It is increasingly important to accurately and comprehensively estimate the effects of
particular clinical treatments. Although randomization is the current gold standard …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

This report aims to provide methodological guidance to help practitioners select the most
appropriate weighting method based on propensity scores for their analysis out of many …

Propensity score methods are used to reduce the effects of observed confounding when
using observational data to estimate the effects of treatments or exposures. A popular …

Background Double-adjustment can be used to remove confounding if imbalance exists
after propensity score (PS) matching. However, it is not always possible to include all …

More comprehensive than other texts, this new book covers the classic and cutting edge
multivariate techniques used in today's research. Ideal for courses on multivariate …

The propensity score is the probability of treatment assignment conditional on observed
baseline characteristics. The propensity score allows one to design and analyze an …

Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure
effects of intervention or treatment on outcomes. They are also the designs of choice for …

Estimation of causal effects using observational data continues to grow in popularity in the
epidemiologic literature. While many applications of causal effect estimation use propensity …

Propensity score methods are increasingly being used to reduce or minimize the effects of
confounding when estimating the effects of treatments, exposures, or interventions when …