Biostatistical Methods The Assessment of Relative Risks

by Lachin, John M.

• ISBN13: 9780470508220
• ISBN10: 0470508221
• Edition: 2nd
• Format: Hardcover
• Copyright: 2010-12-21
• Publisher: Wiley

Summary

This book focuses on the comparison, contrast, and assessment of risks on the basis of clinical investigations. It develops basic concepts as well as deriving 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; the analysis of event time data, including the proportional hazards and multiplicative intensity models; and elements of categorical data analysis (expanded in this edition).SAS subroutines are both showcased in the text and embellished online by way of a dedicated author website. The book contains a technical, but accessible appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods.

Author Biography

John M. Lachin, ScD, is Co-Director of The Biostatistics Center at The George Washington University, where he also serves as Professor of Biostatistics and Epidemiology, and of Statistics. He has published extensively in his areas of research interest, which include sample size evaluation, group sequential methods, analysis of repeated measures, and survival analysis. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is the coauthor of Randomization in Clinical Trials: Theory and Practice, also published by Wiley.

Table of Contents

Preface

Biostatistics and Biomedical Science

Statistics and the Scientific Method

Biostatistics

Natural History of Disease Progression

Types of Biomedical Studies

Studies of Diabetic Nephropathy

Relative Risk Estimates and Tests for Independent Groups

Probability As a Measure of Risk

Measures of Relative Risk

Large Sample Distribution

Sampling Models Likelihoods

Exact Inference

Large Sample Inferences

SAS PROC FREQ

Other Measures of Differential Risk

Polychotomous and Ordinal Data

Two Independent Groups With Polychotomous Response

Multiple Independent Groups

Problems

Sample Size, Power, and Efficiency

Estimation Precision

Power of Z-Tests

Test for Two Proportions

Power of Chi-Square Tests

SAS PROC POWER

Efficiency

Problems

Stratified-Adjusted Analysis for Independent Groups

Introduction

Mantel-Haenszel Test and Cochran's Test

Stratified-Adjusted Estimators

Nature of Covariate Adjustment

Multivariate Tests of Hypotheses

Tests of Homogeneity

Efficient Tests of No Partial Association

Asymptotic Relative Efficiency of Competing Tests

Maximin Efficient Robust Tests

Random Effects Model

Power and Sample Size for Tests of Association

Polychotomous and Ordinal Data

Problems

Case-Control and Matched Studies

Unmatched Case-Control (Retrospective) Sampling

Matching

Tests of Association for Matched Pairs

Measures of Association for Matched Pairs

Pair-Matched Retrospective Study

Power Function of McNemar's Test

Stratified Analysis of Pair-Matched Tables

Multiple Matching-Mantel-Haenszel Analysis

Matched Polychotomous Data

Problems

Applications of Maximum Likelihood and Efficient Scores

Binomial

2x2 Table: Product Binomial (Unconditionally)

2x2 Table, Conditionally

Score-Based Estimate

Stratified Score Analysis of Independent 2x2 Tables

Matched Pairs

Iterative Maximum Likelihood

Problems

Logistic Regression Models

Unconditional Logistic Regression Model

Interpretation of the Logistic Regression Model

Tests of Significance

Interactions

Measures of the Strength of Association

Conditional Logistic Regression Model for Matched Sets

Models for Polychotomous or Ordinal Data

Random Effects and Mixed Models

Models for Multivariate or Repeated Measures

Problems

Analysis of Count Data

Event Rates and the Homogeneous Poisson Model

Over-Dispersed Poisson Model

Poisson Regression Model

Over-Dispersed and Robust Poisson Regression

Conditional Poisson Regression for Matched Sets

Negative Binomial Models

Power and Sample Size

Multiple Outcomes

Problems

Analysis of Event-Time Data

Introduction to Survival Analysis

Lifetable Construction

Family of Weighted Mantel-Haenszel Tests

Proportional Hazards Models

Evaluation of Sample Size and Power

Additional Models

Analysis of Recurrent Events

Problems

Appendix Statistical Theory

Introduction 535

Central Limit Theorem and the Law of Large Numbers

Delta Method

Slutsky's Convergence Theorem

Least Squares Estimation

Maximum Likelihood Estimation and Efficient Scores

Tests of Significance

Explained Variation

Robust Inference

Generalized Linear Models and Quasi-Likelihood

Generalized Estimating Equations (GEE)

References

Table of Contents provided by Publisher. All Rights Reserved.