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Appendix D: Analysis and Interpretation of Studies with Missing Data
Pages 185-194

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From page 185...
... In a review of studies by Khan (2001a,b) , dropout rates in ­ trials of antidepressants averaged 37 percent, similar between treatment and placebo, and were in the 50–60 percent range for trials of antipsychotics, somewhat greater on treatment than on placebo, and intermediate among active controls.
From page 186...
... Very few of the ­ studies e ­ xamined here obtained outcome information after a patient stopped treatment or during post-treatment follow-up. Because a very high percentage of ­patients, from 20 percent to 50 percent, typically dropped out of these studies, large fractions of outcome data were therefore missing.
From page 187...
... However, complete case analysis is inefficient in that it does not make use of the interim information from subjects without final outcome data. I ­ nterestingly, even in this situation where completers represent a completely random representative sample, LOCF is generally biased, because of its assumption that disease severity remains unchanged from its last recorded value (Molenberghs, 2004)
From page 188...
... Although dropout is not completely random in the simplest sense, if dropout depends only on treatment, and treatment is included in the analytic model, the mechanism giving rise to the dropout would be MCAR. Issues with Last Observation Carried Forward Approaches to Missing Data We will focus here on the problems created by using the LOCF ­approach to handling missing data, which is the most widely used approach in the literature reviewed.
From page 189...
... . These three factors -- false certainty about the missing outcome, ­ignoring relevant information about the missing outcome, and assuming that dropout itself is not related to outcome -- conspire to make LOCF a misleading statistical approach to handling missing data.
From page 190...
... It is difficult to quantify in a simple manner the relationship between dropout rate and the degree of bias introduced by LOCF, since that bias depends on a number of things besides the dropout rate: the clinical course of untreated patients over time, the time course of the therapeutic effect, the relationship between the interim measurement and the final measurement, and the nature of the outcome measurement (e.g., percentage of "success" versus disease severity)
From page 191...
... Interim, increasing benefit. Dropouts: Identical benefit LOCF bias: 0–33% understated benefit Completers 75 –5 –15 0 Dropouts 90 –5 –15 0 Scenario 5 Completers: Steadily increasing benefit, with equal natural improvement Dropouts: Identical to completers LOCF bias: 0–25% understated benefit Completers 75 –5 –10 –5 Dropouts 75 –5 –10 –5 Scenario 6 Completers: Early large benefit, sustained Dropouts: No effect, some early benefit LOCF bias: 0–33% overstated benefit Completers 75 –15 –15 0 Dropouts 75 –5 0 0 NOTE: True underlying patterns for completers and non-completers are listed.
From page 192...
... , and statistics text books (Little and Rubin, 2002; Molenberghs and Kenward, 2007; Verbeke and Molenherghs, 2000) have all recommended that analyses of longitudinal clinical trial data move away from simple methods such as LOCF or observed-case analysis to more principled approaches, such as multiple imputation or the likelihood-based family in which MMRM resides.
From page 193...
... 2001b. Symptom reduction and suicide risk among patients treated with placebo in antipsychotic clinical trials: An analysis of the Food and Drug Administration database.


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