Background Adjusting for large numbers of covariates ascertained from patients’ health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. Methods We developed a multi-step algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, e.g. diagnoses, procedures, and medications, (2) empirically identifying candidate covariates, (3) assess recurrence of codes, (4) prioritizing covariates, (5) selecting covariates for adjustment, (6) estimating the exposure propensity score, and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared to nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). Results In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91–1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94[0.78–1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88[0.73–1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02[0.85–1.21]). Conclusion In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.

Many e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.

The current emphasis on comparative effectiveness research will provide practicing physicians with increasing volumes of observational evidence about preventive care. However, numerous highly publicized observational studies of the effect of prevention on health outcomes have reported exaggerated relationships that were later contradicted by randomized controlled trials. A growing body of research has identified sources of bias in observational studies that are related to patient behaviors or underlying patient characteristics, known as the healthy user effect, the healthy adherer effect, confounding by functional status or cognitive impairment, and confounding by selective prescribing. In this manuscript we briefly review observational studies of prevention that have appeared to reach incorrect conclusions. We then describe potential sources of bias in these studies and discuss study designs, analytical methods, and sensitivity analyses that may mitigate bias or increase confidence in the results reported. More careful consideration of these sources of bias and study designs by providers can enhance evidence-based decision-making. P racticing clinicians face a substantial challenge when attempting to interpret data from observational studies that report the effects of prevention on patient health outcomes. Numerous high-profile descriptive studies of preventive screening tests, behaviors, and treatments have reported dramatically reduced mortality or improved health outcomes. However, many of these findings were later thrown into question when randomized controlled trials (RCTs) indicated contradictory results. In some cases, the flawed observational studies were the source of evidence for broad practice recommendations.1 While it would be a mistake to ignore all evidence from observational studies-there are many questions that will never be answered by RCTs-clinicians must be careful when interpreting observational studies demonstrating what seem to be surprisingly large beneficial effects of preventive therapy. With the investment of over $1 billion in comparative effectiveness research, clinicians will be faced with increasing volumes of complex results. Proper interpretation will require familiarity with a host of sources of bias in observational research. Bias results when features of a study's design lead to estimates that do not accurately reflect the relationship between the study variables. In this review, we explore a specific subset of these sources of bias-confounding in observational studies resulting from patient-level tendencies to engage in healthy behaviors or physician's perceptions of the health of patients. A recent body of research has emerged examining these sources of bias, and their effect on the interpretation of observational research findings. In this paper, we provide a brief review of observational studies that have appeared to reach incorrect conclusions due to healthy user and other related types of bias. We describe the sources of bias in these studies and discuss study designs, ...

The instrumental variable method that we have proposed appears to have substantially reduced the bias due to unobserved confounding. However, more work needs to be done to understand the sensitivity of this approach to possible violations of the instrumental variable assumptions.