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PubMed2004

Optimal multivariate matching before randomization.

Although blocking or pairing before randomization is a basic principle of experimental design, the principle is almost invariably applied to at most one or two blocking variables. Here, we discuss the use of optimal multivariate matching prior to randomization to improve covariate balance for many variables at the same time, presenting an algorithm and a case-study of its performance. The method is useful when all subjects, or large groups of subjects, are randomized at the same time. Optimal matching divides a single group of 2n subjects into n pairs to minimize covariate differences within pairs-the so-called nonbipartite matching problem-then one subject in each pair is picked at random for treatment, the other being assigned to control. Using the baseline covariate data for 132 patients from an actual, unmatched, randomized experiment, we construct 66 pairs matching for 14 covariates. We then create 10000 unmatched and 10000 matched randomized experiments by repeatedly randomizing the 132 patients, and compare the covariate balance with and without matching. By every measure, every one of the 14 covariates was substantially better balanced when randomization was performed within matched pairs. Even after covariance adjustment for chance imbalances in the 14 covariates, matched randomizations provided more accurate estimates than unmatched randomizations, the increase in accuracy being equivalent to, on average, a 7% increase in sample size. In randomization tests of no treatment effect, matched randomizations using the signed rank test had substantially higher power than unmatched randomizations using the rank sum test, even when only 2 of 14 covariates were relevant to a simulated response. Unmatched randomizations experienced rare disasters which were consistently avoided by matched randomizations.

Adult↗

PubMed2004

Alternative funding for academic medicine: experience at a Canadian Health Sciences Center.

In 1994 the School of Medicine of Queen's University in Kingston, Ontario, its clinical teachers, and the three principal teaching hospitals initiated a new approach to funding, the Alternative Funding Plan, a pragmatic response to the inability of fee-for-service billing by clinical faculty to subsidize the academic mission of the health sciences center. The center was funded to provide a package of service and academic deliverables (outputs), rather than on the basis of payment for physician clinical activity (inputs). The new plan required a new governance structure representing stakeholders and raised a number of important issues: how to reconcile the preservation of physician professional autonomy with corporate responsibilities; how to gather requisite information so as to equitably allocate resources; and how to report to the Ontario Ministry of Health and Long-term Care in order to demonstrate accountability. In subsequent iterations of the agreement it was necessary to address issues of flexibility resulting from locked-in funding levels and to devise meaningful performance measures for departments and the center as a whole. The authors conclude that the Alternative Funding Plan represents a successful innovation in funding for an academic health sciences center in that it has created financial stability, as well as modest positive effects for education and research. The Ontario government hopes to replicate the model at the province's other four health sciences centers, and it may have applicability in any jurisdiction in which the costs of medical education outstrip the capacity of faculty clinical earnings.

Faculty, Medical↗