QUANTITATIVE METHODS FOR HEALTH RESEARCH

Author: Nigel Bruce, Daniel Pope, and Debbi Stanistreet
Affiliation: University of Liverpool, UK
Publisher: Wiley-Blackwell
Publication Date: 2008
ISBN 10: 0470022752
ISBN 13: 9780470022757
Edition: 1st

Description:

Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care community. Drawing examples from a wide range of health research, this practical handbook covers important contemporary health research methods such as survival analysis, Cox regression, and meta-analysis, the understanding of which go beyond introductory concepts.

Table of Contents

Front Matter

  • ABOUT
  • Preface

Philosophy of science and introduction to epidemiology

  • Introduction and learning objectives
  • 1.1 Approaches to scientific research
  • 1.2 Formulating a research question
  • 1.3 Rates: incidence and prevalence
  • 1.4 Concepts of prevention
  • 1.5 Answers to self-assessment exercises

Routine data sources and descriptive epidemiology

  • Introduction and learning objectives
  • 2.1 Routine collection of health information
  • 2.2 Descriptive epidemiology
  • 2.3 Information on the environment
  • 2.4 Displaying, describing and presenting data
  • 2.5 Summary of routinely available data
  • 2.6 Descriptive epidemiology in action
  • 2.7 Overview of epidemiological study designs
  • 2.8 Answers to self-assessment exercises

Standardisation

  • Introduction and learning objectives
  • 3.1 Health inequalities in Merseyside
  • 3.2 Indirect standardisation: calculation of the standardised mortality ratio (SMR)
  • 3.3 Direct standardisation
  • 3.4 Standardisation for factors other than age
  • 3.5 Answers to self-assessment exercises

Surveys

  • Introduction and learning objectives
  • 4.1 Purpose and context
  • 4.2 Sampling methods
  • 4.3 The sampling frame
  • 4.4 Sampling error, confidence intervals and sample size
  • 4.5 Response
  • 4.6 Measurement
  • 4.7 Data types and presentation
  • 4.8 Answers to self-assessment exercises

Cohort studies

  • Introduction and learning objectives
  • 5.1 Why do a cohort study?
  • 5.2 Obtaining the sample
  • 5.3 Measurement
  • 5.4 Follow-up
  • 5.5 Basic presentation and analysis of results
  • 5.6 How large should a cohort study be?
  • 5.7 Confounding
  • 5.8 Simple linear regression
  • 5.9 Introduction to multiple linear regression
  • 5.10 Answers to self-assessment exercises

Case-control studies

  • Introduction and learning objectives
  • 6.1 Why do a case-control study?
  • 6.2 Key elements of study design
  • 6.3 Basic unmatched and matched analysis
  • 6.4 Sample size for a case-control study
  • 6.5 Confounding and logistic regression
  • 6.6 Answers to self-assessment exercises

Intervention studies

  • Introduction and learning objectives
  • 7.1 Why do an intervention study?
  • 7.2 Key elements of intervention study design
  • 7.3 The analysis of intervention studies
  • 7.4 Testing more complex interventions
  • 7.5 How big should the trial be?
  • 7.6 Further aspects of intervention study design and analysis
  • 7.7 Answers to self-assessment exercises

Life tables, survival analysis and Cox regression

  • Introduction and learning objectives
  • 8.1 Survival analysis
  • 8.2 Cox regression
  • 8.3 Current life tables
  • 8.4 Answers to self-assessment exercises

Systematic reviews and meta-analysis

  • Introduction and learning objectives
  • 9.1 The why and how of systematic reviews
  • 9.2 The methodology of meta-analysis
  • 9.3 Systematic reviews and meta-analyses of observational studies
  • 9.4 The Cochrane Collaboration
  • 9.5 Answers to self-assessment exercises

Prevention strategies and evaluation of screening

  • Introduction and learning objectives
  • 10.1 Concepts of risk
  • 10.2 Strategies of prevention
  • 10.3 Evaluation of screening programmes
  • 10.4 Cohort and period effects
  • 10.5 Answers to self-assessment exercises

Probability distributions, hypothesis testing and Bayesian methods

  • Introduction and learning objectives
  • 11.1 Probability distributions
  • 11.2 Data that do not ‘fit’ a probability distribution
  • 11.3 Hypothesis testing
  • 11.4 Choosing an appropriate hypothesis test
  • 11.5 Bayesian methods
  • 11.6 Answers to self-assessment exercises

References

  • References