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Prospective individual matching: covariate balance and power in a comparative study

✍ Scribed by Robert W. Makuch; Zhongxin Zhang; Peter A. Charpentier; Sharon K. Inouye


Book ID
101239058
Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
99 KB
Volume
17
Category
Article
ISSN
0277-6715

No coin nor oath required. For personal study only.

✦ Synopsis


In phase II to phase IV studies, randomization has gained widespread acceptance as a methodologic tool for the allocation of patients to treatment. However, randomization is not always feasible. At times, the treatment intervention occurs universally throughout one or more units (for example, a hospital unit), while the control therapy is the only intervention provided in other units. Patients may arrive randomly at a unit, based solely on availability of the unit to accept new subjects. Thus, the treatment assignment process is out of the investigator's control and not subject to selection bias. We describe a prospective individual matching procedure through which one can achieve balanced allocation of subjects to treatment groups in this comparative study setting. In this paper, we compare balance of baseline covariates and power for this design, in which the subject is selected at random and assigned to a treatment group, and the traditional randomized block design, in which the treatment is chosen at random and assigned to a subject. We show that the prospective individual matching procedure compares favourably to the traditional randomized blocked design with respect to both baseline covariate comparability and statistical power.


πŸ“œ SIMILAR VOLUMES


A matching method for improving covariat
✍ Jasjeet Singh Sekhon; Richard D. Grieve πŸ“‚ Article πŸ“… 2011 πŸ› John Wiley and Sons 🌐 English βš– 449 KB

## SUMMARY In cost‐effectiveness analyses (CEA) that use randomized controlled trials (RCTs), covariates of prognostic importance may be imbalanced and warrant adjustment. In CEA that use non‐randomized studies (NRS), the selection on observables assumption must hold for regression and matching met