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Real-time data collection of patient health status and medications is sped up with modern electronic devices and technologies. As real-world data provide enormous research opportunities, propensity score (PS) methods have been getting attention due to their theoretical grounds in a nonrandomized study setting. In contrast to randomized clinical trials, observational clinical data obtained from a real-world database may not have balanced distributions of patient characteristics between treatment and control groups at the beginning of the respective study. These imbalanced distributions may cause a bias in an estimated treatment effect, which needs to be eliminated. Propensity scoring is one class of statistical methods to address the imbalance issue of real-world data sets. This article provides basic concepts and assesses advantages, disadvantages, and methodological objectives of propensity scoring. Targeting clinical pharmacology researchers with limited statistical background, 5 representative methods are reviewed and visualized: matching, stratification, covariate modeling, inverse probability of treatment weighting, and doubly robust methods. Examples of applications of PS methods were selected from the literature of outcomes research and drug development, nephrology, and pediatrics. Opportunities of applications related to these examples are described. Furthermore, potential future applications of PS methods in clinical pharmacology are discussed. The 21st Century Cures Act signed in 2016 encourages scientists to find opportunities to apply propensity scoring to real-world data. This article underscores that scientists need to justify their choice of statistical methods, whether a PS method or an alternative method, based on their clinical study design, statistical assumptions, and research objectives., (© 2021, The American College of Clinical Pharmacology.)

Analyses of study variables are frequently based on log transformations. To calculate the power for detecting the between-treatment difference in the log scale, we need an estimate of the standard deviation of the log-transformed variable. However, in many situations a literature search only provides the arithmetic means and the corresponding standard deviations. Without individual log-transformed data to directly calculate the sample standard deviation, we need alternative methods to estimate it. This paper presents methods for estimating and constructing confidence intervals for the standard deviation of a log-transformed variable given the mean and standard deviation of the untransformed variable. It also presents methods for estimating the standard deviation of change from baseline in the log scale given the means and standard deviations of the untransformed baseline value, on-treatment value and change from baseline. Simulations and examples are provided to assess the performance of these estimates., (Copyright 2003 John Wiley & Sons, Ltd.)

Bruno de Finetti is possibly the best known Italian applied mathematician of the 20th century, but was he really just a mathematician? Looking at his papers it is always possible to find original and pioneering contributions to the various fields he was interested in, where he always put his mathematical "formamentis" and skills at the service of the applications, often extending standard theories and models in order to achieve more general results. Many contributions are also devoted to educational issues, in mathematics in general and in probability and statistics in particular.He really thought that mathematics and, in particular, those topics related to uncertainty, should enter in everyday life as a useful support to everyone's decision making. He always imagined and lived mathematics as a basic tool both for better understanding and describing complex phenomena and for helping decision makers in assuming coherent and feasible actions. His many important contributions to the theory of probability and to mathematical statistics are well known all over the world, thus, in the following, minor, but still pioneering, aspects of his work, related both to theory and to applications of mathematical tools, and to his work in the field of education and training of teachers, are presented., (Copyright 2001 John Wiley & Sons, Ltd.)