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Abstract
The use of the last observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta‐analyses often include several studies reporting their results according to LOCF. The results from such meta‐analyses are potentially biased and overprecise. We develop methods for estimating summary treatment effects for continuous outcomes in the presence of both missing and LOCF‐imputed outcome data. Our target is the treatment effect if complete follow‐up was obtained even if some participants drop out from the protocol treatment. We extend a previously developed meta‐analysis model, which accounts for the uncertainty due to missing outcome data via an informative missingness parameter. The extended model includes an extra parameter that reflects the level of prior confidence in the appropriateness of the LOCF imputation scheme. Neither parameter can be informed by the data and we resort to expert opinion and sensitivity analysis. We illustrate the methodology using two meta‐analyses of pharmacological interventions for depression.
Highlights
Missing data in clinical trials pervade all fields of medicine and may compromise the validity of inferences even from well-designed randomized controlled trials.[1]
Missing data have not been handled properly in most trials, potentially leading to biased and overprecise results. These problems are propagated in a synthesis of trials through a meta-analysis, and we run the risk of finding a false-positive result because of the inflated sample sizes within trials
The last observation carried forward (LOCF) method has been typically requested by regulatory agencies on the grounds that it is a conservative method, but this is mistaken and recommendations have been against its use.[1,16,17,18]
Summary
Missing data in clinical trials pervade all fields of medicine and may compromise the validity of inferences even from well-designed randomized controlled trials.[1] Trials usually follow patients over time and take measurements at several time points. Our target is the treatment effect if complete follow-up was obtained, even if some participants discontinue the protocol treatment. Discontinuing treatment is indicative of how effective and acceptable a treatment is, but ideally, the target in a randomized controlled trial is to take measurements and calculate an effect size at the end of the trial in order to abide by the intention-to-treat principle. A standard methodology in many clinical fields for imputing incomplete longitudinal data sets is the last observation carried forward (LOCF) method: The missing outcome is replaced by the last observed value. Missing data are evident in mental health trials where dropout rates may exceed 50%2 and the LOCF method is commonly applied.[3]
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