Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching
Abstract
Objective
To demonstrate rolling entry matching (REM), a new statistical method, for comparison group selection in the context of staggered nonuniform participant entry in nonrandomized interventions.
Study Setting
Four Health Care Innovation Award (HCIA) interventions between 2012 and 2016.
Study Design
Center for Medicare and Medicaid Innovation HCIA participants entering these interventions over time were matched with nonparticipants who exhibited a similar pattern of health care use and expenditures during each participant's baseline period.
Data Extraction Methods
Medicare fee‐for‐service claims data were used to identify nonparticipating, fee‐for‐service beneficiaries as a potential comparison group and conduct REM.
Principal Findings
Rolling entry matching achieved conventionally‐accepted levels of balance on observed characteristics between participants and nonparticipants. The method overcame difficulties associated with a small number of intervention entrants.
Conclusions
In nonrandomized interventions, valid inference regarding intervention effects relies on the suitability of the comparison group to act as the counterfactual case for the intervention group. When participants enter over time, comparison group selection is complicated. Rolling entry matching is a possible solution for comparison group selection in rolling entry interventions that is particularly useful with small sample sizes and merits further investigation in a variety of contexts.
Citing Literature
Number of times cited according to CrossRef: 3
- Laine E. Thomas, Siyun Yang, Daniel Wojdyla, Douglas E. Schaubel, Matching with time‐dependent treatments: A review and look forward, Statistics in Medicine, 10.1002/sim.8533, 39, 17, (2350-2370), (2020).
- Brystana G. Kaufman, Courtney H. Van Houtven, Melissa A. Greiner, Bradley G. Hammill, Matthew Harker, David Anderson, Sarah Petry, Janet Bull, Donald H. Taylor, Selection Bias in Observational Studies of Palliative Care: Lessons Learned, Journal of Pain and Symptom Management, 10.1016/j.jpainsymman.2020.09.011, (2020).
- Samuel D. Pimentel, Lauren Vollmer Forrow, Jonathan Gellar, Jiaqi Li, Optimal matching approaches in health policy evaluations under rolling enrolment, Journal of the Royal Statistical Society: Series A (Statistics in Society), 10.1111/rssa.12521, 183, 4, (1411-1435), (2019).




