npcure: An R Package for Nonparametric Inference in Mixture Cure Models

López-Cheda, Ana; Jácome, María Amalia; López-de-Ullibarri, Ignacio

doi:10.32614/rj-2021-027

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…And, R package “npcure” in López‐Cheda et al. (2021) can be applied to compute the suitable bandwidth, where the cross‐validation bandwidth is returned by “berancv()” for the Beran estimator and the bootstrap bandwidth can be computed by the function “probcurehboot()” for the cure rate estimator. In addition, the simulation results in Section 4 show that the performance of the proposed estimators is not sensitive to the choice of bandwidth.…”

Section: Theoretical Propertiesmentioning

confidence: 99%

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment

Wang,

Wang

2023

Biometrical J

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In observational studies, covariates are often confounding factors for treatment assignment. Such covariates need to be adjusted to estimate the causal treatment effect. For observational studies with survival outcomes, it is usually more challenging to adjust for the confounding covariates for causal effect estimation because of censoring. The challenge becomes even thornier when there exists a nonignorable cure fraction in the population. In this paper, we propose a causal effect estimation approach in observational studies for survival data with a cure fraction. We extend the absolute treatment effects on survival outcomes—including the restricted average causal effect and SPCE—to survival outcomes with cure fractions, and construct the corresponding causal effect estimators based on propensity score stratification. We prove the asymptotic properties of the proposed estimators and conduct simulation studies to evaluate their performances. As an illustration, the method is applied to a stomach cancer study.

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Section: Theoretical Propertiesmentioning

confidence: 99%

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment

Wang,

Wang

2023

Biometrical J

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“…Generalized gamma distributions with a cured component as discussed in Jackson (2016) and Amdahl (2020) fit the data quite well (Figure 3) and can be used for further analysis. For an alternative nonparametric fitting, see López‐Cheda, Amalia & de Ullibarri (2021). See Conde‐Amboage, Van Keilegom & González‐Manteiga (2021) and their references for goodness‐of‐fit tests for parametric models with censored data.…”

Section: Data Examplementioning

confidence: 99%

Finite sample and asymptotic distributions of a statistic for sufficient follow‐up in cure models

2023

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The existence of immune or cured individuals in a population and whether there is sufficient follow‐up in a sample of censored observations on their lifetimes to be confident of their presence are questions of major importance in medical survival analysis. Here we give a detailed analysis of a statistic designed to test for sufficient follow‐up in a sample. Assuming an i.i.d. censoring model, we obtain exact finite‐sample and asymptotic distributions for the statistic, and use these to calculate the power of a test based on it. A particularly useful finding is that the asymptotic distribution of the test statistic is parameter‐free in the null case when follow‐up is insufficient. The methods are illustrated with application to a glioma cancer dataset.

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“…A bootstrap bandwidth selection method for the cure rate and for the latency estimators has been proposed in [29,54], respectively. It is important to highlight that, due to the curse of dimensionality, the completely nonparametric models in the literature which are implemented in R can only handle a univariate continuous covariate [55,56]. Therefore, testing the effect of a covariate is a relevant aspect in regression analysis since the number of potential covariates to be included in the model can be extremely large.…”

Section: Nonparametric MCMmentioning

confidence: 99%

On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility

Ezquerro,

Cancela,

López-Cheda

2023

Mathematics

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In classical survival analysis, it is assumed that all the individuals will experience the event of interest. However, if there is a proportion of subjects who will never experience the event, then a standard survival approach is not appropriate, and cure models should be considered instead. This paper deals with the problem of adapting a machine learning approach for classical survival analysis to a situation when cure (i.e., not suffering the event) is a possibility. Specifically, a brief review of cure models and recent machine learning methodologies is presented, and an adaptation of machine learning approaches to account for cured individuals is introduced. In order to validate the proposed methods, we present an extensive simulation study in which we compare the performance of the adapted machine learning algorithms with existing cure models. The results show the good behavior of the semiparametric or the nonparametric approaches, depending on the simulated scenario. The practical utility of the methodology is showcased through two real-world dataset illustrations. In the first one, the results show the gain of using the nonparametric mixture cure model approach. In the second example, the results show the poor performance of some machine learning methods for small sample sizes.

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npcure: An R Package for Nonparametric Inference in Mixture Cure Models

Cited by 12 publications

References 27 publications

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment

Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment

Finite sample and asymptotic distributions of a statistic for sufficient follow‐up in cure models

On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility

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