BACKGROUND: Over 84 % of all prescriptions in the US are filled as generic drugs, though in prior surveys, patients reported concerns about their quality. OBJECTIVE: We aimed to survey patients' perceptions and use of generic drugs. DESIGN: Our survey (administered August 2014) assessed patients' skepticism about generic drug safety and effectiveness and how often they requested brandname drugs. Chi-square tests and two-sample t-tests assessed associations between patient demographics and the outcomes. PARTICIPANTS: Our sample frame was the CVS Advisor Panel, a national database of 124,621 CVS customers. We randomly selected 1450 patients with self-reported chronic conditions who filled at least one prescription in the prior 3 months. MAIN MEASURES: We assessed how often patients reported asking their physicians to prescribe a brandname over a generic drug in the last year, and Bgeneric skepticism,^defined as not believing generic drugs were as safe, effective, had the same side effects, and contained the same active ingredients as brand-name drugs. KEY RESULTS: Of the 1,442 patients with valid addresses, 933 responded (65 % response rate) and 753 took the full survey. A vast majority (83 %) agreed that physicians should prescribe generic drugs when available, and 54 % said they had not asked their physicians to prescribe a brand-name drug over a generic in the past year. Most respondents considered generic drugs to be as effective (87 %) and safe (88 %) as their brand-name counterparts, and to have the same side effects (80 %) and active ingredients (84 %). Non-Caucasians were more likely than Caucasians to request a brand-name drug over a generic (56 % vs. 43 %, p < 0.01), and were also more skeptical of generic drugs' clinical equivalence (43 % vs. 29 %, p < 0.01). CONCLUSIONS: We found a substantial shift towards more patients having positive views of generic drugs, but lingering negative perceptions will have to be overcome to ensure continued cost-savings and improved patient outcomes from generic drugs.

Our new command makes diagnostic plots for multiple imputations created by . The plots compare the distribution of the imputed values with that of the observed values so that problems with the imputation model can be corrected before the imputed data are analyzed. We include an example and suggest extensions to other diagnostics.

Selection and measurement of confounders is critical for successful adjustment in nonrandomized studies. Although the principles behind confounder selection are now well established, variable selection for confounder adjustment remains a difficult problem in practice, particularly in secondary analyses of databases. We present a simulation study that compares the high-dimensional propensity score algorithm for variable selection with approaches that utilize direct adjustment for all potential confounders via regularized regression, including ridge regression and lasso regression. Simulations were based on 2 previously published pharmacoepidemiologic cohorts and used the plasmode simulation framework to create realistic simulated data sets with thousands of potential confounders. Performance of methods was evaluated with respect to bias and mean squared error of the estimated effects of a binary treatment. Simulation scenarios varied the true underlying outcome model, treatment effect, prevalence of exposure and outcome, and presence of unmeasured confounding. Across scenarios, high-dimensional propensity score approaches generally performed better than regularized regression approaches. However, including the variables selected by lasso regression in a regular propensity score model also performed well and may provide a promising alternative variable selection method.

The U.S. FDA issued several announcements related to potential risk of bisphosphonates including osteonecrosis of the jaw (2005), atrial fibrillation (2007) and atypical femur fracture (2010). We aimed to evaluate the impact of three FDA drug safety announcements on the use of bisphosphonates in patients with hip fracture using claims data from a U.S. commercial health plan (2004–13). We calculated the proportion of patients in each quarter who received a bisphosphonate or other osteoporosis medication in the 6 months following hospitalization for hip fracture. Segmented logistic regression models examined the time trends. Among 22,598 patients with hip fracture, use of bisphosphonate decreased from 15% in 2004 to 3% in the last quarter of 2013. Prior to the 2007 announcement, there was a 4% increase in the odds of bisphosphonate use every quarter (OR 1.04, 95%CI 1.02–1.07). After the 2007 announcement, there was a 4% decrease in the odds of bisphosphonate use (OR 0.96, 95%CI 0.93–0.99) every quarter. The announcement in 2007 was associated with a significant decline in the rate of change of bisphosphonate uses over time (p<0.001), but no impact on other osteoporosis medication use (p=0.2). After the 2010 announcement, the odds of bisphosphonate use continued to decrease by 4% (OR 0.96. 95%CI, 0.94–0.98) each quarter and the odds of other osteoporosis medication use remained stable over time (OR 0.99, 95%CI 0.96–1.02). The FDA safety announcement related to atrial fibrillation in 2007 was significantly associated with a decrease in bisphosphonate use among patients with hip fracture.