net/1942/9030
Link to publication: https://www.academia.edu/8393353/Assessing_and_interpreting_treatment_effects_in_longitudinal_clinical_trials_with_missing_data
DOI: 10.1016/S0006-3223(03)01867-X
ISI #: 000182360400012
ISSN: 0006-3223
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2004
Appears in Collections: Research publications

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