Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘How to report t-test resultsIt is preferable to report the P-value itself and not to report, for example, ‘P < 0.05’or ‘P = not significant’ as this does not give the reader an idea of the magnitudeof the statistical significance. The treatment effect and CI should be reported(where possible) in order to give the reader an idea of the uncertainty surroundingthe estimate of the difference(s) and the clinical significance of the difference(s).Furthermore, if performing a two-sample t-test for a difference in means, it isdesirable to quote summary statistics for each sample (number of observations,mean, and standard deviation or standard error), rather than just quoting theP-value. Equally, for a paired t-test, summary statistics for the differences betweenpairs should be presented.How to make multiple group comparisonsIt often happens in research practice that we need to compare more than twogroups (eg, drug 1, drug 2, and placebo). In these cases, we need to analyze thedata using regression modeling techniques (see Chapter 24). These includeanalysis of variance (ANOVA), which can be considered a generalization of thet-test [1,3,4]. In fact, for two-group comparisons, regression analysis will giveresults identical to a t-test [1]. However, when the study design is more complex,regression modeling offers advantages over the t-test and can avoid the problemof conducting multiple statistical tests (see Chapter 29).ConclusionIn this chapter, we have introduced significance test methods and correspondingCI calculations for the analysis of continuous data (summarized in Table 7).Of those methods, the t-test has been widely used in data analysis and has twoimportant applications in clinical research:• to assess if there is a statistically significant change after treatmentin an endpoint from baseline within a treatment group• to assess if there is a significant difference between two treatment groupsFor both forms of the t-test, the test statistic is calculated by comparing the ratioof the mean difference (or difference in means) to its standard error with a criticalt-value from a t-distribution with an appropriate number of degrees of freedom.For large samples, the Z-test can be used to replace the t-test.213