Incremental Methods of Imputation in Longitudinal Clinical Trials
نویسندگان
چکیده
In longitudinal clinical trials, missing data are mostly related to dropouts. Some dropouts appear completely at random. The source for other dropouts is withdrawal from trials due to lack of efficacy. For the latter case, the analyses of the actual observed data and completers can produce bias. One of the approaches to comply with the intent-to-treat principle is the imputation of incomplete data. This paper deals with the incremental methods of imputation applied to incomplete longitudinal data sets with MAR drop-outs. Comparison is done between the incremental methods and some other imputation methods (including the last observation carriedforward method and linear mixed-models method) on simulated longitudinal data. The data sets are simulated to resemble time behavior of the HbA1c and fasting plasma glucose in diabetes clinical trials.
منابع مشابه
چند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
Background and Objectives: A major challenge that affects the longitudinal studies is the problem of missing data. Missing in the data may result in the loss of part of the information which reduces the accuracy of the estimator and obtain the results will be biased and inaccurate. Therefore, it is necessary to evaluate the missing data mechanism from a longitudinal research and to consider thi...
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Missing data are common in clinical trials. In longitudinal studies missing data are mostly related to drop-outs. Some drop-outs appear completely at random. The source for other drop-outs is withdrawal from trials due to lack of efficacy. For the latter case the standard analysis of the actual observed data produces bias. An attractive approach to avoid this problem is to impute (i.e. fill in)...
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