Description

Book Synopsis
Features coverage of the epidemiological indices, and includes developments in the field. This title serves as a useful reference source for biostatisticians and epidemiologists working in disease prevention, as the chapters are self-contained and feature several real examples.

Trade Review
"…a concise, organized, and well-written text that provides the derivations of statistical formulas underlying much epidemiological research and practice." (Journal of the American Statistical Association, December 2005)

"...presents a considerable amount of recent research, much of which is the author's own..." (Royal Statistical Society, Vol.168, No.1, January 2005)

"...systematically organised...an excellent reference..." (Short Book Review, Vol.24, No.3 December 2004)

"...this book is strongly recommended..." (Statistical Methods in Medical Research, Vol 14 2005)



Table of Contents
About the author.

Preface.

1 Population Proportion or Prevalence.

1.1 Binomial sampling.

1.2 Cluster sampling.

1.3 Inverse sampling.

Exercises.

References.

2 Risk Difference.

2.1 Independent binomial sampling.

2.2 A series of independent binomial sampling procedures.

2.2.1 Summary interval estimators.

2.2.2 Test for the homogeneity of risk difference.

2.3 Independent cluster sampling.

2.4 Paired-sample data.

2.5 Independent negative binomial sampling (inverse sampling).

2.6 Independent poisson sampling.

2.7 Stratified poisson sampling.

Exercises.

References.

3 Relative Difference.

3.1 Independent binomial sampling.

3.2 A series of independent binomial sampling procedures.

3.2.1 Asymptotic interval estimators.

3.2.2 Test for the homogeneity of relative difference.

3.3 Independent cluster sampling.

3.4 Paired-sample data.

3.5 Independent inverse sampling.

Exercises.

References.

4 Relative Risk.

4.1 Independent binomial sampling.

4.2 A series of independent binomial sampling procedures.

4.2.1 Asymptotic interval estimators.

4.2.2 Test for the homogeneity of risk ratio.

4.3 Independent cluster sampling.

4.4 Paired-sample data.

4.5 Independent inverse sampling.

4.5.1 Uniformly minimum variance unbiased estimator of relative risk.

4.5.2 Interval estimators of relative risk.

4.6 Independent poisson sampling.

4.7 Stratified poisson sampling.

Exercises.

References.

5 Odds Ratio.

5.1 Independent binomial sampling.

5.1.1 Asymptotic interval estimators.

5.1.2 Exact confidence interval.

5.2 A series of independent binomial sampling procedures.

5.2.1 Asymptotic interval estimators.

5.2.2 Exact confidence interval.

5.2.3 Test for homogeneity of the odds ratio.

5.3 Independent cluster sampling.

5.4 One-to-one matched sampling.

5.5 Logistic modeling.

5.5.1 Estimation under multinomial or independent binomial sampling.

5.5.2 Estimation in the case of paired-sample data.

5.6 Independent inverse sampling.

5.7 Negative multinomial sampling for paired-sample data.

Exercises.

References.

6 Generalized Odds Ratio.

6.1 Independent multinomial sampling.

6.2 Data with repeated measurements (or under cluster sampling).

6.3 Paired-sample data.

6.4 Mixed negative multinomial and multinomial sampling.

Exercises.

References.

7 Attributable Risk.

7.1 Study designs with no confounders.

7.1.1 Cross-sectional sampling.

7.1.2 Case–control studies.

7.2 Study designs with confounders.

7.2.1 Cross-sectional sampling.

7.2.2 Case–control studies.

7.3 Case–control studies with matched pairs.

7.4 Multiple levels of exposure in case–control studies.

7.5 Logistic modeling in case–control studies.

7.5.1 Logistic model containing only the exposure variables of interest.

7.5.2 Logistic regression model containing both exposure and confounding variables.

7.6 Case–control studies under inverse sampling.

Exercises.

References.

8 Number Needed to Treat.

8.1 Independent binomial sampling.

8.2 A series of independent binomial sampling procedures.

8.3 Independent cluster sampling.

8.4 Paired-sample data.

Exercises.

References.

Appendix Maximum Likelihood Estimator and Large-Sample Theory.

A.1: The maximum likelihood estimator, Wald’s test, the score test, and the asymptotic likelihood ratio test.

A.2: The delta method and its applications.

References.

Answers to Selected Exercises.

Index.

Statistical Estimation of Epidemiological Risk

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      View other formats and editions of Statistical Estimation of Epidemiological Risk by Kung-Jong Lui

      Publisher: John Wiley & Sons Inc
      Publication Date: 29/01/2004
      ISBN13: 9780470850718, 978-0470850718
      ISBN10: 047085071X

      Description

      Book Synopsis
      Features coverage of the epidemiological indices, and includes developments in the field. This title serves as a useful reference source for biostatisticians and epidemiologists working in disease prevention, as the chapters are self-contained and feature several real examples.

      Trade Review
      "…a concise, organized, and well-written text that provides the derivations of statistical formulas underlying much epidemiological research and practice." (Journal of the American Statistical Association, December 2005)

      "...presents a considerable amount of recent research, much of which is the author's own..." (Royal Statistical Society, Vol.168, No.1, January 2005)

      "...systematically organised...an excellent reference..." (Short Book Review, Vol.24, No.3 December 2004)

      "...this book is strongly recommended..." (Statistical Methods in Medical Research, Vol 14 2005)



      Table of Contents
      About the author.

      Preface.

      1 Population Proportion or Prevalence.

      1.1 Binomial sampling.

      1.2 Cluster sampling.

      1.3 Inverse sampling.

      Exercises.

      References.

      2 Risk Difference.

      2.1 Independent binomial sampling.

      2.2 A series of independent binomial sampling procedures.

      2.2.1 Summary interval estimators.

      2.2.2 Test for the homogeneity of risk difference.

      2.3 Independent cluster sampling.

      2.4 Paired-sample data.

      2.5 Independent negative binomial sampling (inverse sampling).

      2.6 Independent poisson sampling.

      2.7 Stratified poisson sampling.

      Exercises.

      References.

      3 Relative Difference.

      3.1 Independent binomial sampling.

      3.2 A series of independent binomial sampling procedures.

      3.2.1 Asymptotic interval estimators.

      3.2.2 Test for the homogeneity of relative difference.

      3.3 Independent cluster sampling.

      3.4 Paired-sample data.

      3.5 Independent inverse sampling.

      Exercises.

      References.

      4 Relative Risk.

      4.1 Independent binomial sampling.

      4.2 A series of independent binomial sampling procedures.

      4.2.1 Asymptotic interval estimators.

      4.2.2 Test for the homogeneity of risk ratio.

      4.3 Independent cluster sampling.

      4.4 Paired-sample data.

      4.5 Independent inverse sampling.

      4.5.1 Uniformly minimum variance unbiased estimator of relative risk.

      4.5.2 Interval estimators of relative risk.

      4.6 Independent poisson sampling.

      4.7 Stratified poisson sampling.

      Exercises.

      References.

      5 Odds Ratio.

      5.1 Independent binomial sampling.

      5.1.1 Asymptotic interval estimators.

      5.1.2 Exact confidence interval.

      5.2 A series of independent binomial sampling procedures.

      5.2.1 Asymptotic interval estimators.

      5.2.2 Exact confidence interval.

      5.2.3 Test for homogeneity of the odds ratio.

      5.3 Independent cluster sampling.

      5.4 One-to-one matched sampling.

      5.5 Logistic modeling.

      5.5.1 Estimation under multinomial or independent binomial sampling.

      5.5.2 Estimation in the case of paired-sample data.

      5.6 Independent inverse sampling.

      5.7 Negative multinomial sampling for paired-sample data.

      Exercises.

      References.

      6 Generalized Odds Ratio.

      6.1 Independent multinomial sampling.

      6.2 Data with repeated measurements (or under cluster sampling).

      6.3 Paired-sample data.

      6.4 Mixed negative multinomial and multinomial sampling.

      Exercises.

      References.

      7 Attributable Risk.

      7.1 Study designs with no confounders.

      7.1.1 Cross-sectional sampling.

      7.1.2 Case–control studies.

      7.2 Study designs with confounders.

      7.2.1 Cross-sectional sampling.

      7.2.2 Case–control studies.

      7.3 Case–control studies with matched pairs.

      7.4 Multiple levels of exposure in case–control studies.

      7.5 Logistic modeling in case–control studies.

      7.5.1 Logistic model containing only the exposure variables of interest.

      7.5.2 Logistic regression model containing both exposure and confounding variables.

      7.6 Case–control studies under inverse sampling.

      Exercises.

      References.

      8 Number Needed to Treat.

      8.1 Independent binomial sampling.

      8.2 A series of independent binomial sampling procedures.

      8.3 Independent cluster sampling.

      8.4 Paired-sample data.

      Exercises.

      References.

      Appendix Maximum Likelihood Estimator and Large-Sample Theory.

      A.1: The maximum likelihood estimator, Wald’s test, the score test, and the asymptotic likelihood ratio test.

      A.2: The delta method and its applications.

      References.

      Answers to Selected Exercises.

      Index.

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