Clinical Trials

❘❙❚■ Chapter 26 | Confoundingmeasured outcome. Once confounders are identified, the next stage is to controlfor these in statistical analysis so that unbiased results can be obtained. There aretwo methods for this: stratification and regression modeling [1,2].StratificationThe simplest way to control for a confounding factor is to perform the analysiswithin each stratification of the confounder and then to calculate a summarymeasure from these strata-specific estimators using a suitable weighting scheme.The most common way of performing such a stratification analysis is to useMantel–Haenszel methods, which adjust for a categorical confounding factor onthe relationship between treatment and some binary outcome [1]. UsingMantel–Haenszel methods on the data from our earlier HRT example (Tables 1and 2), the Mantel–Haenszel estimate of the odds ratio adjusted forsocioeconomic class is calculated as 1.00 (95% CI 0.67, 1.48; P = 1.00). Theadjusted result suggests that, in this example, HRT does not result in improvedmental function in postmenopausal women.Regression modelingAs the number of potential confounders or number of possible sub-strata fora confounder increases, controlling confounding through stratification presentsproblems. This is because unless the overall sample size is large, each stratum willcontain only a very small number of patients. In this case, a multivariableregression model approach is the preferred analysis method. There are a numberof specific regression models, and the most appropriate technique will depend onthe type of data to be analyzed. For example:• A linear regression model is most suitable for a continuous outcomemeasure such as blood pressure.• A logistic regression model is the preferred option for a binary outcomesuch as the occurrence of death.• A Cox regression model should be used for a time-to-event outcomesuch as time to next seizure or pain episode.The regression model is a very powerful technique that allows for the estimationof the effects of a treatment and a whole range of prognostic factors, each oneadjusted for the potential confounding effect of the others. For the HRT example,using the logistic regression model to adjust for confounding produces identicalresults to the Mantel–Haenszel method.302