Description
Taylor & Francis Ltd Statistical Methods For Drug Safety 2015 Edition by Robert D. Gibbons, Anup Amatya
Explore Important Tools for High-Quality Work in Pharmaceutical SafetyStatistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data.Choose the Right Statistical Approach for Analyzing Your Drug Safety DataThe book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis of rare binary adverse events. It explores research involving the re-analysis of complete longitudinal patient records from randomized clinical trials. The book discusses causal inference models, including propensity score matching, marginal structural models, and differential effects, as well as mixed-effects Poisson regression models for analyzing ecological data, such as county-level adverse event rates. The authors also cover numerous other methods useful for the analysis of within-subject and between-subject variation in adverse events abstracted from large-scale medical claims databases, electronic health records, and additional observational data streams. Advance Statistical Practice in PharmacoepidemiologyAuthored by two professors at the forefront of developing new statistical methodologies to address pharmacoepidemiologic problems, this book provides a cohesive compendium of statistical methods that pharmacoepidemiologists can readily use in their work. It also encourages statistical scientists to develop new methods that go beyond the foundation covered in the text. Introduction Randomized Clinical Trials Observational Studies The Problem of Multiple Comparisons The Evolution of Available Data Streams The Hierarchy of Scientific Evidence Statistical Significance Summary Basic Statistical Concepts Relative Risk Odds Ratio Statistical Power Maximum Likelihood Estimation Non-Linear Regression Models Causal Inference Multi-Level Models Introduction Issues Inherent in Longitudinal Data Historical Background Statistical Models for the Analysis of Longitudinal and/or Clustered Data Causal Inference Introduction Propensity Score Matching Marginal Structural Models Instrumental Variables Differential Effects Analysis of Spontaneous ReportsProportional Reporting Ratio Bayesian Confidence Propagation Neural Network (BCPNN) Empirical Bayes Screening Multi-Item Gamma Poisson Shrinker Bayesian Lasso Logistic Regression Random-Effect Poisson Regression Discussion Meta-Analysis Fixed-Effect Meta-Analysis Random-Effect Meta-Analysis Maximum Marginal Likelihood/Empirical Bayes Method Bayesian Meta-Analysis Confidence Distribution Framework for Meta-Analysis Discussion Ecological Methods Time Series Methods State Space Model Change Point Analysis Mixed-Effects Poisson Regression Model Discrete-Time Survival Models Introduction Discrete-Time Ordinal Regression Model Discrete-Time Ordinal Regression Frailty Model Illustration Competing Risk Models Illustration Research Synthesis Introduction Three-Level Mixed-Effects Regression Models Analysis of Medical Claims Data Introduction Administrative Claims Observational Data Experimental Strategies Statistical Strategies Illustrations Conclusion Methods to Be Avoided Introduction Spontaneous Reports Vote Counting Simple Pooling of Studies Including Randomized and Non-Randomized Trials in Meta-Analysis Multiple Comparisons and Biased Reporting of Results Immortality Time Bias Summary and Conclusions Final Thoughts BibliographyIndex