The Experts below are selected from a list of 120909 Experts worldwide ranked by ideXlab platform
Jeroen P. Jansen - One of the best experts on this subject based on the ideXlab platform.
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incorporating single arm evidence into a network meta analysis using Aggregate Level matching assessing the impact
Statistics in Medicine, 2019Co-Authors: Joe Leahy, Jeroen P. Jansen, Howard Thom, Emma Gray, Aisling Oleary, Arthur White, Cathal WalshAbstract:Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process. We consider matching Aggregate Level covariates to comparator arms or trials and including this evidence in a network meta-analysis. We consider two methods of matching: (i) we include the chosen matched arm in the data set itself as a comparator for the single-arm trial; (ii) we use the baseline odds of an event in a chosen matched trial to use as a plug-in estimator for the single-arm trial. We illustrate that the synthesis of evidence resulting from such a setup is sensitive to the between-study variability, formulation of the prior for the between-design effect, weight given to the single-arm evidence, and extent of the bias in single-armed evidence. We provide a flowchart for the process involved in such a synthesis and highlight additional sensitivity analyses that should be carried out. This work was motivated by a hepatitis C data set, where many agents have only been examined in single-arm studies. We present the results of our methods applied to this data set.
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network meta analysis of individual and Aggregate Level data
Research Synthesis Methods, 2012Co-Authors: Jeroen P. JansenAbstract:Network meta-analysis is often performed with Aggregate-Level data (AgD). A challenge in using AgD is that the association between a patient-Level covariate and treatment effects at the study Level may not reflect the individual-Level effect modification. In this paper, non-linear network meta-analysis models for combining individual patient data (IPD) and AgD are presented to reduce bias and uncertainty of direct and indirect treatment effects in the presence of heterogeneity. The first method uses the same model form for IPD and AgD. With the second method, the model for AgD is obtained by integrating an underlying IPD model over the joint within-study distribution of covariates, in line with the method by Jackson et al. for ecological inferences. With simulated examples, the models are illustrated. Having IPD for a subset of studies improves estimation of treatment effects in the presence of patient-Level heterogeneity. Of the two proposed non-linear models for combining IPD and AgD, the second seems less affected by bias in situations with large treatment-by-patient-Level-covariate interactions, probably at the cost of greater uncertainty. Additional studies are needed to better understand when one model is favorable over the other. For network meta-analysis, it is recommended to use IPD when available. Copyright © 2012 John Wiley & Sons, Ltd.
Ahmed M Elgeneidy - One of the best experts on this subject based on the ideXlab platform.
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extraboard team sizing an analysis of short unscheduled absences among regular transit drivers
Transportation Research Part A-policy and Practice, 2014Co-Authors: Ehab Diab, Rania Wasfi, Ahmed M ElgeneidyAbstract:Several factors contribute to short-duration unscheduled absences of bus transit drivers. This article aims to understand these factors at the Aggregate Level and to anticipate future total absence that will need to be filled for a large-size transit operator. The Aggregate Level is defined as the total number of regular driver absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multiLevel regression model is generated to investigate regular drivers’ absences. The short-unscheduled absence is estimated in relation to temporal factors, drivers’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard drivers.
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extraboard team sizing an analysis of short unscheduled absences among regular transit operators case study of oc transpo ottawa canada
Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014Co-Authors: Ehab Diab, Rania Wasfi, Ahmed M ElgeneidyAbstract:Several factors contribute to short-duration unscheduled absences of bus transit operators (drivers). This article aims to understand these factors at the Aggregate Level and to anticipate future total absence that will need to be filled for a large-size transit operator. The Aggregate Level is defined as the total number of regular operator absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multiLevel regression model is generated to investigate regular operators’ absence. The short-unscheduled absence is estimated in relation to temporal factors, operators’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones being adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard operators.
Abba M Krieger - One of the best experts on this subject based on the ideXlab platform.
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post selection inference following Aggregate Level hypothesis testing in large scale genomic data
Journal of the American Statistical Association, 2018Co-Authors: Ruth Heller, Nilanjan Chatterjee, Abba M KriegerAbstract:ABSTRACTIn many genomic applications, hypotheses tests are performed for powerful identification of signals by aggregating test-statistics across units within naturally defined classes. Following class-Level testing, it is naturally of interest to identify the lower Level units which contain true signals. Testing the individual units within a class without taking into account the fact that the class was selected using an Aggregate-Level test-statistic, will produce biased inference. We develop a hypothesis testing framework that guarantees control for false positive rates conditional on the fact that the class was selected. Specifically, we develop procedures for calculating unit Level p-values that allows rejection of null hypotheses controlling for two types of conditional error rates, one relating to family-wise rate and the other relating to false discovery rate. We use simulation studies to illustrate validity and power of the proposed procedure in comparison to several possible alternatives. We illu...
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post selection inference following Aggregate Level hypothesis testing in large scale genomic data
bioRxiv, 2016Co-Authors: Ruth Heller, Nilanjan Chatterjee, Abba M KriegerAbstract:In many genomic applications, hypotheses tests are performed by aggregating test-statistics across units within naturally defined classes for powerful identification of signals. Following class-Level testing, it is naturally of interest to identify the lower Level units which contain true signals. Testing the individual units within a class without taking into account the fact that the class was selected using an Aggregate-Level test-statistic, will produce biased inference. We develop a hypothesis testing framework that guarantees control for false positive rates conditional on the fact that the class was selected. Specifically, we develop procedures for calculating unit Level p-values that allows rejection of null hypotheses controlling for two types of conditional error rates, one relating to family wise rate and the other relating to false discovery rate. We use simulation studies to illustrate validity and power of the proposed procedure in comparison to several possible alternatives. We illustrate the power of the method in a natural application involving whole-genome expression quantitative trait loci (eQTL) analysis across 17 tissue types using data from The Cancer Genome Atlas (TCGA) Project.
Ehab Diab - One of the best experts on this subject based on the ideXlab platform.
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extraboard team sizing an analysis of short unscheduled absences among regular transit drivers
Transportation Research Part A-policy and Practice, 2014Co-Authors: Ehab Diab, Rania Wasfi, Ahmed M ElgeneidyAbstract:Several factors contribute to short-duration unscheduled absences of bus transit drivers. This article aims to understand these factors at the Aggregate Level and to anticipate future total absence that will need to be filled for a large-size transit operator. The Aggregate Level is defined as the total number of regular driver absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multiLevel regression model is generated to investigate regular drivers’ absences. The short-unscheduled absence is estimated in relation to temporal factors, drivers’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard drivers.
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extraboard team sizing an analysis of short unscheduled absences among regular transit operators case study of oc transpo ottawa canada
Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014Co-Authors: Ehab Diab, Rania Wasfi, Ahmed M ElgeneidyAbstract:Several factors contribute to short-duration unscheduled absences of bus transit operators (drivers). This article aims to understand these factors at the Aggregate Level and to anticipate future total absence that will need to be filled for a large-size transit operator. The Aggregate Level is defined as the total number of regular operator absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multiLevel regression model is generated to investigate regular operators’ absence. The short-unscheduled absence is estimated in relation to temporal factors, operators’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones being adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard operators.
Cathal Walsh - One of the best experts on this subject based on the ideXlab platform.
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incorporating single arm evidence into a network meta analysis using Aggregate Level matching assessing the impact
Statistics in Medicine, 2019Co-Authors: Joe Leahy, Jeroen P. Jansen, Howard Thom, Emma Gray, Aisling Oleary, Arthur White, Cathal WalshAbstract:Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process. We consider matching Aggregate Level covariates to comparator arms or trials and including this evidence in a network meta-analysis. We consider two methods of matching: (i) we include the chosen matched arm in the data set itself as a comparator for the single-arm trial; (ii) we use the baseline odds of an event in a chosen matched trial to use as a plug-in estimator for the single-arm trial. We illustrate that the synthesis of evidence resulting from such a setup is sensitive to the between-study variability, formulation of the prior for the between-design effect, weight given to the single-arm evidence, and extent of the bias in single-armed evidence. We provide a flowchart for the process involved in such a synthesis and highlight additional sensitivity analyses that should be carried out. This work was motivated by a hepatitis C data set, where many agents have only been examined in single-arm studies. We present the results of our methods applied to this data set.
