Indications for Propensity Scores and Review of their Use in Pharmacoepidemiology

Abstract

Use of propensity scores to identify and control for confounding in observational studies that relate medications to outcomes has increased substantially in recent years. However, it remains unclear whether, and if so when, use of propensity scores provides estimates of drug effects that are less biased than those obtained from conventional multivariate models. In the great majority of published studies that have used both approaches, estimated effects from propensity score and regression methods have been similar. Simulation studies further suggest comparable performance of the two approaches in many settings. We discuss five reasons that favour use of propensity scores: the value of focus on indications for drug use; optimal matching strategies from alternative designs; improved control of confounding with scarce outcomes; ability to identify interactions between propensity of treatment and drug effects on outcomes; and correction for unobserved confounders via propensity score calibration. We describe alternative approaches to estimate and implement propensity scores and the limitations of the C-statistic for evaluation. Use of propensity scores will not correct biases from unmeasured confounders, but can aid in understanding determinants of drug use and lead to improved estimates of drug effects in some settings.Use of propensity scores in pharmacoepidemiologic studies has increased substantially over the past few years, yet evidence is lacking that this approach will systematically give better estimates of drug effects than those obtained from conventional regression approaches. If one compares the distributions of variables included in a propensity score between users of a drug and non-users matched on the propensity score, the balance of these distributions between groups will frequently be better than if drug allocation were randomized (Joffe & Rosembaum 1999). However, randomization tends to balance the unmeasured confounders, whereas matching on the propensity score often will not.Thus, propensity score methods and conventional multivariate methods (Drake 1993) have similar inability to control unmeasured confounding. In this context, this article considers whether increased use of propensity scores is warranted. We begin with definitions, a review of the properties of the propensity score, and a description of its increasing use. We summarize available empirical comparisons and simulation studies of drug effects estimated by the propensity score versus conventional regression methods. We mention specific circumstances when use of propensity scores

Comparison of Propensity Score Methods and Covariate Adjustment

“…Recently, some have argued that PS methods may be more robust or offer more complete adjustment for confounders. [28] However, whilst there are theoretical grounds on which to favor propensity score adjustment, we see little practical evidence to justify such negative claims [30] and in our examples, covariate adjustment provided reliable and statistically efficient estimates. One issue for datasets with few event outcomes is that the number of covariates considered for inclusion in the model may be limited, whereas many -21 -more covariates may be included in a PS model without raising concerns of over-fitting or lack of model convergence.…”

886 Partial nephrectomy for the treatment of renal cell carcinoma and the risk of end stage renal disease

“…A matched analysis will exclude all unmatched patients, which may not necessarily be appropriate and whose real benefit remains unknown. 133 …”

Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes

“…Confounding adjustment by baseline covariates (L(0)) using standard modeling approaches or causal methods for point treatment problems (e.g., propensity score [14][15][16][17] ) can then provide unbiased estimates of the effect of the early therapy exposure (A(0)). Such results are especially useful in studies (e.g., randomized trials) in which changes in therapies are uncommon during the follow-up time, that is, an intention-to-treat (ITT) interpretation of the results is informative.…”