Propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates. This matching is the observational study analog of randomization in ideal experiments, but is far less complete as it can only balance the distribution of observed covariates, whereas randomization balances the distribution of all covariates, both observed and unobserved. An important feature of propensity score matching is that it can be easily combined with model-based regression adjustments or with matching on a subset of special prognostic covariates or combinations of prognostic covariates that have been identified as being especially predictive of the outcome variables. We extend earlier results by developing approximations for the distributions of covariates in matched samples created with linear propensity score methods for the practically important situation where matching uses both the estimated linear propensity scores and a set of special prognostic covariates. Such matching on a subset of special prognostic covariates is an observational study analog of blocking in a randomized experiment. An example combining propensity score matching with Mahalanobis metric matching and regression adjustment is presented that demonstrates the flexibility of these methods for designing an observational study that effectively reduces both bias due to many observed covariates and bias and variability due to a more limited subset of covariates. Of particular importance, the general approach, which includes propensity score matching, was distinctly superior to methods that focus only on a subset of the prognostically most important covariates, even if those covariates account for most of the variation in the outcome variables. Also of importance, analyses based on matched samples were superior to those based on the full unmatched samples, even when regression adjustment was included.

No abstract

No abstract

. Odds ratios and adjusted prevalences of hypertension were calculated across sexspecific quintiles of body mass index (BMI), waist-to-hip ratio (WHR), waist circumference, and waist-to-height ratio (waist/ height) and adjusted for age, research center, smoking, education, physical activity, alcohol consumption, hormone replacement therapy, and menopausal status. Results: The prevalence of hypertension was higher among African Americans than whites. In the lowest quintile of BMI, 41% of African American women and 43% of African American men had hypertension compared with 14% of white women and 19% of white men. Elevated BMI, WHR, waist circumference, and waist/height were associated with increased odds of hypertension in African American and white men and women. In women, but not in men, there were significant interactions between ethnicity and the anthropometric variables studied here. The direction of the interaction indicated larger odds ratios for hypertension with increasing levels of anthropometric indices in white compared with African American women. Discussion: Obesity and abdominal fat preponderance were associated with increased prevalence of hypertension in African American and white men and women. Associations were similar among African American and white men, but obesity and fat patterning were less strongly associated with hypertension in African American than in white women.

No abstract

No abstract

No abstract

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.