Stagewise pseudo-value regression for time-varying effects on the cumulative incidence
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Stagewise pseudo-value regression for time-varying effects on the cumulative incidence. / Zöller, Daniela; Schmidtmann, Irene; Weinmann, Arndt; Gerds, Thomas A.; Binder, Harald.
In: Statistics in Medicine, Vol. 35, No. 7, 30.03.2016, p. 1144-1158.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Stagewise pseudo-value regression for time-varying effects on the cumulative incidence
AU - Zöller, Daniela
AU - Schmidtmann, Irene
AU - Weinmann, Arndt
AU - Gerds, Thomas A.
AU - Binder, Harald
N1 - Copyright © 2015 John Wiley & Sons, Ltd.
PY - 2016/3/30
Y1 - 2016/3/30
N2 - In a competing risks setting, the cumulative incidence of an event of interest describes the absolute risk for this event as a function of time. For regression analysis, one can either choose to model all competing events by separate cause-specific hazard models or directly model the association between covariates and the cumulative incidence of one of the events. With a suitable link function, direct regression models allow for a straightforward interpretation of covariate effects on the cumulative incidence. In practice, where data can be right-censored, these regression models are implemented using a pseudo-value approach. For a grid of time points, the possibly unobserved binary event status is replaced by a jackknife pseudo-value based on the Aalen-Johansen method. We combine a stagewise regression technique with the pseudo-value approach to provide variable selection while allowing for time-varying effects. This is implemented by coupling variable selection between the grid times, but determining estimates separately. The effect estimates are regularized to also allow for model fitting with a low to moderate number of observations. This technique is illustrated in an application using clinical cancer registry data from hepatocellular carcinoma patients. The results are contrasted with traditional hazard-based modeling. In addition to a more straightforward interpretation, when using the proposed technique, the identification of time-varying effect patterns on the cumulative incidence is seen to be feasible with a moderate number of observations.
AB - In a competing risks setting, the cumulative incidence of an event of interest describes the absolute risk for this event as a function of time. For regression analysis, one can either choose to model all competing events by separate cause-specific hazard models or directly model the association between covariates and the cumulative incidence of one of the events. With a suitable link function, direct regression models allow for a straightforward interpretation of covariate effects on the cumulative incidence. In practice, where data can be right-censored, these regression models are implemented using a pseudo-value approach. For a grid of time points, the possibly unobserved binary event status is replaced by a jackknife pseudo-value based on the Aalen-Johansen method. We combine a stagewise regression technique with the pseudo-value approach to provide variable selection while allowing for time-varying effects. This is implemented by coupling variable selection between the grid times, but determining estimates separately. The effect estimates are regularized to also allow for model fitting with a low to moderate number of observations. This technique is illustrated in an application using clinical cancer registry data from hepatocellular carcinoma patients. The results are contrasted with traditional hazard-based modeling. In addition to a more straightforward interpretation, when using the proposed technique, the identification of time-varying effect patterns on the cumulative incidence is seen to be feasible with a moderate number of observations.
U2 - 10.1002/sim.6770
DO - 10.1002/sim.6770
M3 - Journal article
C2 - 26510388
VL - 35
SP - 1144
EP - 1158
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 7
ER -
ID: 157490992