Addressing the issue of channeling bias in observational studies with propensity scores analysis.
Randomized Clinical Trials (RCTs) remain the gold standard for determining the utility of pharmaceuticals especially from a safety and efficacy standpoint. However, restrictive entry criteria and stringent protocols can be barriers to generalizing RCT findings to real world practices and outcomes. Observational studies overcome these limitations of RCTs since they are representative of real world populations and practices. Nonetheless, attributing causality remains a major limitation in observational studies, due to the non-random assignment of subjects to treatment. Non-random assignment can lead to imbalances in risk-factors between the groups being compared and thus bias the estimates of the treatment effect. Non-random assignment can be particularly problematic in observational studies comparing older versus newer pharmaceuticals from similar therapeutic classes due to the phenomenon of channeling. Channeling occurs when drug therapies with similar indications are preferentially prescribed to groups of patients with varying baseline prognoses. In this manuscript we discuss the phenomenon of channeling and the use of a statistical technique known an propensity scores analysis which potentially adjusts for the effects of channeling. During the course of this manuscript we discuss tests for determining the quality of the derived propensity score, various techniques for utilizing propensity scores, and also the potential limitations of this technique. With the increasing availability of high quality pharmaceutical and medical claims data for use in observational studies, increased attention must be given to analytic techniques that adjust optimally for non-random assignment and resulting channeling bias. For research studies using observational study designs, propensity score analysis offers a reasonable solution to address the limitation of non-random assignment, especially when RCTs are too costly, time-consuming or not ethically feasible.
Lobo
Francis S
FS
Health Economics & Outcomes Research Group, East Hanover, NJ 07936, USA. francis.lobo@novartis.com
Wagner
Samuel
S
Gross
Cynthia R
CR
Schommer
Jon C
JC
D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004358:Drug Therapy; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D016896:Treatment Outcome