Statistical analysis

This section contains the following:

Introduction
Things to consider
Additional resources
Further reading

Introduction

The principal features of the statistical analysis of the trial should have been described in the Statistical Analysis section of the protocol. This section should include details of the proposed analysis of both the primary and secondary outcome(s) and provide some information on how any anticipated analysis problems will be handled. In general, the analysis of the primary outcome(s) should follow the intention to treat principle – the participants should remain in the group to which they were randomised and not analysed according to the treatment actually received. Any novel statistical techniques should be referenced in the text. A statistician should write or comment on the analysis strategy.

In addition to the Statistical Analysis section of the protocol, it is necessary to describe a statistical analysis plan. This is usually written as a separate document that is appended to the protocol. The plan includes a more detailed description of the principal statistical features stated in the protocol. This plan can be finalised as the trial progresses, and might require amendment if unusual features of the data are identified during the analyses. This may occur when interim results are presented to the Data Monitoring Committee (DMC).

Things to consider

Provide details of the proposed analysis for the primary and secondary outcome(s)
Give references for any non-standard statistical techniques
Supply information on how to handle anticipated problems such as missing values (see Compliance and missing data)
Any proposed analyses should correspond to the data required for the Dummy tables (see Dummy tables)
Get a statistician involved with the development of this section

Scenarios where special considerations apply

Cluster trials

A fundamental assumption of standard statistical methods used to analyse patient randomised trials is that the outcome for an individual patient is completely unrelated to that for any other patient – they are said to be ‘independent’. This assumption is violated, however, in cluster trials because patients within any one cluster are more likely to respond in a similar manner. Cluster trials that do not account for clustering during analysis have artificially extreme p-values and over narrow confidence intervals increasing the chances of spuriously significant findings and misleading conclusions. The statistical analysis section of the protocol should provide specific details on how to account for the clustering in the data.

Equivalence trials

In equivalence trials, the statistical analysis is generally based on the use of confidence intervals. The investigator sets equivalence margins, and if the entire confidence interval lies within these margins the interventions are said to be equivalent. In contrast to the parallel group designs described above, the correct analysis is by treatment received not by intention to treat. The protocol should state the equivalence margins and specific details on how to analyse the equivalence trial.

Additional resources

Statistical analysis checklist

This checklist was developed by Dave Sackett, who prepared it for the forthcoming 3rd edition of Clinical Epidemiology; A Basic Science for Answering Questions about Health Care, to be published by Lippincott, Williams & Wilkins in 2004.

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ICH Harmonised Tripartite Guideline: Statistical Principles for Clinical Trials

This document provides guidance for the design, conduct, analysis, and evaluation of clinical trials of an intervention in the context of its overall clinical development.

Dummy tables checklist

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Epi Info

Epi Info is a public domain software package designed for the global community of public health practitioners and researchers. It provides for easy form and database construction, data entry, and analysis with epidemiologic statistics, maps, and graphs. Within Epi Info there is an analysis program for producing statistical analyses of data, report output and graphs.