Hypothese et modalite de la phrase

Within the theoretical framework that he presented at the XVIIIth Congress of Scholars of Romance languages held in Trier (1986), the author investigates the relation between conditional sentences (beginning with si ) and the modalities of sentences. The investigation is carried out from two different aspects. On the one hand, it investigates the relation of the word si to negative and affirmative modalities. Secondly, it investigates “substitutions for si “, i.e. those syntactic constructions that substitute for the conditional sentence starting with si , where we find interrogative and imperative sentences in the place of prolosis (Example: Restait-on dehors, on fondati au soleil. Fais un pas, je t'assomme. ) This complementarity points to a kind of “hypothetical (conditional) transformation" which - differently than the interrogative and imperative modalities - does not start with the virtual positive but with the sentence previously determined as affirmative or negative; the hypothetical (conditional) noininalizator si indicates to the affirmative or negative sentence : (Si ) -+ Il (n) etati (pas) là , and never to the interrogative, imperative or optative with which it, otherwise, shares the area of virtualizing modalities and constructions, located between negation (0) and affirmation (1).

Conditional Transformation Models for Survivor Function Estimation

In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient’s risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patient-specific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The well-known Cox model is included in the class of CTMs as a special case. We investigated the performance of CTMs in survival data analysis in a simulation that included proportional and non-proportional hazard settings and different scenarios of explanatory variables. Furthermore, we re-analysed the survival times of patients suffering from chronic myelogenous leukaemia and studied the impact of the proportional hazards assumption on previously published results.