The paired availability design for historical controls.
BACKGROUND
Although a randomized trial represents the most rigorous method of evaluating a medical intervention, some interventions would be extremely difficult to evaluate using this study design. One alternative, an observational cohort study, can give biased results if it is not possible to adjust for all relevant risk factors.


METHODS
A recently developed and less well-known alternative is the paired availability design for historical controls. The paired availability design requires at least 10 hospitals or medical centers in which there is a change in the availability of the medical intervention. The statistical analysis involves a weighted average of a simple "before" versus "after" comparison from each hospital or medical center that adjusts for the change in availability.


RESULTS
We expanded requirements for the paired availability design to yield valid inference. (1) The hospitals or medical centers serve a stable population. (2) Other aspects of patient management remain constant over time. (3) Criteria for outcome evaluation are constant over time. (4) Patient preferences for the medical intervention are constant over time. (5) For hospitals where the intervention was available in the "before" group, a change in availability in the "after group" does not change the effect of the intervention on outcome.


CONCLUSIONS
The paired availability design has promise for evaluating medical versus surgical interventions, in which it is difficult to recruit patients to a randomized trial.
Baker
S G
SG
Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA. sb16i@nih.gov
Lindeman
K S
KS
Kramer
B S
BS
D002986:Clinical Trials as Topic / Q000379:methods / Q000639:trends; D005069:Evaluation Studies as Topic; D006297:Health Services Accessibility / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data / Q000639:trends; D011336:Probability; D012107:Research Design / Q000706:statistics & numerical data; D012189:Retrospective Studies; D016896:Treatment Outcome