Biomedical subjects

Xavier Paoletti

Publications and source records attributed to Xavier Paoletti.

3 recordsLinked to original sources

PubMed2003

Induced and spontaneous abortion and breast cancer risk: results from the E3N cohort study.

Recent reviews reach conflicting conclusions on breast cancer risk after spontaneous or induced abortion. E3N is a large-scale cohort study collecting detailed information on environmental and reproductive factors. We investigated the relation between breast cancer and a history of induced and/or spontaneous abortion, using the data from the 100,000 women aged 40-65 at entrance in 1990. Among them, over 2,600 new invasive breast cancers had been diagnosed by June 2000. Multivariate analysis, adjusted for known potential confounders, showed no association between a history of induced abortion and breast cancer risk either in the whole population (relative risk [RR] = 0.91, 95% confidence interval [CI] 0.82-0.99) or in subgroups defined by parity or by menopausal status. Overall, the association between spontaneous abortion and breast cancer was not significant (RR = 1.05, 95% CI 0.95-1.15). However, there is a suggestion of increased risk with increased number of miscarriages (RR = 1.20, 95% CI 0.92-1.56 after 3 or more). Moreover, an interaction with menopausal status was observed. In premenopause, the risk decreased with increasing number of spontaneous abortions, whereas it increased in postmenopause. Among nulliparous and parous women, the relative risk estimates were respectively equal to 1.16 (95% CI 1.04-1.30, p trend < 0.0008) and 1.14 (95% CI 1.01-1.28, p trend = 0.005). Premenopausal breast cancer, on the other hand, appeared to be less frequent in women who had had repeated miscarriages. We conclude that there is no relationship between breast cancer and induced abortion but that an association with spontaneous abortion is possible and may depend on menopausal status.

Abortion, Induced↗

PubMed2003

Continual reassessment method for ordered groups.

We investigate the two-group continual reassessment method for a dose-finding study in which we anticipate some ordering between the groups. This is a situation in which, for either group, we have little or almost no knowledge about which of the available dose levels will correspond to the maximum tolerated dose (MTD), but we may have quite strong knowledge concerning which of the two groups will have the higher level of MTD, if indeed they do not have the same MTD. The motivation for studying this problem came from an investigation into a new therapy for acute leukemia in children. The background to this study is discussed. There were two groups of patients: one group already received heavy prior therapy while the second group had received relatively much lighter prior therapy. It was therefore anticipated that the second group would have an MTD higher or at least as high as the first. Generally, likelihood methods or, equivalently, the use of noninformative Bayes priors, can be used to model the main aspects of the study, i.e., the MTD for one of the groups, reserving more informative Bayes modeling to be applied to the secondary features of the study. These secondary features may simply be the direction of the difference between the MTD levels for the two groups or, possibly, information on the potential gap between the two MTDs.

Antineoplastic Agents↗

PubMed2002

Non-parametric optimal design in dose finding studies.

We describe a non-parametric optimal design as a theoretical gold standard for dose finding studies. Its purpose is analogous to the Cramer-Rao bound for unbiased estimators, i.e. it provides a bound beyond which improvements are not generally possible. The bound applies to the class of non-parametric designs where the data are not assumed to be generated by any known parametric model. Whenever parametric assumptions really hold it may be possible to do better than the optimal non-parametric design. The goal is to be able to compare any potential dose finding scheme with the optimal non-parametric benchmark. This paper makes precise what is meant by optimal in this context and also why the procedure is described as non-parametric.

Journal Article↗