2015
DOI: 10.1002/sim.6740
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Flexible estimation of survival curves conditional on non‐linear and time‐dependent predictor effects

Abstract: Prognostic studies often estimate survival curves for patients with different covariate vectors, but the validity of their results depends largely on the accuracy of the estimated covariate effects. To avoid conventional proportional hazards and linearity assumptions, flexible extensions of Cox's proportional hazards model incorporate non-linear (NL) and/or time-dependent (TD) covariate effects. However, their impact on survival curves estimation is unclear. Our primary goal is to develop and validate a flexib… Show more

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Cited by 11 publications
(31 citation statements)
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“…Here we assumed a proportional-hazards spline model where time-dependent effects were not considered. This may also have caused bias in the risk estimates ( 63 ) of prosthesis revision. The flexible model can be further extended for possible improvement in fit by adding terms for interactions between covariates and the effect of time ( 27 ).…”
Section: Discussionmentioning
confidence: 99%
“…Here we assumed a proportional-hazards spline model where time-dependent effects were not considered. This may also have caused bias in the risk estimates ( 63 ) of prosthesis revision. The flexible model can be further extended for possible improvement in fit by adding terms for interactions between covariates and the effect of time ( 27 ).…”
Section: Discussionmentioning
confidence: 99%
“…Although alternative methods exist for analyzing non-proportional time-to-event data, much of which has been done in a Cox setting (Gray 1992;Hastie and Tibshirani 1993;Grambsch and Therneau 1994;Abrahamowicz, MacKenzie, and Esdaile 1996;Abrahamowicz and MacKenzie 2007;Wynant and Abrahamowicz 2015), here we concentrate on assessing the ability of flexible parametric survival models in capturing time-dependent effects. Under proportional hazards, flexible parametric survival models have been shown to be able to more accurately capture complex hazard functions than standard parametric models and provide unbiased estimates of hazard ratios (Rutherford, Crowther, and Lambert 2015), but no assessment under non-proportionality has been done.…”
Section: Introductionmentioning
confidence: 99%
“…Accordingly, study events were (by definition) interval‐censored. To ensure that our use of the customized flexible modeling program was appropriate in this context, sensitivity analyses comparing estimates from conventional vs . interval‐censored Cox PH models were first conducted and results did not differ materially (data not shown).…”
Section: Discussionmentioning
confidence: 99%
“…Yet, inappropriate use of this model may lead to biased results, inaccurate risk prediction, and reduced statistical power . Therefore, if one or both of the PH and linearity assumptions were rejected in our analyses, nonproportional (i.e., time‐dependent (TD)) and/or nonlinear (NL) effects were modeled using the flexible generalization of the Cox PH model . The insights that stand to be gained by modeling these effects are of clinical relevance .…”
Section: Introductionmentioning
confidence: 99%