Randomization—There Is No Substitute

Randomized clinical trials are the gold standard for evaluating the efficacy of therapies or comparing one therapy with another.1Whenapplied inadequatelypowered trials, randomization eliminates selection and other forms of bias, generates groups under study that are alike in all important aspects (except for the intervention received), andavoids confounding by measured and unmeasured confounding variables. Bydesign, randomized clinical trials havehigh internal validity, with the ability to determine cause-effect relationships.Randomizedcontrolled clinical trials are considered the highest level of evidence for guidelinegenerationand the fundamental basis of most regulatory agency approvals for drug therapies.1 However, by virtue of their inclusion and exclusion criteria and participation by volunteers providing informed consent, randomized clinical trials generally include patients who are adherent to therapy, monitoring, and follow-up andmay not be entirely representative of the general patient population.2,3 It is also well recognized that higherrisk patients, older patients, women, and patients in racial/ ethnic minority groups are underrepresented in randomized clinical trials.2,3 Thesedifferencesmay influencewhether the efficacy and safetydemonstrated in randomized clinical trials will completely translate into clinical effectiveness and safety when the same therapy is applied in clinical practice (generalizablility and external validity).1-3 There has been substantial and growing interest in using observational data for comparing the outcomes and safety of different therapies inactual clinical practice and inbroader cohorts of patients.4,5 Because treatment is not assigned at random, these comparisons are subject tovariousbiases andconfounding. Unobserved treatment selection bias may lead to prognostically important baseline differences among patients. Unmeasured variables may affect treatment selection decisions, as well as outcomes. There have been a variety of different statistical approaches developed to account for differences in patient baseline characteristics that are of prognostic importance and to address treatment selection bias in observational studies.6-10 These approaches include multivariable riskmodels, propensity score risk adjustment (eg, inverse probability of treatment weighting), propensitymatching, and instrumental variable analysis. Propensity score adjustmentandpropensitymatchingmayhelpcontrol forovert bias in treatment selection.6,7 While initially suggested that these propensity methods could replicate clinical trial randomization, it has been increasingly recognized that this form of analysis is still subject to hidden bias and may be no more likely to remove bias due to unmeasured confounding when treatmentselectionbiasexists.8-10 Instrumentalvariableanalysis has beenmore recently proposed as amethod designed to control not only for overt bias but also for hidden bias.8-10 An ideal instrumental variable needs to be highly correlated with treatment and does not independently affect the outcomes of interest (except through a treatment effect), such that it is not otherwise associated with measured or unmeasured patient health status. Authors of studies9,10 using instrumental variable analysis have claimed to have generated findings that have been consistent with the results in randomized clinical trials and thus had greater external validity compared with other observational statistical methodological approaches. Related article page 655 Editorial Opinion