Evaluating the introduction of a computerized prior-authorization system on the completeness of drug exposure data

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

Restrictive reimbursement policies: bias implications for claims-based drug safety studies

Restrictive reimbursement policies-including those based on non-formulary drug status and prior authorizations-can create situations in which patients' use of prescription medications is not fully captured in administrative claims data. This can create bias in drug safety studies that depend solely on these data. An analysis in two Canadian provinces found that primary administrative databases captured only 61 % of dispensations of drugs for which restrictive reimbursement policies were in place. A subsequent simulation study found that, in certain circumstances bias due to exposure misclassification resulting from restrictive reimbursement policies can be quite large in analyses comparing outcomes between drug exposure groups. Investigators need to be knowledgeable about the data they analyze and know whether restrictive reimbursement policies are in place that might affect the capture of drugs of interest. It is also critical to understand the mechanisms by which restrictive reimbursement might cause bias in claims-based drug safety studies, the direction and magnitude of the potential bias, and strategies that could be used to mitigate such bias.

Misclassification in administrative claims data: quantifying the impact on treatment effect estimates

Misclassification is present in nearly every epidemiologic study, yet is rarely quantified in analysis in favor of a focus on random error. In this review, we discuss past and present wisdom on misclassification and what measures should be taken to quantify this influential bias, with a focus on bias in pharmacoepidemiologic studies. To date, pharmacoepidemiology primarily utilizes data obtained from administrative claims, a rich source of prescription data but susceptible to bias from unobservable factors including medication sample use, medications filled but not taken, health conditions that are not reported in the administrative billing data, and inadequate capture of confounders. Due to the increasing focus on comparative effectiveness research, we provide a discussion of misclassification in the context of an active comparator, including a demonstration of treatment effects biased away from the null in the presence of nondifferential misclassification. Finally, we highlight recently developed methods to quantify bias and offer these methods as potential options for strengthening the validity and quantifying uncertainty of results obtained from pharmacoepidemiologic research.

Misclassification in administrative claims data: quantifying the impact on treatment effect estimates

Misclassification is present in nearly every epidemiologic study, yet is rarely quantified in analysis in favor of a focus on random error. In this review, we discuss past and present wisdom on misclassification and what measures should be taken to quantify this influential bias, with a focus on bias in pharmacoepidemiologic studies. To date, pharmacoepidemiology primarily utilizes data obtained from administrative claims, a rich source of prescription data but susceptible to bias from unobservable factors including medication sample use, medications filled but not taken, health conditions that are not reported in the administrative billing data, and inadequate capture of confounders. Due to the increasing focus on comparative effectiveness research, we provide a discussion of misclassification in the context of an active comparator, including a demonstration of treatment effects biased away from the null in the presence of nondifferential misclassification. Finally, we highlight recently developed methods to quantify bias and offer these methods as potential options for strengthening the validity and quantifying uncertainty of results obtained from pharmacoepidemiologic research.