Efficient semiparametric mixture inferences on cure rate models for competing risks

Choi, Sangbum; Huang, Xuelin; Cormier, Janice N.

doi:10.1002/cjs.11256

Cited by 9 publications

(10 citation statements)

References 20 publications

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“…In this situation, analysis using the proportional subdistribution hazards model (Fine & Gray, 1999) could be misleading, as it cannot facilitate the crossing of the subdistribution functions. The mixture approach (Choi & Huang, 2015) can fit such a data pattern but has some complexity in interpreting the results. Alternatively, we applied the quantile regression model that can evaluate the conditional treatment effects orderly over a set of different quantiles.…”

Section: Analysis Of Soft Tissue Sarcoma Datamentioning

confidence: 99%

Smoothed quantile regression analysis of competing risks

2018

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Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the L -type convex function or solving the nonsmoothed estimating equations. This approach could lead to multiple roots in practical settings, particularly with multiple covariates. Moreover, variance estimation involves an unknown error distribution and most methods rely on computationally intensive resampling techniques such as bootstrapping. We consider the induced smoothing procedure for censored quantile regressions to the competing risks setting. The proposed procedure permits the fast and accurate computation of quantile regression parameter estimates and standard variances by using conventional numerical methods such as the Newton-Raphson algorithm. Numerical studies show that the proposed estimators perform well and the resulting inference is reliable in practical settings. The method is finally applied to data from a soft tissue sarcoma study.

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Section: Analysis Of Soft Tissue Sarcoma Datamentioning

confidence: 99%

Smoothed quantile regression analysis of competing risks

2018

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“…When a cure fraction is present, several authors have casted the mixture competing risks model of Larson and Dinse 22 into the cure framework. For example, Choi et al 23,24 extended the classical mixture cure model by treating the cure outcome and competing failures as distinct outcomes, all of which are fitted by multinomial logistic regression model. Given the failure type, the conditional distribution of the failure time are specified.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric mixture cure model analysis with competing risks data: Application to vascular access thrombosis data

Chen¹,

Shen²,

Lin

et al. 2020

Statistics in Medicine

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The article is motivated by a nephrology study in Taiwan, which enrolled hemodialysis patients who suffered from vascular access thrombosis. After treatment, some patients were cured of thrombosis, while some may experience recurrence of either type (acute or nonacute) of vascular access thrombosis. Our major interest is to estimate the cumulative incidence probability of time to the first recurrence of acute thrombosis after therapy. Since the occurrence of one type of vascular access thrombosis precludes occurrence of the other type, patients are subject to competing risks. To account for the presence of competing risks and cured patients, we develop a mixture model approach to the regression analysis of competing‐risks data with a cure fraction. We make inference about the effects of factors on both the cure rate and cumulative incidence function (CIF) for a failure of interest, which are separately specified in the logistic regression model and semiparametric regression model with time‐varying and time‐invariant effects. Based on two‐stage method, we develop novel estimation equations using the inverse probability censoring weight techniques. The asymptotic properties of the estimators are rigorously studied and the plug‐in variance estimators can be obtained for constructing interval estimators. We also propose a lack‐of‐fit test for assessing the adequacy of the proposed model and several tests for time‐varying effects. The simulation studies and vascular access thrombosis data analysis are conducted to illustrate the proposed method.

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“…Our preliminary simulation studies show that the KP estimator provides a more accurate estimation of cure rates than the Breslow estimator. These results may be used to check the goodness-of-fit of cure rate models [ 22 , 23 ], which warrants further research.…”

Section: Discussionmentioning

confidence: 99%

Empirical Comparison of the Breslow Estimator and the Kalbfleisch Prentice Estimator for Survival Functions

2018

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When analyzing time-to-event data in a non-parametric setting without considering covariates, the Kaplan-Meier estimator is widely used to estimate the survival function. When considering covariates, the Cox proportional hazards model is widely used to account for covariates effects. In this setting, for the baseline survival function, the most commonly used approach is the Breslow method, which estimates the baseline survival function as an exponential function of the cumulative baseline hazard function. However, an unnatural and undesirable feature of the Breslow estimator is that, its estimated survival probability will never reaches zero even if the last observation is an event. In this article, we consider an less commonly used alternative, the Kalbfleisch Prentice estimator for the baseline survival function. It is the counterpart of the Kaplan-Meier estimator in a setting with covariates, and thus similarly as the Kaplan Meier estimator, it will reach zero if the last observation is an event. To evaluate the usefulness of the Kalbfleisch Prentice estimator and its relative performance comparing with the Breslow estimator, we conduct simulation studies across a range of conditions by varying the true survival time distribution, sample size, censoring rate and covariate values. We compare the performance of the two estimators regarding bias, mean squared error and relative mean squared error. In most situations in our study, the Kalbfleisch Prentice estimator results in less bias and smaller mean squared error than the Breslow estimator. Their differences are especially clear at the tail of the distribution. The implications of such differences in applications are discussed. We advocate the use of Kalbfleisch Prentice estimator in practice, and further research on its properties.

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Efficient semiparametric mixture inferences on cure rate models for competing risks

Cited by 9 publications

References 20 publications

Smoothed quantile regression analysis of competing risks

Smoothed quantile regression analysis of competing risks

Semiparametric mixture cure model analysis with competing risks data: Application to vascular access thrombosis data

Empirical Comparison of the Breslow Estimator and the Kalbfleisch Prentice Estimator for Survival Functions

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