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Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
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Statistical Methods for Drug Safety (Paperback)
Loot Price: R1,505
Discovery Miles 15 050
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Statistical Methods for Drug Safety (Paperback)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 12 - 19 working days
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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.
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