Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical 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 Data The 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 Pharmacoepidemiology Authored 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.

Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical 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 Data The 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 Pharmacoepidemiology Authored 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.

Longitudinal data analysis for biomedical and behavioral sciences
This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data.
Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include:
* Repeated measures analysis of variance
* Multivariate analysis of variance for repeated measures
* Random-effects regression models (RRM)
* Covariance-pattern models
* Generalized-estimating equations (GEE) models
* Generalizations of RRM and GEE for categorical outcomes
Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge.
This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.