The Experts below are selected from a list of 6132 Experts worldwide ranked by ideXlab platform
Rene J F Melis - One of the best experts on this subject based on the ideXlab platform.
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rasch analysis reveals comparative analyses of activities of daily living instrumental activities of daily living summary scores from different residential settings is inappropriate
Journal of Clinical Epidemiology, 2016Co-Authors: Jennifer E Lutomski, Paul F M Krabbe, Wendy Den P J Elzen, Marcel Olderikkert, Ewout W Steyerberg, Maaike E Muntinga, Nienke Bleijenberg, Gertrudis I J M Kempen, Rene J F MelisAbstract:Abstract Objectives To internally validate a 15-item dichotomous activities of daily living (ADL) and instrumental activities of daily living (IADL) index. Study Design and Setting Data were extracted from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS). Using Rasch modeling, six aspects of the ADL/IADL Scale were assessed: (1) overall fit, (2) internal consistency, (3) individual item and person fit, (4) local dependency, (5) targeting, and (6) differential item functioning (DIF) (RUMM 2030). All analyses were stratified by living situation [community-dwelling ( n = 21,926) or residential care facility ( n = 2,458)]. Results In both settings, "eating" was the easiest activity on the Scale and "performing household tasks" was the most difficult activity. However, based on the location on the Logit Scale, the level of difficulty for certain items varied between residential settings, suggesting summary scores are not equivalent between these settings. DIF by gender and age group was observed for several items, indicating potential measurement bias in the Scale. Conclusion Unless adjustments are undertaken, ADL/IADL summary scores retrieved from older persons residing in the community or residential care facilities should not be directly compared. This 15-item Scale is poorly targeted for a community-dwelling older population, underscoring the need for items with improved discriminative ability.
Pak C. Sham - One of the best experts on this subject based on the ideXlab platform.
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On the Transformation of Genetic Effect Size from Logit to Liability Scale.
Behavior genetics, 2021Co-Authors: Pak C. ShamAbstract:Genetic effects on the liability Scale are informative for describing the genetic architecture of binary traits, typically diseases. However, most genetic association analyses on binary traits are performed by logistic regression, and there is no straightforward method that transforms both effect size estimate and standard error from the Logit Scale to the liability Scale. Here, we derive a simple linear transformation of the log odds ratio and its standard error for a single nucleotide polymorphism (SNP) to an effect size and standard error on the liability Scale. We show by analytic calculations and simulations that this approximation is accurate when the disease is common and the SNP effect is small. We also apply this method to estimate the contribution of a SNP near the RET gene to the variance of Hirschsprung disease liability, and the age-specific contributions of APOE4 on the variance of Alzheimer’s disease liability. We discuss the approximate linear inter-relationships between genotype and effect sizes on the observed binary, Logit, and liability Scales, and the potential applications of the linear approximation to statistical power calculation for binary traits.
Rolf A. Ims - One of the best experts on this subject based on the ideXlab platform.
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Effect of food availability, season and river catchment on grey-sided vole selectivity.
2013Co-Authors: Eeva M. Soininen, Virve T. Ravolainen, Kari Anne Bråthen, Nigel G. Yoccoz, Ludovic Gielly, Rolf A. ImsAbstract:Parameter estimates of linear mixed effect models for the effect of food availability (biomass g/m), season and river catchment on grey-sided vole selectivity, i.e. difference between stomach content and biomass proportions. Intercept is calculated for autumn, Komagelva (KO) and mean biomass of all continuous predictor variables. “Est.” refers to regression coefficients, measured at Logit-Scale. Random effects are presented as standard deviations, sample size (n) referring to the number of grids included in the analysis, % values to the percentage of residual variance assigned to grid. Estimates with bold indicate that 95% CI does not include 0, with italics that 95% CI includes zero at most 0.05 and effect size is >0.15. Models where data were insufficient to evaluate the random effect (NA), have been calculated as linear regressions with fixed effects only. “Forbs” as predictor variable represents availability of the functional group of forbs, except for models which have a forb family (Polygonaceae, Cornaceae) as response variable. For these, biomass of the respective family is excluded from that of forbs and used as a separate predictor. Empty cells indicate that predictor variable in question has not been included in the model. See Material and Methods for details.
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Effect of food availability, season and river catchment on grey-sided vole diets.
2013Co-Authors: Eeva M. Soininen, Virve T. Ravolainen, Kari Anne Bråthen, Nigel G. Yoccoz, Ludovic Gielly, Rolf A. ImsAbstract:Parameter estimates of linear mixed effect models for the effect of food availability (biomass g/m), season and river catchment on grey-sided vole stomach content proportions. Intercept is calculated for autumn, Komagelva (KO) and mean biomass of all continuous predictor variables. “Est.” refers to regression coefficients, measured at Logit-Scale. Random effects are presented as standard deviations, sample size (n) referring to the number of grids included in the analysis, % values to the percentage of residual variance assigned to grid. Estimates with bold indicate that 95% CI does not include 0, with italics that 95% CI includes zero at most 0.05 and effect size is >0.15. Models where data were insufficient to evaluate the random effect (NA), have been calculated as linear regressions with fixed effects only. “Forbs” as predictor variable represents availability of the functional group of forbs, except for models which have a forb family (Polygonaceae, Cornaceae) as response variable. For these, biomass of the respective family is excluded from that of forbs and used as a separate predictor. Empty cells indicate that predictor variable in question has not been included in the model. See Material and Methods for details.
Jennifer E Lutomski - One of the best experts on this subject based on the ideXlab platform.
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rasch analysis reveals comparative analyses of activities of daily living instrumental activities of daily living summary scores from different residential settings is inappropriate
Journal of Clinical Epidemiology, 2016Co-Authors: Jennifer E Lutomski, Paul F M Krabbe, Wendy Den P J Elzen, Marcel Olderikkert, Ewout W Steyerberg, Maaike E Muntinga, Nienke Bleijenberg, Gertrudis I J M Kempen, Rene J F MelisAbstract:Abstract Objectives To internally validate a 15-item dichotomous activities of daily living (ADL) and instrumental activities of daily living (IADL) index. Study Design and Setting Data were extracted from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS). Using Rasch modeling, six aspects of the ADL/IADL Scale were assessed: (1) overall fit, (2) internal consistency, (3) individual item and person fit, (4) local dependency, (5) targeting, and (6) differential item functioning (DIF) (RUMM 2030). All analyses were stratified by living situation [community-dwelling ( n = 21,926) or residential care facility ( n = 2,458)]. Results In both settings, "eating" was the easiest activity on the Scale and "performing household tasks" was the most difficult activity. However, based on the location on the Logit Scale, the level of difficulty for certain items varied between residential settings, suggesting summary scores are not equivalent between these settings. DIF by gender and age group was observed for several items, indicating potential measurement bias in the Scale. Conclusion Unless adjustments are undertaken, ADL/IADL summary scores retrieved from older persons residing in the community or residential care facilities should not be directly compared. This 15-item Scale is poorly targeted for a community-dwelling older population, underscoring the need for items with improved discriminative ability.
Eeva M. Soininen - One of the best experts on this subject based on the ideXlab platform.
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Effect of food availability, season and river catchment on grey-sided vole selectivity.
2013Co-Authors: Eeva M. Soininen, Virve T. Ravolainen, Kari Anne Bråthen, Nigel G. Yoccoz, Ludovic Gielly, Rolf A. ImsAbstract:Parameter estimates of linear mixed effect models for the effect of food availability (biomass g/m), season and river catchment on grey-sided vole selectivity, i.e. difference between stomach content and biomass proportions. Intercept is calculated for autumn, Komagelva (KO) and mean biomass of all continuous predictor variables. “Est.” refers to regression coefficients, measured at Logit-Scale. Random effects are presented as standard deviations, sample size (n) referring to the number of grids included in the analysis, % values to the percentage of residual variance assigned to grid. Estimates with bold indicate that 95% CI does not include 0, with italics that 95% CI includes zero at most 0.05 and effect size is >0.15. Models where data were insufficient to evaluate the random effect (NA), have been calculated as linear regressions with fixed effects only. “Forbs” as predictor variable represents availability of the functional group of forbs, except for models which have a forb family (Polygonaceae, Cornaceae) as response variable. For these, biomass of the respective family is excluded from that of forbs and used as a separate predictor. Empty cells indicate that predictor variable in question has not been included in the model. See Material and Methods for details.
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Effect of food availability, season and river catchment on grey-sided vole diets.
2013Co-Authors: Eeva M. Soininen, Virve T. Ravolainen, Kari Anne Bråthen, Nigel G. Yoccoz, Ludovic Gielly, Rolf A. ImsAbstract:Parameter estimates of linear mixed effect models for the effect of food availability (biomass g/m), season and river catchment on grey-sided vole stomach content proportions. Intercept is calculated for autumn, Komagelva (KO) and mean biomass of all continuous predictor variables. “Est.” refers to regression coefficients, measured at Logit-Scale. Random effects are presented as standard deviations, sample size (n) referring to the number of grids included in the analysis, % values to the percentage of residual variance assigned to grid. Estimates with bold indicate that 95% CI does not include 0, with italics that 95% CI includes zero at most 0.05 and effect size is >0.15. Models where data were insufficient to evaluate the random effect (NA), have been calculated as linear regressions with fixed effects only. “Forbs” as predictor variable represents availability of the functional group of forbs, except for models which have a forb family (Polygonaceae, Cornaceae) as response variable. For these, biomass of the respective family is excluded from that of forbs and used as a separate predictor. Empty cells indicate that predictor variable in question has not been included in the model. See Material and Methods for details.
