Biostatistical Methods: The Assessment of Relative Risks
Comprehensive coverage of classical and modern methods of biostatistics
Biostatistical Methods focuses on the assessment of risks and relative risks on the basis of clinical investigations. It develops basic concepts and derives biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories.
The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; and the analysis of event time data, including the proportional hazards and multiplicative intensity models. The book contains a technical appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods. Biostatistical Methods: The Assessment of Relative Risks:
* Presents modern biostatistical methods that are generalizations of the classical methods discussed
* Emphasizes derivations, not just cookbook methods
* Provides copious reference citations for further reading
* Includes extensive problem sets
* Employs case studies to illustrate application of methods
* Illustrates all methods using the Statistical Analysis System(r) (SAS)
Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises, Biostatistical Methods: The Assessment of Relative Risks is an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.
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Relative Risk Estimates and Tests for Two Independent Groups
Sample Size Power and Efficiency
StratifiedAdjusted Analysis for Two Independent Groups
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2x2 table adjusted alternative hypothesis assess assumed asymmetric confidence limits asymptotically distributed binomial censored chi-square Clinical Trial coefficient cohort common odds ratio compute confidence limits consistently estimated covariance matrix covariate vector DCCT derived described in Section disease error estimated variance evaluated Example exposure expressed hazard function hazard ratio HbA1c hypoglycemia information sandwich intensity interval iterative large sample variance likelihood ratio test linear log likelihood log odds ratio log relative risk logistic model logistic regression logistic regression model Mantel-Haenszel test marginal MVLE nephropathy non-centrality null hypothesis number of events observations obtained over-dispersion P-value parameter patients Poisson regression population presented probability PROC GENMOD proportional hazards provides random effects model relative risk risk difference robust scale score equation score vector Slutsky's Theorem specified strata stratified-adjusted stratum subjects survival function test statistic Theorem treatment group unadjusted variable versus Wald test weights yields Z-test