Statistics in Medicine

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

Journal Article

  • Author(s): Dimitris Mavridis, Georgia Salanti, Toshi A. Furukawa, Andrea Cipriani, Anna Chaimani, Ian R. White
  • Article first published online: 22 Oct 2018
  • DOI: 10.1002/sim.8009
  • Read on Online Library
  • Subscribe to Journal

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.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.