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Concerns over reproducibility in science extend to research using existinghealthcare data; many observational studies investigating the same topicproduce conflicting results, even when using the same data. To address thisproblem, we propose a paradigm shift. The current paradigm centers ongenerating one estimate at a time using a unique study design with unknownreliability and publishing (or not) one estimate at a time. The new paradigmadvocates for high-throughput observational studies using consistent andstandardized methods, allowing evaluation, calibration, and unbiaseddissemination to generate a more reliable and complete evidence base. Wedemonstrate this new paradigm by comparing all depression treatments for a setof outcomes, producing 17,718 hazard ratios, each using methodology on par withstate-of-the-art studies. We furthermore include control hypotheses to evaluateand calibrate our evidence generation process. Results show good transitivityand consistency between databases, and agree with four out of the five findingsfrom clinical trials. The distribution of effect size estimates reported inliterature reveals an absence of small or null effects, with a sharp cutoff atp = 0.05. No such phenomena were observed in our results, suggesting morecomplete and more reliable evidence.

A systematic approach to improving the reliability and scale of evidence from health care data