02407nas a2200253 4500008004100000022001400041245016800055210006900223260001500292300000700307490000700314520158100321100002401902700001901926700002201945700002101967700001901988700002302007700002002030700002202050700002002072700002502092856003602117 2020 eng d a1471-228800aIncorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis.0 aIncorporating sampling weights into robust estimation of Cox pro c2020 03 14 a620 v203 a

BACKGROUND: Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations.

METHODS: Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights.

RESULTS: Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke.

CONCLUSIONS: Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.

1 aSitlani, Colleen, M1 aLumley, Thomas1 aMcKnight, Barbara1 aRice, Kenneth, M1 aOlson, Nels, C1 aDoyle, Margaret, F1 aHuber, Sally, A1 aTracy, Russell, P1 aPsaty, Bruce, M1 aDelaney, Joseph, A C uhttps://chs-nhlbi.org/node/8405