Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘that the NYHA distribution in the active treatment group is not significantlydifferent from that in the placebo group.ConclusionIn this chapter, we have described statistical methods for the analysis ofcategorical data, focusing mainly on the analysis of 2 × 2 tables (see Table 7).Generally, a chi-squared test can be used to analyze any r × c contingency table,involving the calculation of the expected numbers of frequencies in each cell andthen comparing these to the observed numbers. The purpose of the chi-squaredtest is to assess whether the percentage distributions differ among differentgroups. The limitation of a chi-squared test is that it does not produce a pointestimate or CI for the treatment effect.To quantify the treatment effect in a clinical trial, the risk difference, risk ratio,or odds ratio for a binary endpoint can be used to address different researchquestions. It should be noted that the latter two are different measures, exceptwhen the outcome event is rare.It is important to appreciate how proportions or percentages of outcomes arecompared in a trial so that we can be clear about when to use terms such as ‘oddsratios’ and ‘risk ratios’. These statistical tools are appropriate in specific situations:an odds ratio is useful for summarizing treatment effects in systematic reviews anddetermining the epidemiology risk of a particular exposure, while a risk ratio isuseful in a balanced trial to represent the benefit of a treatment exposure.References1. Pocock SJ. Clinical Trials: A Practical Approach. New York: John Wiley & Sons, 1983.2. Altman DG. Practical Statistics for Medical Research. London: Chapman & Hall, 1999.3. Kirkwood B, Sterne J. Essential Medical Statistics, 2nd edition. Oxford: Blackwell Publishing, 2003.233