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Ann Thorac Surg 2005;79:1104-1109
© 2005 The Society of Thoracic Surgeons

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The statistician's page

Quantitative Methods for Tracking Cognitive Change 3 Years After Coronary Artery Bypass Surgery

^a Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
^b Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
^c Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland
^e Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland
^d Zanvyl Krieger Mind Brain Institute, The Johns Hopkins University, Baltimore, Maryland

^_* Address reprint requests to Ms Barry, Department of Biostatistics, The Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD 21205 (E-mail: sbarry{at}jhsph.edu).

The analysis and interpretation of change in cognitive function test scores after coronary artery bypass grafting (CABG) present considerable statistical challenges. Application of hierarchical linear statistical models can estimate the effects of a surgical intervention on the time course of multiple biomarkers.

We use an "analyze then summarize" approach whereby we estimate the intervention effects separately for each cognitive test and then pool them, taking appropriate account of their statistical correlations. The model accounts for dropouts at follow-up, the chance of which may be related to past cognitive score, by implicitly imputing the missing data from individuals' past scores and group patterns. We apply this approach to a study of the effects of CABG on the time course of cognitive function as measured by 16 separate neuropsychological test scores, clustered into 8 cognitive domains. The study includes measurements on 140 CABG patients and 92 nonsurgical controls at baseline, and at 3, 12, and 36 months.

Our "analyze then summarize" method allows us to identify differences between the treatment groups in individual tests as well as in aggregate measures. It takes into account the correlation structure of the data and thereby produces more precise results than summarizing before analyzing.

The methods used have application to a wide range of intervention studies in which multiple biomarkers are followed over time to quantify health effects. Software to implement the methods in the R statistical package is available from the authors at http://www.biostat.jhsph.edu/sbarry/software/ATSrcode.pdf.

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