Statistical methods in SUPPORT.

The analysis and interpretation of the data collected in SUPPORT provide great potential for understanding the relationships among treatment choices, patient and physician values and preferences, perceptions about the risks and benefits of treatments, institutional characteristics, and outcomes (as measured by quality of life, survival, and satisfaction). The complicated analyses required to elucidate these relationships will pose many technical challenges in dealing with longitudinal observational data collected from seriously ill patients at multiple sites. Major challenges include the handling of incomplete data, proper parameterization of treatment effects, strategies to avoid various potential biases, validating predictive models, and constructing endpoints that combine survival with quality of life. Within the structure of the SUPPORT study, mechanisms have been established to guide the analyses and to ensure their quality and validity.

Duke Scholars

Author Lawrence H. Muhlbaier Biostatistics & Bioinformatics, Division of Biostatistics