Nonparametric estimation of pregnancy outcome probabilities

The outcome of pregnancy following the cerclage

Background: Cervix insufficiency is diagnosed based on a previous history of pregnancy loss in the second trimester, followed by painless cervical dilatation or premature rupture of the fetal membranes. Abnormal cervical tissue structural appears to be the cause of this complication. There are no diagnostic methods for cervical insufficiency before pregnancy, but magnetic resonance imaging (MRI...

I-21: Outcome of Twin Pregnancy

Nonparametric Estimation of Conditional Transition Probabilities in a non-Markov Illness-death Model

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years significant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the inf...

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. We investigate the asymptotic properties of the introduced estimators, such as their...

Estimation of Chaotic Probabilities

A Chaotic Probability model is a usual set of probability measures, M, the totality of which is endowed with an objective, frequentist interpretation as opposed to being viewed as a statistical compound hypothesis or an imprecise behavioral subjective one. In the prior work of Fierens and Fine, given finite time series data, the estimation of the Chaotic Probability model is based on the analys...