A method for determining groups in cumulative incidence curves in competing risk data.

The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the equality of these curves but also group them if they are not equal.

A method for determining groups in nonparametric regression curves: Application to prefrontal cortex neural activity analysis.

Generalized additive models provide a flexible and easily-interpretable method for uncovering a nonlinear relationship between response and covariates. In many situations, the effect of a continuous covariate on the response varies across groups defined by the levels of a categorical variable. When confronted with a considerable number of groups defined by the levels of the categorical variable and a factor-by-curve interaction is detected in the model, it then becomes important to compare these regression curves.

Anxiety and/or depression: which symptoms contribute to adverse clinical outcomes after amputation?

Background: One of the most serious complications of diabetes mellitus (DM) is a diabetic foot ulcer (DFU), with lower extremity amputation (LEA).

Aims: This study aims to explore the role of anxiety and depression on mortality, reamputation and healing, after a LEA due to DFU.

Methods: A sample of 149 patients with DFU who underwent LEA answered the Hospital Anxiety and Depression Scale and a sociodemographic and clinical questionnaire.

Estimation in the progressive illness-death model: A nonexhaustive review.

Multistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so-called "illness-death" model plays a central role in the theory and practice of these models. Many time-to-event datasets from medical studies with multiple end points can be reduced to this generic structure.

A method for determining groups in multiple survival curves.

Survival analysis includes a wide variety of methods for analyzing time-to-event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data.

A Multistate Model for Analyzing Transitions Between Body Mass Index Categories During Childhood: The Generation XXI Birth Cohort Study.

Prevalences of overweight and obesity in young children have risen dramatically in the last several decades in most developed countries. Childhood overweight and obesity are known to have immediate and long-term health consequences and are now recognized as important public health concerns. We used a Markov 4-state model with states defined by 4 body mass index (BMI; weight (kg)/height (m)2) categories (underweight (<-2 standard deviations (SDs) of BMI z score), normal weight (-2 ≤ SD ≤ 1), overweight (1 < SD ≤ 2), and obese (>2 SDs of BMI z score)) to study the rates of transition to higher or lower BMI categories among children aged 4-10 years.

Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate.

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.

Nonparametric estimation of the survival function for ordered multivariate failure time data: A comparative study.

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions, and the conditional distribution of gap times.

Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.

Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov.

smoothHR: an R package for pointwise nonparametric estimation of hazard ratio curves of continuous predictors.

The Cox proportional hazards regression model has become the traditional choice for modeling survival data in medical studies. To introduce flexibility into the Cox model, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based hazard ratio (HR) curves, taking a specific covariate value as reference.

Multi-state models for the analysis of time-to-event data.

The experience of a patient in a survival study may be modelled as a process with two states and one possible transition from an "alive" state to a "dead" state. In some studies, however, the "alive" state may be partitioned into two or more intermediate (transient) states, each of which corresponding to a particular stage of the illness. In such studies, multi-state models can be used to model the movement of patients among the various states.

tdc.msm: an R library for the analysis of multi-state survival data.

The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data.

Nonparametric estimation of transition probabilities in a non-Markov illness-death model.

In this paper we consider nonparametric estimation of transition probabilities for multi-state models. Specifically, we focus on the illness-death or disability model. The main novelty of the proposed estimators is that they do not rely on the Markov assumption, typically assumed to hold in a multi-state model.