Peritoneal Dialysis Modality Failure in a Middle-Income Country: A Retrospective Cohort Study.

RATIONALE & OBJECTIVE: Technique failure in peritoneal dialysis (PD) remains one of the most critical challenges of this therapy and is associated with a significant increase in costs and morbidity. Our objective was to estimate the frequency of PD technique failure and identify factors associated with technique failure. STUDY DESIGN: A retrospective multicenter observational cohort study. SETTING & PARTICIPANTS: All adult patients initiating PD between January 1, 2010, and December 31, 2015, with follow-up until December 31, 2018, at the Renal Therapy Services network in Colombia. EXPOSURE & PREDICTORS: PD modality (continuous ambulatory PD and automated PD) and demographic and clinical characteristics. OUTCOMES: Technique failure, defined as a switch to hemodialysis lasting at least 30 days. ANALYTICAL APPROACH: Sociodemographic and clinical characteristics of all patients were summarized descriptively according to modality. We estimated the cumulative incidence of technique failure, and a flexible parametric survival model with competing risks was used to evaluate factors associated with this outcome. RESULTS: Among 6,452 patients meeting inclusion criteria, 67% were treated with continuous ambulatory PD. The cumulative incidence of technique failure within 1 year of PD initiation adjusting for competing risks was 6.9% (95% CI, 6.3%-7.6%); within 2 years, technique failure was 13.5% (95% CI, 12.6%-14.4%); and within 3 years, 19.6% (95% CI, 18.5%-20.7%). Female sex, larger center size, and higher Kt/V were associated with lower risk for modality change, whereas diabetes, history of major abdominal surgery, catheter implant technique (laparotomy and percutaneous techniques), obesity, and peritonitis were associated with a higher likelihood of technique failure. LIMITATIONS: Variables of distance to the center, use of icodextrin, and measures of outcomes reported by patients were not included. CONCLUSIONS: Technique failure is relatively uncommon in Colombia; catheter-related problems are the most frequent cause of technique failure. Best practices in catheter insertion could minimize the risk for this outcome.

A new cure rate model with flexible competing causes with applications to melanoma and transplantation data.

In this article, we introduce a long-term survival model in which the number of competing causes of the event of interest follows the zero-modified geometric (ZMG) distribution. Such distribution accommodates equidispersion, underdispersion, and overdispersion and captures deflation or inflation of zeros in the number of lesions or initiated cells after the treatment. The ZMG distribution is also an appropriate alternative for modeling clustered samples when the number of competing causes of the event of interest consists of two subpopulations, one containing only zeros (cure proportion), while in the other (noncure proportion) the number of competing causes of the event of interest follows a geometric distribution. The advantage of this assumption is that we can measure the cure proportion in the initiated cells. Furthermore, the proposed model can yield greater or lower cure proportion than that of the geometric distribution when modeling the number of competing causes. In this article, we present some statistical properties of the proposed model and use the maximum likelihood method to estimate the model parameters. We also conduct a Monte Carlo simulation study to evaluate the performance of the estimators. We present and discuss two applications using real-world medical data to assess the practical usefulness of the proposed model.

Long-term frailty modeling using a non-proportional hazards model: Application with a melanoma dataset.

The semiparametric Cox regression model is often fitted in the modeling of survival data. One of its main advantages is the ease of interpretation, as long as the hazards rates for two individuals do not vary over time. In practice the proportionality assumption of the hazards may not be true in some situations. In addition, in several survival data is common a proportion of units not susceptible to the event of interest, even if, accompanied by a sufficiently large time, which is so-called immune, "cured," or not susceptible to the event of interest. In this context, several cure rate models are available to deal with in the long term. Here, we consider the generalized time-dependent logistic (GTDL) model with a power variance function (PVF) frailty term introduced in the hazard function to control for unobservable heterogeneity in patient populations. It allows for non-proportional hazards, as well as survival data with long-term survivors. Parameter estimation was performed using the maximum likelihood method, and Monte Carlo simulation was conducted to evaluate the performance of the models. Its practice relevance is illustrated in a real medical dataset from a population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil.

A new survival model with surviving fraction: An application to colorectal cancer data.

We propose a new survival model for lifetime data in the presence of surviving fraction and obtain some of its properties. Its genesis is based on extensions of the promotion time cure model, where an extra parameter controls the heterogeneity or dependence of an unobserved number of lifetimes. We construct a regression model to evaluate the effects of covariates in the cured fraction. We discuss inference aspects for the proposed model in a classical approach, where some maximum likelihood tools are explored. Further, an expectation maximization algorithm is developed to calculate the maximum likelihood estimates of the model parameters. We also perform an empirical study of the likelihood ratio test in order to compare the promotion time cure and the proposed models. We illustrate the usefulness of the new model by means of a colorectal cancer data set.