Nils Bröckelmann1, Julia Stadelmaier1, Louisa Harms1, Charlotte Kubiak1, Jessica Beyerbach1, Martin Wolkewitz2, Jörg J Meerpohl1,3, Lukas Schwingshackl4. 1. Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 2. Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany. 3. Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany. 4. Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. schwingshackl@ifem.uni-freiburg.de.
Abstract
BACKGROUND: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review. METHODS: BoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs. RESULTS: Overall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%). CONCLUSIONS: Our findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling.
BACKGROUND: Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review. METHODS: BoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs. RESULTS: Overall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%). CONCLUSIONS: Our findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling.
Authors: Ahmed M Abou-Setta; Lauren A Beaupre; Saifee Rashiq; Donna M Dryden; Michele P Hamm; Cheryl A Sadowski; Matthew R G Menon; Sumit R Majumdar; Donna M Wilson; Mohammad Karkhaneh; Shima S Mousavi; Kai Wong; Lisa Tjosvold; C Allyson Jones Journal: Ann Intern Med Date: 2011-08-16 Impact factor: 25.391
Authors: Nils Bröckelmann; Sara Balduzzi; Louisa Harms; Jessica Beyerbach; Maria Petropoulou; Charlotte Kubiak; Martin Wolkewitz; Joerg J Meerpohl; Lukas Schwingshackl Journal: BMC Med Date: 2022-05-11 Impact factor: 11.150
Authors: Carlos A Cuello-Garcia; Nancy Santesso; Rebecca L Morgan; Jos Verbeek; Kris Thayer; Mohammed T Ansari; Joerg Meerpohl; Lukas Schwingshackl; Srinivasa Vittal Katikireddi; Jan L Brozek; Barnaby Reeves; Mohammad H Murad; Maicon Falavigna; Reem Mustafa; Deborah L Regidor; Paul Elias Alexander; Paul Garner; Elie A Akl; Gordon Guyatt; Holger J Schünemann Journal: J Clin Epidemiol Date: 2021-11-17 Impact factor: 6.437