The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Abraham (avi) Reichenberg - One of the best experts on this subject based on the ideXlab platform.
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advancing paternal age and risk of autism new evidence from a population based study and a meta analysis of Epidemiological Studies
Molecular Psychiatry, 2011Co-Authors: Christina M Hultman, Abraham (avi) Reichenberg, Sven Sandin, Paul Lichtenstein, Stephen Z LevineAbstract:Advanced paternal age has been suggested as a risk factor for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (1) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (2) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (3) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10-year birth cohort (N=1 075 588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth cohort, autism risk increased monotonically with increasing paternal age. Offspring of men aged ⩾50 years were 2.2 times (95% confidence interval: 1.26–3.88: P=0.006) more likely to have autism than offspring of men aged ⩽29 years, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (P<0.0001). Meta-analysis demonstrated advancing paternal age association with increased risk of autism across Studies. These findings provide the strongest evidence to date that advanced paternal age is a risk factor for autism in the offspring. Possible biological mechanisms include de novo aberration and mutations or epigenetic alterations associated with aging.
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Advancing Paternal Age and Risk of Autism New Evidence from a Population-Based Study and a Meta-Analysis of Epidemiological Studies.
Molecular Psychiatry, 2010Co-Authors: Abraham (avi) Reichenberg, Christina Hulltman, Sven Sandin, Stephen Levine, Paul LichtensteinAbstract:Advanced paternal age has been suggested as risk factors for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (I) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (II) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (III) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10 birth-years cohort (N=1,075,588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth-cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth-cohort autism risk increased monotonically with increasing paternal age. Offspring of men 50 years or older were 2.2 times (95% confidence interval: 1.26-3.88: p=0.006) more likely to have autism than offspring of men 29 years or younger, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (p
Paul Lichtenstein - One of the best experts on this subject based on the ideXlab platform.
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advancing paternal age and risk of autism new evidence from a population based study and a meta analysis of Epidemiological Studies
Molecular Psychiatry, 2011Co-Authors: Christina M Hultman, Abraham (avi) Reichenberg, Sven Sandin, Paul Lichtenstein, Stephen Z LevineAbstract:Advanced paternal age has been suggested as a risk factor for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (1) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (2) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (3) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10-year birth cohort (N=1 075 588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth cohort, autism risk increased monotonically with increasing paternal age. Offspring of men aged ⩾50 years were 2.2 times (95% confidence interval: 1.26–3.88: P=0.006) more likely to have autism than offspring of men aged ⩽29 years, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (P<0.0001). Meta-analysis demonstrated advancing paternal age association with increased risk of autism across Studies. These findings provide the strongest evidence to date that advanced paternal age is a risk factor for autism in the offspring. Possible biological mechanisms include de novo aberration and mutations or epigenetic alterations associated with aging.
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Advancing Paternal Age and Risk of Autism New Evidence from a Population-Based Study and a Meta-Analysis of Epidemiological Studies.
Molecular Psychiatry, 2010Co-Authors: Abraham (avi) Reichenberg, Christina Hulltman, Sven Sandin, Stephen Levine, Paul LichtensteinAbstract:Advanced paternal age has been suggested as risk factors for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (I) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (II) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (III) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10 birth-years cohort (N=1,075,588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth-cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth-cohort autism risk increased monotonically with increasing paternal age. Offspring of men 50 years or older were 2.2 times (95% confidence interval: 1.26-3.88: p=0.006) more likely to have autism than offspring of men 29 years or younger, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (p
Caroline C.w. Klaver - One of the best experts on this subject based on the ideXlab platform.
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The European Eye Epidemiology spectral-domain optical coherence tomography classification of macular diseases for Epidemiological Studies
Acta Ophthalmologica Scandinavica -Supplement-, 2019Co-Authors: Sarra Gattoussi, Gabriëlle H.s. Buitendijk, Tunde Peto, Irene Leung, Steffen Schmitz-valckenberg, Akio Oishi, Sebastian Wolf, Gabor Deak, Cécile Delcourt, Caroline C.w. KlaverAbstract:[u]Purpose:[/u] The aim of the European Eye Epidemiology (E3) consortium was to develop a spectral-domain optical coherence tomography (SD-OCT)-based classification for macular diseases to standardize Epidemiological Studies. [u]Methods:[/u] A European panel of vitreoretinal disease experts and epidemiologists belonging to the E3 consortium was assembled to define a classification for SD-OCT imaging of the macula. A series of meeting was organized, to develop, test and finalize the classification. First, grading methods used by the different research groups were presented and discussed, and a first version of classification was proposed. This first version was then tested on a set of 50 SD-OCT images in the Bordeaux and Rotterdam centres. Agreements were analysed and discussed with the panel of experts and a final version of the classification was produced. [u]Results:[/u] Definitions and classifications are proposed for the structure assessment of the vitreomacular interface (visibility of vitreous interface, vitreomacular adhesion, vitreomacular traction, epiretinal membrane, full-thickness macular hole, lamellar macular hole, macular pseudo-hole) and of the retina (retinoschisis, drusen, pigment epithelium detachment, hyper-reflective clumps, retinal pigment epithelium atrophy, intraretinal cystoid spaces, intraretinal tubular changes, subretinal fluid, subretinal material). Classifications according to size and location are defined. Illustrations of each item are provided, as well as the grading form. [u]Conclusion:[/u] The E3 SD-OCT classification has been developed to harmonize Epidemiological Studies. This homogenization will allow comparing and sharing data collection between European and international Studies.
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The European Eye Epidemiology spectral-domain optical coherence tomography classification of macular diseases for Epidemiological Studies
Wiley-Blackwell, 2019Co-Authors: Gattoussi Sarra, Gabriëlle H.s. Buitendijk, Delcourt Cécile, Peto Tunde, Leung Irene, Schmitz-valckenberg Steffen, Oishi Akio, Wolf Sebastian, Deák Gábor, Caroline C.w. KlaverAbstract:\u3cp\u3ePurpose: The aim of the European Eye Epidemiology (E3) consortium was to develop a spectral-domain optical coherence tomography (SD-OCT)-based classification for macular diseases to standardize Epidemiological Studies. Methods: A European panel of vitreoretinal disease experts and epidemiologists belonging to the E3 consortium was assembled to define a classification for SD-OCT imaging of the macula. A series of meeting was organized, to develop, test and finalize the classification. First, grading methods used by the different research groups were presented and discussed, and a first version of classification was proposed. This first version was then tested on a set of 50 SD-OCT images in the Bordeaux and Rotterdam centres. Agreements were analysed and discussed with the panel of experts and a final version of the classification was produced. Results: Definitions and classifications are proposed for the structure assessment of the vitreomacular interface (visibility of vitreous interface, vitreomacular adhesion, vitreomacular traction, epiretinal membrane, full-thickness macular hole, lamellar macular hole, macular pseudo-hole) and of the retina (retinoschisis, drusen, pigment epithelium detachment, hyper-reflective clumps, retinal pigment epithelium atrophy, intraretinal cystoid spaces, intraretinal tubular changes, subretinal fluid, subretinal material). Classifications according to size and location are defined. Illustrations of each item are provided, as well as the grading form. Conclusion: The E3 SD-OCT classification has been developed to harmonize Epidemiological Studies. This homogenization will allow comparing and sharing data collection between European and international Studies.\u3c/p\u3
Ludovic Trinquart - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Calculation for Meta-Epidemiological Studies
Statistics in Medicine, 2016Co-Authors: Bruno Giraudeau, Julian P T Higgins, Elsa Tavernier, Ludovic TrinquartAbstract:Meta-Epidemiological Studies are used to compare treatment effect estimates between randomized clinical trials with and without a characteristic of interest. To our knowledge, there is presently nothing to help researchers to a priori specify the required number of meta-analyses to be included in a meta-Epidemiological study. We derived a theoretical power function and sample size formula in the framework of a hierarchical model that allows for variation in the impact of the characteristic between trials within a meta-analysis and between meta-analyses. A simulation study revealed that the theoretical function overestimated power (because of the assumption of equal weights for each trial within and between meta-analyses). We also propose a simulation approach that allows for relaxing the constraints used in the theoretical approach and is more accurate. We illustrate that the two variables that mostly influence power are the number of trials per meta-analysis and the proportion of trials with the characteristic of interest. We derived a closed-form power function and sample size formula for estimating the impact of trial characteristics in meta-Epidemiological Studies. Our analytical results can be used as a 'rule of thumb' for sample size calculation for a meta-epidemiologic study. A more accurate sample size can be derived with a simulation study.
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Sample size calculation for meta‐Epidemiological Studies
Statistics in medicine, 2015Co-Authors: Bruno Giraudeau, Julian P T Higgins, Elsa Tavernier, Ludovic TrinquartAbstract:Meta-Epidemiological Studies are used to compare treatment effect estimates between randomized clinical trials with and without a characteristic of interest. To our knowledge, there is presently nothing to help researchers to a priori specify the required number of meta-analyses to be included in a meta-Epidemiological study. We derived a theoretical power function and sample size formula in the framework of a hierarchical model that allows for variation in the impact of the characteristic between trials within a meta-analysis and between meta-analyses. A simulation study revealed that the theoretical function overestimated power (because of the assumption of equal weights for each trial within and between meta-analyses). We also propose a simulation approach that allows for relaxing the constraints used in the theoretical approach and is more accurate. We illustrate that the two variables that mostly influence power are the number of trials per meta-analysis and the proportion of trials with the characteristic of interest. We derived a closed-form power function and sample size formula for estimating the impact of trial characteristics in meta-Epidemiological Studies. Our analytical results can be used as a 'rule of thumb' for sample size calculation for a meta-epidemiologic study. A more accurate sample size can be derived with a simulation study.
Sven Sandin - One of the best experts on this subject based on the ideXlab platform.
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advancing paternal age and risk of autism new evidence from a population based study and a meta analysis of Epidemiological Studies
Molecular Psychiatry, 2011Co-Authors: Christina M Hultman, Abraham (avi) Reichenberg, Sven Sandin, Paul Lichtenstein, Stephen Z LevineAbstract:Advanced paternal age has been suggested as a risk factor for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (1) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (2) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (3) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10-year birth cohort (N=1 075 588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth cohort, autism risk increased monotonically with increasing paternal age. Offspring of men aged ⩾50 years were 2.2 times (95% confidence interval: 1.26–3.88: P=0.006) more likely to have autism than offspring of men aged ⩽29 years, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (P<0.0001). Meta-analysis demonstrated advancing paternal age association with increased risk of autism across Studies. These findings provide the strongest evidence to date that advanced paternal age is a risk factor for autism in the offspring. Possible biological mechanisms include de novo aberration and mutations or epigenetic alterations associated with aging.
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Advancing Paternal Age and Risk of Autism New Evidence from a Population-Based Study and a Meta-Analysis of Epidemiological Studies.
Molecular Psychiatry, 2010Co-Authors: Abraham (avi) Reichenberg, Christina Hulltman, Sven Sandin, Stephen Levine, Paul LichtensteinAbstract:Advanced paternal age has been suggested as risk factors for autism, but empirical evidence is mixed. This study examines whether the association between paternal age and autism in the offspring (I) persists controlling for documented autism risk factors, including family psychiatric history, perinatal conditions, infant characteristics and demographic variables; (II) may be explained by familial traits associated with the autism phenotype, or confounding by parity; and (III) is consistent across Epidemiological Studies. Multiple study methods were adopted. First, a Swedish 10 birth-years cohort (N=1,075,588) was established. Linkage to the National Patient Register ascertained all autism cases (N=883). Second, 660 families identified within the birth-cohort had siblings discordant for autism. Finally, meta-analysis included population-based Epidemiological Studies. In the birth-cohort autism risk increased monotonically with increasing paternal age. Offspring of men 50 years or older were 2.2 times (95% confidence interval: 1.26-3.88: p=0.006) more likely to have autism than offspring of men 29 years or younger, after controlling for maternal age and documented risk factors for autism. Within-family analysis of discordant siblings showed that affected siblings had older paternal age, adjusting for maternal age and parity (p
