Clinical Trials

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❘❙❚■ Chapter 24 | Regression Analysis1. Adjusting for differences in baseline characteristicsIn a randomized clinical trial, if the randomization has created perfectly identicalgroups then the treatment groups will be equal in terms of both known andunknown factors. If this is the case then any association between baselinecharacteristics and the treatment will be balanced, and thus no confounding effectwill need be adjusted for. A simple unadjusted test to estimate the treatmenteffect can then be used. However, despite randomization, treatment groups cansometimes be different with respect to some variables that are associated with theoutcome variable and treatment. Under such circumstances, adjusted analysis forthe baseline differences in these variables may become necessary (see Chapter 25for more about covariate adjustment analysis).2. Identifying the predictors of an outcome variableThis is the most popular use of regression analysis in clinical research. Usingmultiple regression analysis, we can describe the extent, direction, and strength ofthe relationship between several independent variables and a dependent variable.The two examples used in the previous sections fall largely into this category [2, 3].The sign of b kindicates the direction of the effect of predictor x kon the outcomestatistic being modeled (the mean value of the outcome variable if the outcome isa continuous variable; the log of the odds of the outcome if the outcome is abinary variable; the log of the hazard of the outcome if the outcome is a time toevent), whereas the value of b k(or e b k) measures the magnitude of its effect.The CI for b k(or e b k) gives a range for the true population value, and the P-valueis a measure of the strength of evidence for the effect. In the case of a linearregression analysis, a positive b kimplies a positive (or increasing) relationshipbetween x kand the continuous outcome variable, while negative values wouldsuggest a protective effect of the baseline and outcome variable. For logistic andhazards regression, e b k< 1 (e b k> 1) suggests that increasing x kis associated withdecreasing (increasing) the odds or hazard of having an outcome.3. Identifying prognostic factors while controlling forpotential confoundersWith advances in medical research, we have learned more about the multifactorialnature of many diseases. In the case of, eg, coronary artery disease (CAD), manyrisk factors have been identified through epidemiological studies and clinical trials,such as smoking, high blood pressure, and high cholesterol. If we want to assess theeffect of a new study variable on the occurrence of CAD, we need to adjust theanalysis for risk factors that are already established as predictors of the disease.Wei et al. conducted prospective cohort studies among 40,069 men and women toinvestigate the association between fasting plasma glucose levels, cardiovascular282
<strong>Clinical</strong> <strong>Trials</strong>: A Practical Guide ■❚❙❘disease, and all-cause mortality using a Cox proportional hazards model [4].After multivariate adjustment for age, gender, study population, ethnicity,current smoking status, high blood pressure, total cholesterol, BMI, triglycerides,history of cardiovascular disease and cancer, and a family history of prematurecardiovascular disease, patients with fasting plasma glucose
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❘❙❚■ ContributorsStephen L
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Clinical Trials: A Practical Guide
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❘❙❚■ Chapter 2 | Uncontroll
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■■❚❙❘Part IIAlternativeTr
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❘❙❚■ Chapter 15 | Cluster R
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❘❙❚■ Chapter 16 | Multicent
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■■❚❙❘Part IIIBasics ofSta
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■■❚❙❘Chapter 17Types of D
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■■❚❙❘Chapter 19Comparison
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■■❚❙❘Chapter 20Comparison
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■■❚❙❘Chapter 32Overview o
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■■❚❙❘Chapter 33Trial Prof
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■■❚❙❘Chapter 35Use of Tab
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■■❚❙❘GlossaryGlossary453
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■■❚❙❘IndexIndex467
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Clinical Trials

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