By Eric Vittinghoff, David V. Glidden, Visit Amazon's Stephen C. Shiboski Page, search results, Learn about Author Central, Stephen C. Shiboski, , Charles E. McCulloch
This new publication presents a unified, in-depth, readable advent to the multipredictor regression equipment most generally utilized in biostatistics: linear types for non-stop results, logistic versions for binary results, the Cox version for right-censored survival instances, repeated-measures types for longitudinal and hierarchical results, and generalized linear types for counts and different results.
Treating those issues jointly takes good thing about all they've got in universal. The authors indicate the many-shared parts within the tools they current for choosing, estimating, checking, and analyzing every one of those types. additionally they express that those regression equipment care for confounding, mediation, and interplay of causal results in basically an analogous approach.
The examples, analyzed utilizing Stata, are drawn from the biomedical context yet generalize to different components of software. whereas a primary path is information is thought, a bankruptcy reviewing easy statistical equipment is incorporated. a few complicated subject matters are lined however the presentation is still intuitive. a short advent to regression research of advanced surveys and notes for additional analyzing are supplied. for lots of scholars and researchers studying to exploit those equipment, this one ebook could be all they should behavior and interpret multipredictor regression analyses.
The authors are at the school within the department of Biostatistics, division of Epidemiology and Biostatistics, college of California, San Francisco, and are authors or co-authors of greater than two hundred methodological in addition to utilized papers within the organic and biomedical sciences. The senior writer, Charles E. McCulloch, is head of the department and writer of Generalized Linear combined types (2003), Generalized, Linear, and combined versions (2000), and Variance parts (1992).