On the use of summary comorbidity measures for prognosis and survival treatment effect estimation

Prognostic scores have been proposed as outcome based confounder adjustment scores akin to propensity scores. However, prognostic scores have not been widely used in the substantive literature. Instead, comorbidity scores, which are limited versions of prognostic scores, have been used extensively by clinical and health services researchers. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. Comorbidity scores are used to reduce the dimension of a vector of comorbidity variables into a single scalar variable. Such scores are often added to regression models with other non-comorbidity variables such as age and sex, both for analyzing prognosis and for confounder adjustment when analyzing treatment effects. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. In this article, we show that under relatively standard assumptions, comorbidity scores can have equal prognostic and confounder-adjustment abilities as the individual comorbidity variables, but that biases can occur if there are additional effects, such as interactions, of covariates beyond that captured by the comorbidity score. Simulations were performed to illustrate empirical properties and a data example using breast cancer data from the SEER Medicare Database demonstrates the application of these results.