Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘Table 1. Data from a hypothetical, randomized clinical trial, conducted to assess the administration of a newagent to reduce systolic blood pressure (SBP) among people aged between 60 and 70 years.Subject Age Gender SBP Heart rate Chest pain symptoms Race Treatment CAD CAD-free(years) (mm Hg) (bpm) present? time (days)1 65.9 Male 189 62 No chest pain White Intervention Yes 1,6572 65.2 Female 207 60 Nonanginal pain White Control Yes 2833 66.8 Male 152 80 Atypical chest pain Asian Control Yes 1884 63.7 Male 154 60 Typical chest pain Asian Intervention No 1,6575 69.8 Male 158 65 No chest pain Other Control Yes 1,2576 68.1 Male 177 88 Nonanginal pain White Intervention No 2287 63.5 Female 99 100 Atypical chest pain Other Control No 1,6578 63.3 Male 153 94 No chest pain White Control Yes 8279 67.2 Male 123 72 Nonanginal pain Asian Intervention No 1,65610 68.6 Female 120 72 Atypical chest pain White Intervention No 1,027bpm = beats per minute; CAD = coronary artery disease.The next concept we shall consider is that there might be relationships betweenthese variables.What are dependent and independent variables?Multivariate regression techniques (see Chapter 24) are tools to explorerelationships among a set of variables, particularly when at least three variablesare involved. In regression analysis, one variable (the dependent variable) isusually taken to be the response or outcome variable, to be predicted by the othervariables. These other variables are called predictor or explanatory variables or,sometimes, independent variables because multivariate regression analysis aims toseparate the independent contribution of each of these variables to the outcomevariable. For example, if we are interested in predicting the likelihood of a patienthaving CAD, then the CAD variable (‘yes’ or ‘no’) is the response variable,whereas age, gender, SBP, heart rate, chest pain symptoms, race, and smokingstatus may all be predictors or independent variables. If the relationship betweenthese variables and CAD is strong then one can confidently predict the likelihoodof being CAD-‘yes’ or CAD-‘no’ given the other variable values.169