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    The Validity of Summary Comorbidity Measures

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    Genre
    Thesis/Dissertation
    Date
    2016
    Author
    Gilbert, Elizabeth
    Advisor
    Krafty, Robert T.
    Egleston, Brian
    Department
    Statistics
    Subject
    Statistics
    Causal Inference
    Confounding Adjustment
    Prognostic Scores
    Summary Comorbidity Measures
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/1300
    
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    DOI
    http://dx.doi.org/10.34944/dspace/1282
    Abstract
    Prognostic scores, and more specifically comorbidity scores, are important and widely used measures in the health care field and in health services research. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. A comorbidity score is a summary score that can be created from these individual comorbidities for prognostic purposes, as well as for confounding adjustment. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. This dissertation explores the use of summary comorbidity measures in statistical models. Three particular aspects are examined. First, it is shown that, under standard conditions, the predictive ability of these summary comorbidity measures remains as accurate as the individual comorbidities in regression models, which can include factors such as treatment variables and additional covariates. However, these results are only true when no interaction exists between the individual comorbidities and any additional covariate. The use of summary comorbidity measures in the presence of such interactions leads to biased results. Second, it is shown that these measures are also valid in the causal inference framework through confounding adjustment in estimating treatment effects. Lastly, we introduce a time dependent extension of summary comorbidity scores. This time dependent score can account for changes in patients' health over time and is shown to be a more accurate predictor of patient outcomes. A data example using breast cancer data from the SEER Medicare Database is used throughout this dissertation to illustrate the application of these results to the health care field.
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