Abstract
Background
Controlled clinical trials of health care interventions are either explanatory or pragmatic. Explanatory trials test whether an intervention is efficacious; that is, whether it can have a beneficial effect in an ideal situation. Pragmatic trials measure effectiveness; they measure the degree of beneficial effect in real clinical practice. In pragmatic trials, a balance between external validity (generalizability of the results) and internal validity (reliability or accuracy of the results) needs to be achieved. The explanatory trial seeks to maximize the internal validity by assuring rigorous control of all variables other than the intervention. The pragmatic trial seeks to maximize external validity to ensure that the results can be generalized. However the danger of pragmatic trials is that internal validity may be overly compromised in the effort to ensure generalizability. We are conducting two pragmatic randomized controlled trials on interventions in the management of hypertension in primary care. We describe the design of the trials and the steps taken to deal with the competing demands of external and internal validity.Discussion
External validity is maximized by having few exclusion criteria and by allowing flexibility in the interpretation of the intervention and in management decisions. Internal validity is maximized by decreasing contamination bias through cluster randomization, and decreasing observer and assessment bias, in these non-blinded trials, through baseline data collection prior to randomization, automating the outcomes assessment with 24 hour ambulatory blood pressure monitors, and blinding the data analysis.Summary
Clinical trials conducted in community practices present investigators with difficult methodological choices related to maintaining a balance between internal validity (reliability of the results) and external validity (generalizability). The attempt to achieve methodological purity can result in clinically meaningless results, while attempting to achieve full generalizability can result in invalid and unreliable results. Achieving a creative tension between the two is crucial.