Clinical Trials

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❘❙❚■ Chapter 24 | Regression AnalysisTable 3. Hazard ratios and 95% confidence intervals (CIs) for the effects of baseline characteristics onmortality in the long-term PRAIS (Prospective Registry of Acute Ischaemic Syndromes in the UK) follow-upstudy (653 patients): Cox regression analysis [3].Variable Hazard ratio 95% CI P-valueAge70 years 4.88 2.62, 9.06
<strong>Clinical</strong> <strong>Trials</strong>: A Practical Guide ■❚❙❘Age, gender, SBP, heart rate, prior heart failure, prior stroke, and ECG changeswere found to be significantly associated with the hazard (risk) of death in thelong-term follow-up period. For example, the risk of death for a patient with priorheart failure was 2.41 times that for a patient without a history of heart failure; the95% CI for this relative risk was 1.60, 3.63. Males had a risk of 1.78 (95% CI 1.22,2.59; P = 0.003) compared with females.Age was treated as a categorical variable in the analysis with three categories:70 years. For a categorical variable, one category must bechosen as the reference, while the other categories are compared with this groupin the multivariate analysis. The choice of reference category in a study is usuallybased on the main hypothesis being tested. For example, in the PRAIS-UKanalysis, it was expected that the youngest age group would have the lowest risk ofdeath, so that was chosen as the reference group.The interpretation of results for a categorical variable is similar to that for a binaryvariable. Taking the age effect as an example, the results in Table 3 suggest that,compared with patients aged 70 years is 2.29 (95% CI 1.18, 4.44; P < 0.014) and 4.88(95% CI 2.62, 9.06; P < 0.001), respectively. SBP is a continuous variablemeasured in units of 10 mm Hg, so the hazard ratio of 0.94 means that for every10 mm Hg increase, the risk of death is multiplied by 0.94. In other words, the riskis decreased by 6% ([1 – 0.94] × 100).Five most common uses of regression modelsin clinical researchMultiple regression models have a variety of uses in clinical research. The fivemost common uses are to:• adjust for differences in baseline characteristics• identify the predictors of an outcome variable• identify prognostic factors while controlling for potential confounders• determine prognosis (prognostic models)• determine diagnosis (diagnostic models)These five uses are related, and many studies will use multiple regression forseveral or all of these purposes. We discuss them separately below.281
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❘❙❚■ ContributorsStephen L
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Clinical Trials: A Practical Guide
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Clinical Trials

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