Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey
- PMID: 26858277
- PMCID: PMC4772787
- DOI: 10.1136/bmj.i493
Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey
Erratum in
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Correction notice to paper "Agreement of treatment effects for mortality from routinely collected data and subsequent randomized trials: meta-epidemiological survey".BMJ. 2018 Aug 17;362:k3210. doi: 10.1136/bmj.k3210. BMJ. 2018. PMID: 30120145 No abstract available.
Abstract
Objective: To assess differences in estimated treatment effects for mortality between observational studies with routinely collected health data (RCD; that are published before trials are available) and subsequent evidence from randomized controlled trials on the same clinical question.
Design: Meta-epidemiological survey.
Data sources: PubMed searched up to November 2014.
Methods: Eligible RCD studies were published up to 2010 that used propensity scores to address confounding bias and reported comparative effects of interventions for mortality. The analysis included only RCD studies conducted before any trial was published on the same topic. The direction of treatment effects, confidence intervals, and effect sizes (odds ratios) were compared between RCD studies and randomized controlled trials. The relative odds ratio (that is, the summary odds ratio of trial(s) divided by the RCD study estimate) and the summary relative odds ratio were calculated across all pairs of RCD studies and trials. A summary relative odds ratio greater than one indicates that RCD studies gave more favorable mortality results.
Results: The evaluation included 16 eligible RCD studies, and 36 subsequent published randomized controlled trials investigating the same clinical questions (with 17,275 patients and 835 deaths). Trials were published a median of three years after the corresponding RCD study. For five (31%) of the 16 clinical questions, the direction of treatment effects differed between RCD studies and trials. Confidence intervals in nine (56%) RCD studies did not include the RCT effect estimate. Overall, RCD studies showed significantly more favorable mortality estimates by 31% than subsequent trials (summary relative odds ratio 1.31 (95% confidence interval 1.03 to 1.65; I(2)=0%)).
Conclusions: Studies of routinely collected health data could give different answers from subsequent randomized controlled trials on the same clinical questions, and may substantially overestimate treatment effects. Caution is needed to prevent misguided clinical decision making.
Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Conflict of interest statement
Competing interests: All authors have completed the ICMJE uniform disclosure form at
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Comment in
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Authors' reply to Pérol and colleagues.BMJ. 2016 Dec 16;355:i6747. doi: 10.1136/bmj.i6747. BMJ. 2016. PMID: 27986652 No abstract available.
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Routinely collected data may usefully supplement randomised controlled data on treatment effects for mortality.BMJ. 2016 Dec 16;355:i6745. doi: 10.1136/bmj.i6745. BMJ. 2016. PMID: 27986716 No abstract available.
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References
-
- Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984;79:516-24. 10.1080/01621459.1984.10478078. . - DOI
-
- Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR good research practices for retrospective database analysis task force report—Part III. Value Health 2009;12:1062-73. 10.1111/j.1524-4733.2009.00602.x. .19793071. - DOI - PubMed
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