Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons
AbstractLongitudinal clinical trials are often plagued by nonmonotone missingness due to both patient dropout and intermittent missingness. Standard analysis assumes that missingness is ignorable. Because the assumption can be questionable, the sensitivity of inferences to alternative assumptions about missingness needs to be evaluated. This need arises in the analysis of a longitudinal prostate cancer quality-of-life (QoL) clinical trial dataset, in which nonmonotone missingness occurs. The choice of the missing data model is studied in the analysis. A local sensitivity analysis method is then applied to analyze the dataset and to investigate the changes in parameter estimates in the neighborhood of the ignorable model. One advantage of the method is that it surmounts computational difficulty and completely avoids evaluating the high-dimensional integrals in the likelihood due to nonmonotone missingness. Another is that it can be implemented using the standard software without excessive additional computation. The method is especially advantageous for large clinical datasets for which alternative approaches can become computationally prohibitive. In addition, the analysis demonstrates the importance of exploiting information on reasons for missingness. When such information is unavailable for some missingness and therefore the missingness types (i.e., dropout versus intermittent missingness) are unknown, a bound analysis is proposed, combined with genetic algorithms, to account for unknown missingness types. The analysis demonstrates the usefulness of the method as a general approach to evaluating the sensitivity of standard analysis to nonignorable nonmonotone missingness in clinical trials.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 5 ()
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Web page: http://www.elsevier.com/locate/csda
Bound analysis; Clinical trial; Genetic algorithm; Missing data; Multinomial logit model; Sensitivity analysis;
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