Comparative effectiveness research is designed to inform health-care decisions by providing evidence on the effectiveness, benefits, and harms of different treatment options. Evidence is made available via two pathways: a) Assess all available evidence about the benefits and harms of each choice for different groups of people from existing research reviews or systematic reviews of existing evidence; b) Conduct studies generating new evidence of effectiveness or comparative effectiveness of a test, treatment, procedure, or health-care service delivery.

Network meta-analyses (or so-called multiple treatments meta-analysis, or mixed treatment comparisons), which allow competing interventions to be ranked and involve the simultaneous analysis of both direct and indirect comparisons among multiple treatments across multiple studies, usually randomized trials, have been increasingly common for quantitative synthesis of existing evidence in comparative effectiveness research.

Majority of network meta-analyses are carried out with aggregate-level statistical parameters extracted from selected publications. However, since heterogeneity and inconsistency exist across studies, and the number of studies in a network is often limited, adjustment by incorporating study-level covariates with meta-regression models may sometimes be questionable. In addition, aggregate-level covariate adjustment might produce systemic bias, limiting the interpretation of estimated results for subgroups. In contrast, patient-level data network meta-analyses usually have sufficient power to estimate meta-regression models, thereby reducing inconsistency and heterogeneity and providing the opportunity to explore differences in effect among subgroups. Integrated summary of effectiveness and integrated summary of safety studies have been commonly conducted and support FDA submission. A similar process may be adopted for data integrations to support patient-level network meta-analysis.

Market-approved medicinal products have original trial data submitted to FDA. This session hopes to address the following questions: How can these data be available to be re-used for patient-level network meta-analysis to support comparative effectiveness research to inform health care practice? Are current efforts in RCT patient level data transparency able to support network meta-analysis using individual patient-level data? Are there any barriers to prevent re-use of these existing data for this purpose? How can barriers be eliminated? Are these data accessible to an independent research organization via an agreed study proposal/protocol basis, e.g. Patient-Centered Outcomes Research Institute or Agency for Healthcare Research and Quality funding studies, or healthcare insurance organization supporting studies? Can a public-private partnership or collaboration with pharmaceutical companies and/or healthcare payers support patient-level network meta-analyses performance? What are some critical statistical challenges in this space related to imbalances in sample size across studies, type I error control, or multiplicity?