BACKGROUND: Meta-analyses are now widely used to provide evidence to support clinical strategies. However, large randomized, controlled trials are considered the gold standard in evaluating the efficacy of clinical interventions. METHODS: We compared the results of large randomized, controlled trials (involving 1000 patients or more) that were published in four journals (the New England Journal of Medicine, the Lancet, the Annals of Internal Medicine, and the Journal of the American Medical Association) with the results of meta-analyses published earlier on the same topics. Regarding the principal and secondary outcomes, we judged whether the findings of the randomized trials agreed with those of the corresponding meta-analyses, and we determined whether the study results were positive (indicating that treatment improved the outcome) or negative (indicating that the outcome with treatment was the same or worse than without it) at the conventional level of statistical significance (P<0.05). RESULTS: We identified 12 large randomized, controlled trials and 19 meta-analyses addressing the same questions. For a total of 40 primary and secondary outcomes, agreement between the meta-analyses and the large clinical trials was only fair (kappa= 0.35; 95 percent confidence interval, 0.06 to 0.64). The positive predictive value of the meta-analyses was 68 percent, and the negative predictive value 67 percent. However, the difference in point estimates between the randomized trials and the meta-analyses was statistically significant for only 5 of the 40 comparisons (12 percent). Furthermore, in each case of disagreement a statistically significant effect of treatment was found by one method, whereas no statistically significant effect was found by the other. CONCLUSIONS: The outcomes of the 12 large randomized, controlled trials that we studied were not predicted accurately 35 percent of the time by the meta-analyses published previously on the same topics.
BACKGROUND: Meta-analyses are now widely used to provide evidence to support clinical strategies. However, large randomized, controlled trials are considered the gold standard in evaluating the efficacy of clinical interventions. METHODS: We compared the results of large randomized, controlled trials (involving 1000 patients or more) that were published in four journals (the New England Journal of Medicine, the Lancet, the Annals of Internal Medicine, and the Journal of the American Medical Association) with the results of meta-analyses published earlier on the same topics. Regarding the principal and secondary outcomes, we judged whether the findings of the randomized trials agreed with those of the corresponding meta-analyses, and we determined whether the study results were positive (indicating that treatment improved the outcome) or negative (indicating that the outcome with treatment was the same or worse than without it) at the conventional level of statistical significance (P<0.05). RESULTS: We identified 12 large randomized, controlled trials and 19 meta-analyses addressing the same questions. For a total of 40 primary and secondary outcomes, agreement between the meta-analyses and the large clinical trials was only fair (kappa= 0.35; 95 percent confidence interval, 0.06 to 0.64). The positive predictive value of the meta-analyses was 68 percent, and the negative predictive value 67 percent. However, the difference in point estimates between the randomized trials and the meta-analyses was statistically significant for only 5 of the 40 comparisons (12 percent). Furthermore, in each case of disagreement a statistically significant effect of treatment was found by one method, whereas no statistically significant effect was found by the other. CONCLUSIONS: The outcomes of the 12 large randomized, controlled trials that we studied were not predicted accurately 35 percent of the time by the meta-analyses published previously on the same topics.
Authors: F López-Jiménez; M Brito; Y W Aude; P Scheinberg; M Kaplan; D A Dixon; N Schneiderman; J F Trejo; L H López-Salazar; E J Ramírez-Barba; R Kalil; C Ortiz; J Goyos; A Buenaño; S Kottiech; G A Lamas Journal: Arch Med Res Date: 2000 Jul-Aug Impact factor: 2.235
Authors: Zeina A Habib; Leonidas Tzogias; Suzanne L Havstad; Karen Wells; George Divine; David E Lanfear; Jeffrey Tang; Richard Krajenta; Manel Pladevall; L Keoki Williams Journal: Pharmacoepidemiol Drug Saf Date: 2009-06 Impact factor: 2.890