Last-observation-carried-forward (LOCF) and tests for difference in mean rates of change in controlled repeated measurements designs with dropouts

Last-observation-carried-forward (LOCF) originated as a way of replacing missing measurements for use in classical repeated measurements analysis of variance (ANOVA) where complete data are required. As mathematical statisticians developed newer methods in the attempt to deal effectively with missin...

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Bibliographic Details
Published in:Social science research 2009-06, Vol.38 (2), p.492-503
Main Authors: Overall, John E., Tonidandel, Scott, Starbuck, Robert R.
Format: Article
Language:English
Subjects:
Data analysis
Data Quality
Dropouts
Educational psychology
Endpoint analysis
Error of Measurement
GLMM
Last-observation-carried-forward
Linear models
LOCF
Measurement
Methodology (Data Analysis)
Missing data
Mixed models
Modelling
Power
Qualitative analysis
Repeated measures
School dropouts
Simulation
Statistical methods
Type 1 error
Validity
Variance
Variance (Statistics)
Variance analysis
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Summary:Last-observation-carried-forward (LOCF) originated as a way of replacing missing measurements for use in classical repeated measurements analysis of variance (ANOVA) where complete data are required. As mathematical statisticians developed newer methods in the attempt to deal effectively with missing data, LOCF fell largely by the wayside. This paper reports use of simulation methods to evaluate validity of tests of significance for differences in mean rates of change applied to LOCF-completed data and compares results with those from alternative procedures that do not require replacement of the missing values for dropouts. Across several different data structures and dropout mechanisms, tests of significance for difference in means of slope coefficients fitted to LOCF data maintained appropriate Type 1 error protection. In no case where the Type 1 error rates of more complex generalized linear mixed model (GLMM) tests appeared appropriately conservative did the power of those tests exceed power of the simpler test on LOCF-completed data.
ISSN:0049-089X
1096-0317
DOI:10.1016/j.ssresearch.2009.01.004