A product‐limit estimator of the conditional survival function when cure status is partially known

Safari, Wende Clarence; López-de-Ullibarri, Ignacio; Jácome, María Amalia

doi:10.1002/bimj.202000173

Cited by 14 publications

(28 citation statements)

References 31 publications

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“…The E estimator underestimates the time-to-event due to right censoring, showing shorter values of LoS as it only takes into account patients who experienced the event. The NP-MCM estimates do not suffer from a similar bias [12]. In fact, this is one of the advantages of using these methods.…”

Section: Resultsmentioning

confidence: 91%

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Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

et al. 2021

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Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds’ demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients’ hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.

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Section: Resultsmentioning

confidence: 91%

“…When there is a group of patients known not to experience the event, the survival function S(t) can be estimated nonparametrically as follows [12]:…”

Section: Model Formulationmentioning

confidence: 99%

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

et al. 2021

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“…Assuming model (1) and the availability of a suitable estimator of the S(t | x), estimators of the cure probability and the latency can be derived by considering the following relationships.…”

Section: Estimation When the Cure Status Is Partially Availablementioning

confidence: 99%

“…A common assumption in standard survival modeling is that all individuals can experience the event if observed for a sufficient amount of time. Cure models [1] have been developed because there might be situations where the standard survival model is not true, for example, in the event of a recurrence in some diseases or death from some types of cancer. One challenge with time-to-event data is that the event is not always observed (censored observations).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available

Safari

López-de-Ullibarri

Jácome

2021

The 4th XoveTIC Conference

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“…Several recently developments in cure models have also considered the case that cure is known for a proportion of the patient population. 15 Laska and Meisner 16 and Betensky and Schoenfeld 17 and others 14 , 18 proposed nonparametric estimations of survival function and cure rate with cure status available for some patients. Nevertheless, the goal of our study is to estimate treatment effects on patient's survival and cure rate rather than the survival function itself.…”

Section: Introductionmentioning

confidence: 99%

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

Xue

Saeed

Castagna

et al. 2022

Stat Methods Med Res

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Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed “cure” of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.

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A product‐limit estimator of the conditional survival function when cure status is partially known

Cited by 14 publications

References 31 publications

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

Nonparametric Inference for Mixture Cure Model When Cure Information Is Partially Available

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

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