 |
| Mike Saks and Judith Allsop |
Chapter 11 - Statistical Methods for Health Data Analysis
George Argyrous |
| |
|
| |
| Contributor
biography |
| George Argyrous is Senior Lecturer
in the School of Social Sciences and International Studies
at the University of New South Wales, Australia. He is
author of the popular Sage text, Statistics for Research:
With a Guide to SPSS. George has taught statistical
methods across a range of disciplines and has also acted
as a consultant for many public and private organisations
on the use of statistical methods. |
| |
| Chapter overview |
| The chapter provides a basic initial
introduction to statistics for analysing health data.
It focuses on the first step in statistical analysis –
straightforward statistical description and identifies
more advanced methods for those who wish to undertake
further reading on the subject. |
| |
| Chapter links |
| Chapter
10 - Quantitative Survey Methods in Health Research |
| |
| Suggested
Online Readings |
| Levy,
P. S. and Stolte, K. (2000) ‘Statistical Methods
in Public Health and Epidemiology: A Look at the Recent
Past and Projections for the Next Decade’, Statistical
Methods in Medical Research, 9 (1): 41-55. |
| The article assesses from past patterns,
the type of statistical methods that will be used in published
public health and epidemiological studies in the decade
that follows the millennium. The authors assess trends
in use of statistical methods in two major public health
journals: the American Journal of Public Health, and the
American Journal of Epidemiology. A probability sample
of 348 articles published in these journals between 1970
and 1998 were analysed for the design of the study and
the types of statistical methods used in each article.
The proportion of articles using statistical methods as
well as the mean number of statistical methods used per
article has increased dramatically over the three decades
surveyed. Patterns of use in these journals of three statistical
methodologies: logistic regression, proportional hazards
regression, and methods for analysis of data from complex
sample surveys are also examined. |
| |
| O’Brien,
B.J. and Briggs, A.H. (2002) ‘Analysis of Uncertainty
in Health Care Cost-effectiveness Studies: An Introduction
to Statistical Issues and Methods’, Statistical
Methods in Medical Research, 11 (6): 455-68. |
| The paper discusses a basic framework
for decision making using cost-effectiveness data and
reviews recent developments in statistical methods for
analysis of uncertainty when cost-effectiveness estimates
are based on observed data from a clinical trial. The
paper advocates plotting the joint density of cost and
effect differences, together with cumulative density plots
known as cost-effectiveness acceptability curves (CEACs)
to summarize the overall value-for-money of interventions.
The net-benefit formulation of the cost-effectiveness
problem is outlined its particular advantages over the
standard incremental cost-effectiveness ratio formulation
is discussed. |
| |
| O’Hagan,
A. and Stevens, J.W. (2002) ‘Bayesian Methods for
Design and Analysis of Cost-effectiveness Trials in the
Evaluation of Health Care Technologies’, Statistical
Methods in Medical Research, 11 (6): 469-90. |
| The development of Bayesian statistical
methods for the design and analysis of randomized controlled
trials in the assessment of the cost-effectiveness of
health care technologies are reviewed. The paper examines
the benefits of the Bayesian approach; the implications
of skew cost data; the need to model the data appropriately
to generate efficient and robust inferences instead of
relying on distribution-free methods; the importance of
making full use of quantitative and structural prior information
to produce realistic inferences; and issues in the determination
of sample size. It uses examples to illustrate the methods.
|
| |
| Further Reading |
| Argyrous, G. (2005) Statistics
for Research. London: Sage. |
| This book provides reasonably comprehensive
coverage of the descriptive statistics introduced in this
chapter, as well as an entry point for the more advanced
measures of association and inferential statistics that
have only been briefly outlined here. |
| |
| Liebetrau, A.L. (1983) Measures
of Association. Beverly Hills, CA: Sage. |
| This is a definitive and accessible
presentation of measures of association, including their
calculation and respective limitations |
| |
| Sterne, J.A.C. and Smith, G.D. (2001)
‘Sifting the Evidence - What's Wrong with Significance
Tests?’, British Medical Journal, 322: 226-31.
|
| This is a short and cogent discussion
of the logic and history of hypothesis testing, and its
limitations. |
| |
|
|
| |
| |
| |
© Sage Publications Ltd.
