PCP28 - An Algorithmic Approach To Optimal Study Design for Outcomes Research

Abstract

Research design is complicated by a multitude of factors, including the differing evidentiary needs of health system stakeholders, the differing measures that meet their needs, and the differing methodologic approaches that can be used to collect clinical, economic and real-world data. Health economists have developed algorithms for use in selecting the most appropriate modeling approach, but no such solution exists for selecting the most appropriate real-world research design. The objective of this study was to develop and test an algorithm that addresses this gap. The algorithm consists of a series of structured yes/no questions, as follows: (1) Is the study focused on an intervention? (2) If so, is the intervention on the market? (3) Is the study intended to be comparative? (4) If so, is treatment assigned by study protocol? (5) Are data needed for the study available from existing sources? (6) If so, are those existing sources accessible in computerized form (i.e., in administrative claims or electronic medical records)? And (7) is the study setting real world? As research designs vary dramatically in terms of time and cost requirements, the algorithm steers the researcher to the most cost-effective option first in those instances in which multiple approaches are viable. Responding to each of these yes/no questions within the structure of the algorithm successfully guides the researcher to one of six different research designs: (1) retrospective database analysis; (2) manual chart review; (3) prospective non-interventional study / registry; (4) traditional randomized controlled trial; (5) pragmatic clinical trial; or (6) economic modeling. Algorithms have proven invaluable to guide health economists on optimal selection of modeling approach. This research shows that an algorithmic approach also can facilitate selection of optimal real-world research design based on structured responses to a series of questions regarding the study focus and objectives.