Statistical models in epidemiology

Statistical models in epidemiology

by Clayton, David

Type Book
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This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics.

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Title Statements

  • Full title :Statistical models in epidemiology / David Clayton and Michael Hills.

Summary

This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics.

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Edition Statements

  • Publication Date:1993.

Publication Statements

Notes

(Cont) Table of the chi-squared distribution.

The Physical Object

  • Description:1 online resource (viii, 367 pages : : illustrations

Edition Identifiers

  • ISBN:0191650919, 9780191650918, 0191650900, 9780191650901
  • oclc:on1178535516
  • LC Classification:RA652.2.M3
  • Dewey Decimal:614.4/072

Table of Contents

Probability models -- Conditional probability models -- Likelihood -- Consecutive follow-up intervals -- Rates -- Time -- Competing risks and selection -- Gaussian probability model -- Approximate likelihoods -- Likelihood, probability, and confidence -- Null hypotheses and p-values -- Small studies -- Likelihoods for the rate ration -- Confounding and standardization -- Comparison of rates within strata -- Case-control studies -- Likelihoods for the odds ratio -- Comparison of odds within strata -- Individually matched case-control studies -- Tests for trend -- Size of investigations -- Introduction to regression models -- Poisson and logistic regression -- Testing hypotheses -- Models for dose-response -- More about interaction -- Choice and interpretation of models -- Additivity and synergism -- Conditional logistic regression -- Cox's regression analysis -- Time-varying explanatory variables -- Three examples -- Nested case-control studies -- Gaussian regression models -- Postscript -- Exponentials and logarithms -- Some basic calculus -- Approximate profile likelihoods.

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