The Experts below are selected from a list of 31332 Experts worldwide ranked by ideXlab platform
Kate Tilling - One of the best experts on this subject based on the ideXlab platform.
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joint modeling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
American Journal of Epidemiology, 2021Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly measured exposures might predict later outcomes (e.g., blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP). Because 2-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modeling approach, examining associations of mean BP and BPV across childhood with left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allowed BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroskedasticity). We further distinguished within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher body weights, and in female participants and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21%, 95% credible interval: -0.23, 0.69), but this association became negative (-0.78%, 95% credible interval: -2.54, 0.22) once the effect of mean BP on LVMI was adjusted for. This joint modeling approach offers a flexible method of relating repeatedly measured exposures to later outcomes.
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joint modelling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
American Journal of Epidemiology, 2020Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modelling approach, examining associations of mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK's Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allow BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21% (95% credible interval: -0.23%, 0.69%)), but this association became negative (-0.78%, 95% credible interval: -2.54%, 0.22%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.
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joint modelling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
medRxiv, 2019Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modelling approach, examining associations of both mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data from the UK9s Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Using multilevel models, we allow BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a positive association with LVMI (10% increase in SD(BP) was predicted to increase LVMI by mean = 0.42% (95% credible interval: -0.47%, 1.38%)), but this association became negative (mean = -1.56%, 95% credible interval: -5.01%, 0.44%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.
Lulla Opatowski - One of the best experts on this subject based on the ideXlab platform.
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the impact of co circulating pathogens on sars cov 2 covid 19 surveillance how concurrent epidemics may Introduce Bias and decrease the observed sars cov 2 percent positivity
The Journal of Infectious Diseases, 2021Co-Authors: Aleksandra Kovacevic, Rosalind M Eggo, Marc Baguelin, Matthieu Domenech De Celles, Lulla OpatowskiAbstract:Background Circulation of seasonal non-SARS-CoV-2 respiratory viruses with syndromic overlap during the COVID-19 pandemic may alter quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures. Methods Using a multi-pathogen Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model formalizing co-circulation of SARS-CoV-2 and another respiratory virus, we assess how an outbreak of secondary virus may affect two COVID-19 surveillance indicators: testing demand and positivity. Using simulation, we assess to what extent the use of multiplex PCR tests on a subsample of symptomatic individuals can help correct of the observed SARS-CoV-2 percent positivity and improve surveillance quality. Results We find that a non-SARS-CoV-2 epidemic strongly increases SARS-CoV-2 daily testing demand and artificially reduces the observed SARS-CoV-2 percent positivity for the duration of the outbreak. We estimate that performing one multiplex test for every 1,000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percent positivity. Conclusions This study highlights that co-circulating respiratory viruses can distort SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex PCR tests, and a low number of samples is sufficient to avoid Bias in SARS-CoV-2 surveillance.
Richard M A Parker - One of the best experts on this subject based on the ideXlab platform.
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joint modeling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
American Journal of Epidemiology, 2021Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly measured exposures might predict later outcomes (e.g., blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP). Because 2-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modeling approach, examining associations of mean BP and BPV across childhood with left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allowed BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroskedasticity). We further distinguished within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher body weights, and in female participants and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21%, 95% credible interval: -0.23, 0.69), but this association became negative (-0.78%, 95% credible interval: -2.54, 0.22) once the effect of mean BP on LVMI was adjusted for. This joint modeling approach offers a flexible method of relating repeatedly measured exposures to later outcomes.
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joint modelling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
American Journal of Epidemiology, 2020Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modelling approach, examining associations of mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data (collected 1990-2011) from the UK's Avon Longitudinal Study of Parents and Children cohort. Using multilevel models, we allow BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a weak positive association with LVMI (10% increase in within-individual BP variance was predicted to increase LVMI by 0.21% (95% credible interval: -0.23%, 0.69%)), but this association became negative (-0.78%, 95% credible interval: -2.54%, 0.22%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.
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joint modelling of individual trajectories within individual variability and a later outcome systolic blood pressure through childhood and left ventricular mass in early adulthood
medRxiv, 2019Co-Authors: Richard M A Parker, David Phillippo, Laura D Howe, Jon Heron, Harvey Goldstein, George Leckie, Alun D Hughes, Kate TillingAbstract:Within-individual variability of repeatedly-measured exposures may predict later outcomes: e.g. blood pressure (BP) variability (BPV) is an independent cardiovascular risk factor above and beyond mean BP. Since two-stage methods, known to Introduce Bias, are typically used to investigate such associations, we Introduce a joint modelling approach, examining associations of both mean BP and BPV across childhood to left ventricular mass (indexed to height; LVMI) in early adulthood with data from the UK9s Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Using multilevel models, we allow BPV to vary between individuals (a "random effect") as well as to depend on covariates (allowing for heteroscedasticity). We further distinguish within-clinic variability ("measurement error") from visit-to-visit BPV. BPV was predicted to be greater at older ages, at higher bodyweights, and in females, and was positively correlated with mean BP. BPV had a positive association with LVMI (10% increase in SD(BP) was predicted to increase LVMI by mean = 0.42% (95% credible interval: -0.47%, 1.38%)), but this association became negative (mean = -1.56%, 95% credible interval: -5.01%, 0.44%)) once the effect of mean BP on LVMI was adjusted for. This joint modelling approach offers a flexible method of relating repeatedly-measured exposures to later outcomes.
Krina T Zondervan - One of the best experts on this subject based on the ideXlab platform.
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Basic statistical analysis in genetic case-control studies
Nature Protocols, 2011Co-Authors: Geraldine M. Clarke, Fredrik H. Pettersson, Andrew P. Morris, Carl A Anderson, Lon R Cardon, Krina T ZondervanAbstract:This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to Introduce Bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ∼1 h to complete.
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Data quality control in genetic case-control association studies
Nature Protocols, 2010Co-Authors: Carl A Anderson, Fredrik H. Pettersson, Andrew P. Morris, Geraldine M. Clarke, Lon R Cardon, Krina T ZondervanAbstract:This protocol details the steps for data quality assessment and control that are typically carried out during case-control association studies. The steps described involve the identification and removal of DNA samples and markers that Introduce Bias. These critical steps are paramount to the success of a case-control study and are necessary before statistically testing for association. We describe how to use PLINK, a tool for handling SNP data, to perform assessments of failure rate per individual and per SNP and to assess the degree of relatedness between individuals. We also detail other quality-control procedures, including the use of SMARTPCA software for the identification of ancestral outliers. These platforms were selected because they are user-friendly, widely used and computationally efficient. Steps needed to detect and establish a disease association using case-control data are not discussed here. Issues concerning study design and marker selection in case-control studies have been discussed in our earlier protocols. This protocol, which is routinely used in our labs, should take approximately 8 h to complete.
Aleksandra Kovacevic - One of the best experts on this subject based on the ideXlab platform.
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the impact of co circulating pathogens on sars cov 2 covid 19 surveillance how concurrent epidemics may Introduce Bias and decrease the observed sars cov 2 percent positivity
The Journal of Infectious Diseases, 2021Co-Authors: Aleksandra Kovacevic, Rosalind M Eggo, Marc Baguelin, Matthieu Domenech De Celles, Lulla OpatowskiAbstract:Background Circulation of seasonal non-SARS-CoV-2 respiratory viruses with syndromic overlap during the COVID-19 pandemic may alter quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures. Methods Using a multi-pathogen Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model formalizing co-circulation of SARS-CoV-2 and another respiratory virus, we assess how an outbreak of secondary virus may affect two COVID-19 surveillance indicators: testing demand and positivity. Using simulation, we assess to what extent the use of multiplex PCR tests on a subsample of symptomatic individuals can help correct of the observed SARS-CoV-2 percent positivity and improve surveillance quality. Results We find that a non-SARS-CoV-2 epidemic strongly increases SARS-CoV-2 daily testing demand and artificially reduces the observed SARS-CoV-2 percent positivity for the duration of the outbreak. We estimate that performing one multiplex test for every 1,000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percent positivity. Conclusions This study highlights that co-circulating respiratory viruses can distort SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex PCR tests, and a low number of samples is sufficient to avoid Bias in SARS-CoV-2 surveillance.
