| Titre : | Can trial sequential monitoring boundaries reduce spurious inferences from meta-analyses ? (2009) |
| Auteurs : | Kristian THORLUND ; Bodil ALS-NIELSEN ; P.J. DEVEREAUX ; Lise-Lotte GLUUD ; Christian GLUUD ; Gordon GUYATT ; John-Pa IOANNIDIS ; Lehana THABANE ; Jorn WETTERSLEV ; Copenhagen Trial Unit. Center for Clinical Intervention Research. Copenhagen University Hospital. Copenhagen. DNK ; Faculty of Health Sciences. Department of Clinical Epidemiology and Biostatistics. McMaster University. Clarity. Hamilton. ON. CAN |
| Type de document : | Article |
| Dans : | International journal of epidemiology (vol. 38, n° 1, 2009) |
| Pagination : | 276-286 |
| Langues: | Anglais |
| Mots-clés : | Essai thérapeutique ; Surveillance ; Information |
| Résumé : | [BDSP. Notice produite par INIST-CNRS 8EGoR0xo. Diffusion soumise à autorisation]. Background : Results from apparently conclusive meta-analyses may be false. A limited number of events from a few small trials and the associated random error may be under-recognized sources of spurious findings. The information size (IS, i.e. number of participants) required for a reliable and conclusive meta-analysis should be no less rigorous than the sample size of a single, optimally powered randomized clinical trial. If a meta-analysis is conducted before a sufficient IS is reached, it should be evaluated in a manner that accounts for the increased risk that the result might represent a chance finding (i.e. applying trial sequential monitoring boundaries). Methods : We analysed 33 meta-analyses with a sufficient IS to detect a treatment effect of 15% relative risk reduction (RRR). We successively monitored the results of the meta-analyses by generating interim cumulative meta-analyses after each included trial and evaluated their results using a conventional statistical criterion (alpha=0.05) and two-sided Lan-DeMets monitoring boundaries. We examined the proportion of false positive results and important inaccuracies in estimates of treatment effects that resulted from the two approaches. Results : Using the random-effects model and final data, 12 of the meta-analyses yielded P>alpha=0.05, and 21 yielded P<=alpha=0.05. False positive interim results were observed in 3 out of 12 meta-analyses with P>alpha=0.05. The monitoring boundaries eliminated all false positives. Important inaccuracies in estimates were observed in 6 out of 21 meta-analyses using the conventional P<=alpha=0.05 and 0 out of 21 using the monitoring boundaries. Conclusions : Evaluating statistical inference with trial sequential monitoring boundaries when meta-analyses fall short of a required IS may reduce the risk of false positive results and important inaccurate effect estimates. |
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