Allowing for uncertainty due to missing and LOCF imputed outcomes in meta ‐analysis

The use of thelast 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 ou tcome 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 pa rameter. 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 me ta‐analyses of pharmacological interventions for depression.
Source: Statistics in Medicine - Category: Statistics Authors: Tags: RESEARCH ARTICLE Source Type: research