Journal of comparative effectiveness research
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Propensity score models are increasingly used in observational comparative effectiveness studies to reduce confounding by covariates that are associated with both a study outcome and treatment choice. Any such potentially confounding covariate will bias estimation of the effect of treatment on the outcome, unless the distribution of that covariate is well-balanced between treatment and control groups. ⋯ If, during study design, investigators assemble a comprehensive inventory of known and suspected potentially confounding covariates, examination of how well this inventory is covered by the chosen dataset yields an assessment of the extent of bias reduction that is possible by matching on estimated propensity scores. These considerations are explored by examining the designs of three recently published comparative effectiveness studies.
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Comparative effectiveness research (CER) includes pragmatic clinical trials (PCTs) to address 'real-world' effectiveness. CER interest would be expected to stimulate biopharmaceutical manufacturer PCT investment; however, this does not seem to be the case. In this article we identify all industry-sponsored PCT studies from 1996 to 2010; analyze them across a variety of characteristics, including sponsor, research question, design, comparators and results; and suggest methodological and policy changes to spur future manufacturer PCT investment. ⋯ Of seven that evaluated utilization or costs, six reported no differences and four of five studies comparing brand-generic drugs reported no difference. Whereas private investment in PCTs is in the public interest, manufacturers apparently have not yet seen the business case. To induce investment, we propose several methodological and regulatory policy innovations designed to reduce business risk by decreasing outcome variability and increasing trial efficiency, flexibility and market applicability.
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This article aims to illustrate and critically analyze the results from the 1-year experience of using health technology assessment (HTA) in the development of the Thai Universal Coverage health benefit package. We review the relevant documents and give a descriptive analysis of outcomes resulting from the development process in 2009-2010. Out of 30 topics nominated by stakeholders for prioritization, 12 were selected for further assessment. ⋯ Different stakeholders have diverse interests and capabilities to participate in the process. In conclusion, HTA is helpful for informing coverage decisions for health benefit packages because it enhances the legitimacy of policy decisions by increasing the transparency, inclusiveness and accountability of the process. There is room for improvement of the current use of HTA, including providing technical support for patient representatives and civic groups, better communication between health professionals, and focusing more on health promotion and disease prevention.