Its goal is to decrease bias in the estimate of the treatment effect byreducing treatment group imbalance. The propensityscore, e(x), represents the conditional probability of receiving treatment, given, for example, pretreatment clinical and demographic

characteristics, such that 0≤e(x)≤1. The signaling pathway propensity score for each observation is derived from a logistic regression model as described below. Subjects with low propensity scores have characteristics of someone Inhibitors,research,lifescience,medical unlikely to get treatment, whereas those with high propensity scores have characteristics of someone more likely to get treatment. Before the introduction of the concept of the propensity score, Cochran10 showed that analyses that are stratified Inhibitors,research,lifescience,medical into quintiles of a confounding variable will remove >90% of the associated bias. Building on these

findings, the propensity adjustment can be implemented through stratification. The rationale for this is that within a propensity score quintile (containing 20% of the observations), the variability in propensity scores will be greatly reduced, albeit not necessarily a constant (as in the health insurance example, above). Based on the concept of restriction of range, propensity score stratification will attenuate the association between the confounding variables Inhibitors,research,lifescience,medical and treatment. In addition to stratification, matching, inverse probability weighting, and covariate adjustment are other strategies to implement the propensity adjustment. Inhibitors,research,lifescience,medical Covariate adjustment, though most commonly used, can be problematic.9 Two stages of propensity analyses The propensity adjustment is implemented in two stages: (i) propensity score estimation, and (ii) treatment effectiveness analyses. The first stage is the propensity model. The propensity score is estimated based on parameter estimates from a logistic regression model for cross-sectional data or mixed-effects logistic regression model for longitudinal data. A preponderance of the applications and evaluations of the propensity

methods Inhibitors,research,lifescience,medical have involved cross-sectional data. However, evaluations of the performance of propensity quintile stratification with longitudinal observational data have supported its use for bias reduction11-15 and both examples presented below involve TCL longitudinal data. The dependent variable in the propensity model is treatment (eg, novel vs standard) and the independent variables include demographic and clinical characteristics hypothesized to be associated with treatment. The propensity adjustment assumes that treatment assignment is strongly ignorable conditional on the propensity score.9 The plausibility of this assumption can be examined by evaluating the between-treatment group balance on pretreatment variables (ie, the variables included in the propensity score) after implementing the propensity adjustment. For instance, this could be done by comparing treatment groups on baseline variables separately within each propensity quintile.