Geneviève Lefebvre

Bio: Geneviève Lefebvre is an academic researcher from Université du Québec à Montréal. The author has contributed to research in topics: Estimator & Childhood Acute Lymphoblastic Leukemia. The author has an hindex of 13, co-authored 47 publications receiving 688 citations. Previous affiliations of Geneviève Lefebvre include Université de Montréal & Montreal Neurological Institute and Hospital.

Use of inhaled corticosteroids during pregnancy and risk of pregnancy induced hypertension: nested case-control study

Abstract: Objective To determine whether the use of inhaled corticosteroids during pregnancy increases the risk of pregnancy induced hypertension and pre-eclampsia among asthmatic women. Design Nested case-control study. Setting Three administrative health databases from Quebec: RAMQ, MED-ECHO, and Fichier des evenements demographiques. Participants 3505 women with asthma, totalling 4593 pregnancies, between 1990 and 2000. Main outcome measures Pregnancy induced hypertension and pre-eclampsia. Results 302 cases of pregnancy induced hypertension and 165 cases of pre-eclampsia were identified. Use of inhaled corticosteroids from conception until date of outcome was not associated with an increased risk of pregnancy induced hypertension (adjusted odds ratio 1.02, 95% confidence interval 0.77 to 1.34) or pre-eclampsia (1.06, 0.74 to 1.53). No significant dose-response relation was observed between inhaled corticosteroids and pregnancy induced hypertension or pre-eclampsia. Oral corticosteroids were significantly associated with the risk of pregnancy induced hypertension (adjusted odds ratio 1.57, 1.02 to 2.41), and a trend was seen for pre-eclampsia (1.72, 0.98 to 3.02). Conclusion No significant increase of the risk of pregnancy induced hypertension or pre-eclampsia was detected among users of inhaled corticosteroids during pregnancy, while markers of uncontrolled and severe asthma were found to significantly increase the risks of pregnancy induced hypertension and pre-eclampsia.

Impact of mis-specification of the treatment model on estimates from a marginal structural model.

Abstract: Inverse probability of treatment weighted (IPTW) estimation for marginal structural models (MSMs) requires the specification of a nuisance model describing the conditional relationship between treatment allocation and confounders. However, there is still limited information on the best strategy for building these treatment models in practice. We developed a series of simulations to systematically determine the effect of including different types of candidate variables in such models. We explored the performance of IPTW estimators across several scenarios of increasing complexity, including one designed to mimic the complexity typically seen in large pharmacoepidemiologic studies.Our results show that including pure predictors of treatment (i.e. not confounders) in treatment models can lead to estimators that are biased and highly variable, particularly in the context of small samples. The bias and mean-squared error of the MSM-based IPTW estimator increase as the complexity of the problem increases. The performance of the estimator is improved by either increasing the sample size or using only variables related to the outcome to develop the treatment model. Estimates of treatment effect based on the true model for the probability of treatment are asymptotically unbiased.We recommend including only pure risk factors and confounders in the treatment model when developing an IPTW-based MSM.

Long-Term Abnormalities in the Corpus Callosum of Female Concussed Athletes

Abstract: Concussion is an injury affecting millions of individuals annually that can be associated with long-term sequelae. Recent studies have reported long-term abnormalities in the white matter (WM) tracts of male athletes. The corpus callosum (CC) and corticospinal tract (CST) have been shown to be particularly vulnerable to concussion, which may be related to abnormal interhemispheric functional connectivity and motor impairments. These anatomical pathways, however, have not been investigated in female athletes despite the functional significance of the CC and CST to adequate sports performance. In the present study, 8 healthy, unconcussed female athletes (soccer, hockey) were compared with 10 female athletes (soccer, hockey, water polo) 6 months post-concussion. Diffusion tensor imaging (DTI) of the CC and CST was conducted in a 3T magnetic resonance imaging (MRI) scanner. DTI analysis showed no significant differences between groups within the CST but revealed differences between groups in the CC. ...

The PETALE study: Late adverse effects and biomarkers in childhood acute lymphoblastic leukemia survivors

Abstract: Background Childhood cancer survivorship issues represent an established public health challenge. Most late adverse effects (LAEs) have been demonstrated to be time and treatment dependent. The PETALE study is a multidisciplinary research project aiming to comprehensively characterize LAEs and identify associated predictive biomarkers in childhood acute lymphoblastic leukemia (cALL) survivors. Methods cALL survivors treated at Sainte-Justine University Health Center with Dana-Farber Cancer Institution-ALL protocols 87-01 through 2005-01 were eligible. During Phase I of the study, the participants underwent comprehensive clinical, biologic, and psychosocial investigation targeting metabolic syndrome, cardiotoxicity, bone morbidity, neurocognitive problems, and quality of life issues. Whole-exome sequencing was performed for all participants. Subjects identified with an extreme phenotype during Phase I were recalled for additional testing (Phase II). Results Phase I included 246 survivors (recall rate 71.9%). Of those, 85 participants completed Phase II (recall rate 88.5%). Survivors agreeing to participate in Phase I (n = 251) were similar to those who refused (n = 31) in terms of relapse risk profile, radiotherapy exposure, and age at the time of study. Participants, however, tended to be slightly older at diagnosis (6.1 vs. 4.7 years old, P = 0.08), with a higher proportion of female agreeing to participate compared with males (93.2 vs. 86.5%, P = 0.07). Conclusion The PETALE study will contribute to comprehensively characterize clinical, psychosocial, biologic, and genomic features of cALL survivors using an integrated approach. Expected outcomes include LAE early detection biomarkers, long-term follow-up guidelines, and recommendations for physicians and health professionals.

Missing confounding data in marginal structural models: a comparison of inverse probability weighting and multiple imputation.

Abstract: Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision. The issue of missing data for inverse probability of treatment weighted estimation of marginal structural models (MSMs) has often been addressed, though little has been done to compare different missing data techniques in this relatively new method of analysis. We propose a method for systematically dealing with missingness in MSMs by treating missingness as a cause for censoring and weighting subjects by the inverse probability of missingness. We developed a series of simulations to systematically compare the effect of using case deletion, our inverse weighting approach, and multiple imputation in a MSM when there is missing information on an important confounder. We found that multiple imputation was slightly less biased and considerably less variable than the inverse probability approach. Thus, the lower variability achieved through multiple imputation makes it desirable in most practical cases where the missing data are strongly predicted by the available data. Inverse probability weighting is, however, a superior alternative to naive approaches such as complete-case analysis.

Multiple Imputation for Nonresponse in Surveys

Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

A User’s Guide

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On making causal claims : A review and recommendations

Abstract: Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data

Abstract: Background:Adjusting for large numbers of covariates ascertained from patients’ health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates,