53 research outputs found

    Méthodes d'analyse des essais cliniques en médecine personnalisée

    No full text
    Treatment effects are assessed in clinical trials on multiple endpoints independently and without allowing individualization of the response to treatment. Series of N-of-1 trials permit such individualization but combined analysis is still not possible. The first objective was to analyze PROFIL, a series of N-of-1 trials assessing the effect of sildenafil in Raynaud’s Phenomenon, with the use of mixed effects models in a Bayesian framework. We showed a high overall efficacy probability for sildĂ©nafil but with small and non relevant effect size. Individual effects were highly variable. The second objective was to evaluate the Net Benefit estimated from Generalized Pairwise Comparisons (GPC) when treatments effects are delayed in time or when endpoints are correlated, after having shown how it was possible to use this metric to evaluate a benefit-risk balance in a randomized oncology trial. The Net Benefit is modified by such correlations and the GPC estimation method is unbiased. The third objective was twofold : to extend the GPC method to be able to estimate a Net Benefit at both individual and population levels in series of N-of-1 trials, while allowing the integration of patient preferences in the estimation of this metric. This extension was illustrated by reanalyzing the PROFIL series.Les effets des traitements sont Ă©valuĂ©s dans des essais cliniques sur plusieurs critĂšres de jugements indĂ©pendamment et sans permettre d’individualiser la rĂ©ponse au traitement. Les sĂ©ries d’essais de taille 1 permettent l’individualisation mais pas l’analyse combinĂ©e de cet effet. Le premier objectif de ce travail Ă©tait l’analyse de PROFIL, une sĂ©rie d’essais de taille 1 testant le sildĂ©nafil dans le phĂ©nomĂšne de Raynaud, en utilisant des modĂšles mixtes dont les paramĂštres Ă©taient estimĂ©s en infĂ©rence BayĂ©sienne. Cette analyse a mis en Ă©vidence une probabilitĂ© Ă©levĂ©e d’efficacitĂ© du sildĂ©nafil au niveau populationnel mais avec une ampleur d’effet faible. Les effets individuels Ă©taient trĂšs variables. Le second objectif de ce travail Ă©tait l’évaluation de la mĂ©trique du BĂ©nĂ©fice Net estimĂ© par la mĂ©thode des comparaisons par paires gĂ©nĂ©ralisĂ©es (GPC) lorsque les effets des traitements sont retardĂ©s dans le temps ou lorsque les critĂšres de jugement sont corrĂ©lĂ©s entre eux, aprĂšs avoir montrĂ© comment il Ă©tait possible d’utiliser cette mĂ©trique pour Ă©valuer une balance bĂ©nĂ©fice-risque dans un essai randomisĂ© en oncologie. Le BĂ©nĂ©fice Net est modifiĂ© par la corrĂ©lation entre critĂšres et la mĂ©thode des GPC l’estime sans biais. Le troisiĂšme objectif de ce travail Ă©tait double : il s’agissait d’étendre la mĂ©thode des GPC afin de pouvoir estimer un BĂ©nĂ©fice Net aux niveaux individuel et populationnel dans les sĂ©ries d’essais de taille 1, tout en permettant l’intĂ©gration des prĂ©fĂ©rences des patients Ă  l’estimation de cette mĂ©trique. L’illustration de cette extension Ă©tait rĂ©alisĂ©e via la rĂ©analyse de la sĂ©rie PROFIL

    Methods of analysis of clinical trials in personalised medicine

    No full text
    Les effets des traitements sont Ă©valuĂ©s dans des essais cliniques sur plusieurs critĂšres de jugements indĂ©pendamment et sans permettre d’individualiser la rĂ©ponse au traitement. Les sĂ©ries d’essais de taille 1 permettent l’individualisation mais pas l’analyse combinĂ©e de cet effet. Le premier objectif de ce travail Ă©tait l’analyse de PROFIL, une sĂ©rie d’essais de taille 1 testant le sildĂ©nafil dans le phĂ©nomĂšne de Raynaud, en utilisant des modĂšles mixtes dont les paramĂštres Ă©taient estimĂ©s en infĂ©rence BayĂ©sienne. Cette analyse a mis en Ă©vidence une probabilitĂ© Ă©levĂ©e d’efficacitĂ© du sildĂ©nafil au niveau populationnel mais avec une ampleur d’effet faible. Les effets individuels Ă©taient trĂšs variables. Le second objectif de ce travail Ă©tait l’évaluation de la mĂ©trique du BĂ©nĂ©fice Net estimĂ© par la mĂ©thode des comparaisons par paires gĂ©nĂ©ralisĂ©es (GPC) lorsque les effets des traitements sont retardĂ©s dans le temps ou lorsque les critĂšres de jugement sont corrĂ©lĂ©s entre eux, aprĂšs avoir montrĂ© comment il Ă©tait possible d’utiliser cette mĂ©trique pour Ă©valuer une balance bĂ©nĂ©fice-risque dans un essai randomisĂ© en oncologie. Le BĂ©nĂ©fice Net est modifiĂ© par la corrĂ©lation entre critĂšres et la mĂ©thode des GPC l’estime sans biais. Le troisiĂšme objectif de ce travail Ă©tait double : il s’agissait d’étendre la mĂ©thode des GPC afin de pouvoir estimer un BĂ©nĂ©fice Net aux niveaux individuel et populationnel dans les sĂ©ries d’essais de taille 1, tout en permettant l’intĂ©gration des prĂ©fĂ©rences des patients Ă  l’estimation de cette mĂ©trique. L’illustration de cette extension Ă©tait rĂ©alisĂ©e via la rĂ©analyse de la sĂ©rie PROFIL.Treatment effects are assessed in clinical trials on multiple endpoints independently and without allowing individualization of the response to treatment. Series of N-of-1 trials permit such individualization but combined analysis is still not possible. The first objective was to analyze PROFIL, a series of N-of-1 trials assessing the effect of sildenafil in Raynaud’s Phenomenon, with the use of mixed effects models in a Bayesian framework. We showed a high overall efficacy probability for sildĂ©nafil but with small and non relevant effect size. Individual effects were highly variable. The second objective was to evaluate the Net Benefit estimated from Generalized Pairwise Comparisons (GPC) when treatments effects are delayed in time or when endpoints are correlated, after having shown how it was possible to use this metric to evaluate a benefit-risk balance in a randomized oncology trial. The Net Benefit is modified by such correlations and the GPC estimation method is unbiased. The third objective was twofold : to extend the GPC method to be able to estimate a Net Benefit at both individual and population levels in series of N-of-1 trials, while allowing the integration of patient preferences in the estimation of this metric. This extension was illustrated by reanalyzing the PROFIL series

    Méthodes d'analyse des essais cliniques en médecine personnalisée

    No full text
    Treatment effects are assessed in clinical trials on multiple endpoints independently and without allowing individualization of the response to treatment. Series of N-of-1 trials permit such individualization but combined analysis is still not possible. The first objective was to analyze PROFIL, a series of N-of-1 trials assessing the effect of sildenafil in Raynaud’s Phenomenon, with the use of mixed effects models in a Bayesian framework. We showed a high overall efficacy probability for sildĂ©nafil but with small and non relevant effect size. Individual effects were highly variable. The second objective was to evaluate the Net Benefit estimated from Generalized Pairwise Comparisons (GPC) when treatments effects are delayed in time or when endpoints are correlated, after having shown how it was possible to use this metric to evaluate a benefit-risk balance in a randomized oncology trial. The Net Benefit is modified by such correlations and the GPC estimation method is unbiased. The third objective was twofold : to extend the GPC method to be able to estimate a Net Benefit at both individual and population levels in series of N-of-1 trials, while allowing the integration of patient preferences in the estimation of this metric. This extension was illustrated by reanalyzing the PROFIL series.Les effets des traitements sont Ă©valuĂ©s dans des essais cliniques sur plusieurs critĂšres de jugements indĂ©pendamment et sans permettre d’individualiser la rĂ©ponse au traitement. Les sĂ©ries d’essais de taille 1 permettent l’individualisation mais pas l’analyse combinĂ©e de cet effet. Le premier objectif de ce travail Ă©tait l’analyse de PROFIL, une sĂ©rie d’essais de taille 1 testant le sildĂ©nafil dans le phĂ©nomĂšne de Raynaud, en utilisant des modĂšles mixtes dont les paramĂštres Ă©taient estimĂ©s en infĂ©rence BayĂ©sienne. Cette analyse a mis en Ă©vidence une probabilitĂ© Ă©levĂ©e d’efficacitĂ© du sildĂ©nafil au niveau populationnel mais avec une ampleur d’effet faible. Les effets individuels Ă©taient trĂšs variables. Le second objectif de ce travail Ă©tait l’évaluation de la mĂ©trique du BĂ©nĂ©fice Net estimĂ© par la mĂ©thode des comparaisons par paires gĂ©nĂ©ralisĂ©es (GPC) lorsque les effets des traitements sont retardĂ©s dans le temps ou lorsque les critĂšres de jugement sont corrĂ©lĂ©s entre eux, aprĂšs avoir montrĂ© comment il Ă©tait possible d’utiliser cette mĂ©trique pour Ă©valuer une balance bĂ©nĂ©fice-risque dans un essai randomisĂ© en oncologie. Le BĂ©nĂ©fice Net est modifiĂ© par la corrĂ©lation entre critĂšres et la mĂ©thode des GPC l’estime sans biais. Le troisiĂšme objectif de ce travail Ă©tait double : il s’agissait d’étendre la mĂ©thode des GPC afin de pouvoir estimer un BĂ©nĂ©fice Net aux niveaux individuel et populationnel dans les sĂ©ries d’essais de taille 1, tout en permettant l’intĂ©gration des prĂ©fĂ©rences des patients Ă  l’estimation de cette mĂ©trique. L’illustration de cette extension Ă©tait rĂ©alisĂ©e via la rĂ©analyse de la sĂ©rie PROFIL

    Involvement in Root Cause Analysis and Patient Safety Culture Among Hospital Care Providers

    No full text
    International audienceThe experience feedback committee (EFC) is a tool designed to involve medical teams in patient safety management, through root cause analysis within the team.OBJECTIVE:The aim of the study was to determine whether patient safety culture, as measured by the Hospital Survey on Patient Safety Culture (HSOPS), differed regarding care provider involvement in EFC activities.METHODS:Using the original data from a cross-sectional survey of 5064 employees at a single university hospital in France, we analyzed the differences in HSOPS dimension scores according involvement in EFC activities.RESULTS:Of 5064 eligible employees, 3888 (76.8%) participated in the study. Among the respondents, 440 (11.3%) participated in EFC activities. Experience feedback committee participants had a more developed patient safety culture, with 9 of the 12 HSOPS dimension scores significantly higher than EFC nonparticipants (overall effect size = 0.31, 95% confidence interval = 0.21 to 0.41, P < 0.001). A multivariate analysis of variance indicated that all 12 dimension scores, taken together, were significantly different between EFC participants and nonparticipants (P < 0.0001), independently of sex, hospital department, and healthcare profession category. The largest differences in scores related to the "feedback and communication about error," "organizational learning," and "Nonpunitive response to error" dimensions. The analysis of the subgroup of professionals who worked in a department with a productive EFC, defined as an EFC implementing at least five actions per year, showed a higher patient safety culture level for seven of the 12 HSOPS dimensions (overall effect size = 0.19, 95% confidence interval = 0.10 to 0.27, P < 0.001).DISCUSSION AND CONCLUSIONS:Participation in EFC activities was associated with higher patient safety culture scores. The findings suggest that root cause analysis in the team's routine may improve patient safety culture

    Health Profile of Precarious Migrants Attending the MĂ©decins Du Monde’s Health and Social Care Centres in France: a Cross-Sectional Study

    No full text
    International audienceObjective: The present study aimed to compare the precarious migrants’ health problems managed in MĂ©decins du Monde’s health and social care centres (CASO) with those of patients attending general practice in France. Methods: We compared the most frequent health problems managed in the 19 CASO in metropolitan France with those of a national sample of usual general practice consultations, after standardisation for age and sex. Results: Precarious migrants had fewer health problems managed per consultation than other patients (mean: 1.31 vs. 2.16), and these corresponded less frequently to chronic conditions (21.3% vs. 46.8%). The overrepresented health problems among CASO consultations were mainly headache (1.11% vs. 0.45%), viral hepatitis (1.05% vs. 0.20%), type 1 diabetes (1.01% vs. 0.50%) and teeth/gum disease (1.01% vs. 0.23%). Their underrepresented health problems were mainly lipid disorder (0.39% vs. 8.20%), depressive disorder (1.36% vs. 5.28%) and hypothyroidism (0.50% vs. 3.08%). Prevention issues were nominal in precarious migrants (0.16%). Conclusion: Both chronic somatic and mental conditions of precarious migrants are presumably underdiagnosed. Their screening should be improved in primary care
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