# 21: Methods of analysis

• 21.1: Introduction to methods of analysis
This chapter describes simple statistical methods that are likely to be most useful for the basic analysis of intervention trials. Usually, a statistician will be closely involved in the design and analysis of a trial, and the more advanced analytical techniques that they might employ are not covered in this chapter. For more information on such techniques, the reader is referred to statistical texts such as Armitage and Berry (1987), Kirkwood and Sterne (2003), and Rothman et al. (2008). Howeve
• 21.2: Basics of statistical inference
The appropriate method of statistical analysis depends on the type of outcome measure that is of interest. An outcome in an intervention study can usually be expressed as a proportion, rate, or mean. For example, in a trial of a modified vaccine, an outcome measure of interest may be the proportion of vaccinated subjects who develop a protective level of antibodies.
• 21.3: Statistical analysis plan
A common mistake in the planning of a trial is to delay consideration of the analyses until the data become available. It is essential that the main analyses that will be undertaken are planned at the design stage, as this provides several major benefits. First, it encourages a clearer understanding of the basic questions to be answered and thus assists with the formulation of clear and specific objectives. For example, in a vaccine trial, a simple comparison of the numbers of cases of the disea
• 21.4: Analysis of proportions
Methods appropriate for the analysis of proportions are used when the outcome of interest is a binary (‘yes/no’) variable
• 21.5: Analysis of rates
The terms ‘risk’ and ‘rate’ are often used rather loosely and interchangeably to describe the frequencies of events in epidemiological studies.
• 21.6: Analysis of mean values
If the outcome measure is taken as the mean (−x) of a sample of n observations, for example, the weights of a sample of newborn infants, the standard error of the mean is given by σ/√n, where σ is the standard deviation of the variable measured
• 21.7: Controlling for Confounding Variables
A risk factor for the disease under study that is differentially distributed among the groups receiving different interventions in which the disease incidence is being compared is called a confounding factor.
• 21.8: Analyses when communities have been randomized
In some intervention studies, communities, rather than individuals, are used as the unit of randomization. If this has been done, it is inappropriate to base analyses on responses of individuals, ignoring the fact that randomization was over larger units.
• 21.9: Prevented fraction of disease
The objective of most field trials is to measure the effect of an intervention in reducing disease rates. The results of such studies may be used to estimate the impact that an intervention might have on disease rates if it was introduced into a public health programme. In such circumstances, the overall effect is much influenced by the coverageachieved by the programme.
• References
Armitage, P. and Berry, G. 1987. Statistical methods in medical research. Oxford: Blackwell Scientific. Breslow, N. E. and Day, N. E. 1980. Statistical methods in cancer research. Volume 1. The analysis of case-control studies. Lyon: International Agency for Research on Cancer.