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Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any particular observational study. In this work, we propose TrialVerify, a new approach for evaluating observational study methods based on ground truth sourced from clinical trial reports. We process trial reports into a denoised collection of known causal relationships that can then be used to estimate the precision and recall of various observational study methods. We then use TrialVerify to evaluate multiple observational study methods in terms of their ability to identify the known causal relationships from a large national insurance claims dataset. We found that inverse propensity score weighting is an effective approach for accurately reproducing known causal relationships and outperforms other observational study methods. TrialVerify is made freely available for others to evaluate observational study methods.
Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of combinatio
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which
Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the trial, th
In October 2014, the US Drug Enforcement Agency (DEA) reclassified hydrocodone from Schedule III to Schedule II of the Controlled Substances Act, resulting in a prohibition on refills in the initial prescription. While this schedule change was associ
A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Muller (2017, Biometrics, 1355-1365) to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizin