| 1. |
- Bellavia, Andrea, et al.
(författare)
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Using Laplace Regression to Model and Predict Percentiles of Age at Death When Age Is the Primary Time Scale
- 2015
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Ingår i: American Journal of Epidemiology. - : OXFORD UNIV PRESS INC. - 0002-9262 .- 1476-6256. ; 182:3, s. 271-277
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Tidskriftsartikel (refereegranskat)abstract
- Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies.
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| 2. |
- Orsini, Nicola, et al.
(författare)
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Interaction Analysis Based on Shapley Values and Extreme Gradient Boosting : A Realistic Simulation and Application to a Large Epidemiological Prospective Study
- 2022
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Ingår i: Frontiers in Nutrition. - : Frontiers Media S.A.. - 2296-861X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundSHapley Additive exPlanations (SHAP) based on tree-based machine learning methods have been proposed to interpret interactions between exposures in observational studies, but their performance in realistic simulations is seldom evaluated. MethodsData from population-based cohorts in Sweden of 47,770 men and women with complete baseline information on diet and lifestyles were used to inform a realistic simulation in 3 scenarios of small (ORM = 0.75 vs. ORW = 0.70), moderate (ORM = 0.75 vs. ORW = 0.65), and large (ORM = 0.75 vs. ORW = 0.60) discrepancies in the adjusted mortality odds ratios conferred by a healthy diet among men and among women. Estimates were obtained with logistic regression (L-ORM; L-ORW) and derived from SHAP values (S-ORM; S-ORW). ResultsThe sensitivities of detecting small, moderate, and large discrepancies were 28, 83, and 100%, respectively. The sensitivities of a positive sign (L-ORW > L-ORM) in the 3 scenarios were 93, 100, and 100%, respectively. Similarly, the sensitivities of a positive discrepancy based on SHAP values (S-ORW > S-ORM) were 86, 99, and 100%, respectively. ConclusionsIn a realistic simulation study, the ability of the SHAP values to detect an interaction effect was proportional to its magnitude. In contrast, the ability to identify the sign or direction of such interaction effect was very high in all the simulated scenarios.
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