The Experts below are selected from a list of 163191 Experts worldwide ranked by ideXlab platform
David Mcevoy - One of the best experts on this subject based on the ideXlab platform.
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incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
BMJ Open, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. Design Rapid systematic review and meta-analysis of Observational Research. Setting International studies on incubation period of COVID-19. Participants Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. Primary outcome measures Parameters of a lognormal distribution of incubation periods. Results The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. Conclusions The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
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the incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
medRxiv, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:Background: Reliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. Design: Rapid systematic review and meta-analysis of Observational Research Data sources: Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methods: Studies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. Results: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days. Conclusions and implications: The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available.
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the incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
medRxiv, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:ABSTRACT Background Reliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. Design Rapid systematic review and meta-analysis of Observational Research Data sources Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methods Studies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. Results The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days. Conclusions and implications The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available. ARTICLE SUMMARY Strengths and limitations of this study This study provides a pooled estimate of the distribution of incubation periods which may be used in subsequent modelling studies or to inform decision-making This estimate will need to be revisited as subsequent data become available. We present an RShiny app to allow the meta-analysis to be updated with new estimates
Lori E Ross - One of the best experts on this subject based on the ideXlab platform.
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quality assessment of Observational studies in psychiatry an example from perinatal psychiatric Research
International Journal of Methods in Psychiatric Research, 2011Co-Authors: Lori E Ross, Sophie Grigoriadis, Lana Mamisashvili, Gideon Koren, Meir SteinerAbstract:In perinatal psychiatry, randomized controlled trials are often not feasible on ethical grounds. Many studies are Observational in nature, while others employ large databases not designed primarily for Research purposes. Quality assessment of the resulting Research is complicated by a lack of standardized tools specifically for this purpose. The aim of this paper is to describe the Systematic Assessment of Quality in Observational Research (SAQOR), a quality assessment tool our team devised for a series of systematic reviews and meta-analyses of evidence-based literature regarding risks and benefits of antidepressant medication during pregnancy.
Meir Steiner - One of the best experts on this subject based on the ideXlab platform.
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quality assessment of Observational studies in psychiatry an example from perinatal psychiatric Research
International Journal of Methods in Psychiatric Research, 2011Co-Authors: Lori E Ross, Sophie Grigoriadis, Lana Mamisashvili, Gideon Koren, Meir SteinerAbstract:In perinatal psychiatry, randomized controlled trials are often not feasible on ethical grounds. Many studies are Observational in nature, while others employ large databases not designed primarily for Research purposes. Quality assessment of the resulting Research is complicated by a lack of standardized tools specifically for this purpose. The aim of this paper is to describe the Systematic Assessment of Quality in Observational Research (SAQOR), a quality assessment tool our team devised for a series of systematic reviews and meta-analyses of evidence-based literature regarding risks and benefits of antidepressant medication during pregnancy.
Richard E Gliklich - One of the best experts on this subject based on the ideXlab platform.
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why Observational studies should be among the tools used in comparative effectiveness Research
Health Affairs, 2010Co-Authors: Nancy A Dreyer, Sean R Tunis, Marc L Berger, Dan Ollendorf, Pattra W Mattox, Richard E GliklichAbstract:Doctors, patients, and other decision makers need access to the best available clinical evidence, which can come from systematic reviews, experimental trials, and Observational Research. Despite methodological challenges, high-quality Observational studies have an important role in comparative effectiveness Research because they can address issues that are otherwise difficult or impossible to study. In addition, many clinical and policy decisions do not require the very high levels of certainty provided by large, rigorous randomized trials. This paper provides insights and a framework to guide good decision making that involves the full range of high-quality comparative effectiveness Research techniques, including Observational Research.
Conor G Mcaloon - One of the best experts on this subject based on the ideXlab platform.
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incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
BMJ Open, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. Design Rapid systematic review and meta-analysis of Observational Research. Setting International studies on incubation period of COVID-19. Participants Searches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies. Primary outcome measures Parameters of a lognormal distribution of incubation periods. Results The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days. Conclusions The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
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the incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
medRxiv, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:Background: Reliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. Design: Rapid systematic review and meta-analysis of Observational Research Data sources: Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methods: Studies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. Results: The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days. Conclusions and implications: The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available.
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the incubation period of covid 19 a rapid systematic review and meta analysis of Observational Research
medRxiv, 2020Co-Authors: Conor G Mcaloon, Aine B Collins, Kevin Hunt, Ann Barber, Andrew W Byrne, Francis Butler, Miriam Casey, John M Griffin, Elizabeth A Lane, David McevoyAbstract:ABSTRACT Background Reliable estimates of the incubation period are important for decision making around the control of infectious diseases. Knowledge of the incubation period distribution can be used directly to inform decision-making or as inputs into mathematical models. Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation periods of COVID-19. Design Rapid systematic review and meta-analysis of Observational Research Data sources Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv were searched. The search was not limited to peer-reviewed published data, but also included pre-print articles. Study appraisal and synthesis methods Studies were selected for meta-analysis if they reported either the parameters and confidence intervals of the distributions fit to the data, or sufficient information to facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of the parameters of this distribution. Results The incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean was 5.8 (5.01, 6.69 days). It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates resulted in a median incubation period of 5.1 (4.5, 5.8) days, whereas the 95th percentile was 11.6 (9.5, 14.2) days. Conclusions and implications The choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Finally, we present an RShiny app that facilitates updating these estimates as new data become available. ARTICLE SUMMARY Strengths and limitations of this study This study provides a pooled estimate of the distribution of incubation periods which may be used in subsequent modelling studies or to inform decision-making This estimate will need to be revisited as subsequent data become available. We present an RShiny app to allow the meta-analysis to be updated with new estimates
