An Algorithm for Prospective Individual Matching in a Non-Randomized Clinical Trial

Abstract

A method is described to achieve balance across prognostic factors in intervention trials for which randomized allocation to treatment group is not possible. The method involves prospective individual matching of patients that have already been assigned to treatment groups. Data can be analyzed using methods appropriate for prospective matched cohort studies. Successful implementation depends on the number and complexity of factors to be matched, and on the number of available control patients. Simulation studies suggest that, in order to yield satisfactory match rates and to reduce costs associated with screening unmatched controls, no more than three prognostic factors should generally be considered. Baseline prognostic indices, incorporating information from multiple variables, provide effective matching factors. The implementation of the method in a successful clinical trial, the Delirium Prevention Trial, is discussed. In that study, treatment group was determined by hospital admission to either an intervention floor or to one of two usual care hospital floors. The ratio of available control to intervention patients was 1.3, and 95% of the eligible intervention floor patients were successfully matched to control floor patients. Excellent balance was demonstrated for non-matching factors, due in part to the use of a composite baseline risk score as a matching factor. In addition, external validity is enhanced because most eligible intervention patients are enrolled as they present. The methods outlined in this report provide a methodologically rigorous alternative for achieving balance across treatment groups, with respect to important prognostic factors, in non-randomized clinical trials, and will have broad applicability in the numerous situations in which randomization is not possible.