The Experts below are selected from a list of 20100 Experts worldwide ranked by ideXlab platform
Jonathan W Bartlett - One of the best experts on this subject based on the ideXlab platform.
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missing continuous outcomes under Covariate dependent missingness in cluster randomised trials
Statistical Methods in Medical Research, 2017Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline Covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline Covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline Covariate Effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline Covariate adjusted cluster-level analysis give unbiased estimates of the intervention Effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline Covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline Covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline Covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
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missing binary outcomes under Covariate dependent missingness in cluster randomised trials
Statistics in Medicine, 2017Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline Covariate-adjusted cluster-level analysis, random Effects logistic regression and generalised estimating equations when binary outcomes are missing under a baseline Covariate-dependent missingness mechanism. Missing outcomes were handled using complete records analysis and multilevel multiple imputation. We analytically show that cluster-level analyses for estimating risk ratio using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same Covariate Effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline Covariate in the outcome model. On the basis of the simulation study and analytical results, we give guidance on the conditions under which each approach is valid. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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missing binary outcomes under Covariate dependent missingness in cluster randomised trials
arXiv: Methodology, 2016Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline Covariate adjusted cluster-level analysis, random Effects logistic regression (RELR) and generalised estimating equations (GEE) when binary outcomes are missing under a baseline Covariate dependent missingness mechanism. Missing outcomes were handled using complete records analysis (CRA) and multilevel multiple imputation (MMI). We analytically show that cluster-level analyses for estimating risk ratio (RR) using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same Covariate Effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline Covariate in the outcome model. Based on the simulation study and analytical results, we give guidance on the conditions under which each approach is valid.
Laurent Briollais - One of the best experts on this subject based on the ideXlab platform.
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a competing risks model with binary time varying Covariates for estimation of breast cancer risks in brca1 families
Statistical Methods in Medical Research, 2021Co-Authors: Yunhee Choi, Hae Jung, Saundra S Buys, Mary B Daly, Esther M John, John L Hopper, Irene L Andrulis, Mary Beth Terry, Laurent BriollaisAbstract:Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their Effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying Covariates. Different parametric forms for the Effects of time-varying Covariates are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events.We also introduce a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We applied our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the time-varying Covariate Effect when assessing the role of risk-reducing salpingo oophorectomy on breast cancer. In particular, under the best fitting time-varying Covariate model, the overall Effect of risk-reducing salpingo oophorectomy on breast cancer risk was statistically significant in women with BRCA1 mutation.
Donna Spiegelman - One of the best experts on this subject based on the ideXlab platform.
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estimation in the cox survival regression model with Covariate measurement error and a changepoint
Biometrical Journal, 2020Co-Authors: Sarit Agami, David M Zucker, Donna SpiegelmanAbstract:The Cox regression model is a popular model for analyzing the relationship between a Covariate vector and a survival endpoint. The standard Cox model assumes a constant Covariate Effect across the entire Covariate domain. However, in many epidemiological and other applications, the Covariate of main interest is subject to a threshold Effect: a change in the slope at a certain point within the Covariate domain. Often, the Covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the Effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.
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estimation in the cox survival regression model with Covariate measurement error and a changepoint
arXiv: Applications, 2018Co-Authors: Sarit Agami, David M Zucker, Donna SpiegelmanAbstract:The Cox regression model is a popular model for analyzing the relationship between a Covariate and a survival endpoint. The standard Cox model assumes a constant Covariate Effect across the entire Covariate domain. However, in many epidemiological and other applications, the Covariate of main interest is subject to a threshold Effect: a change in the slope at a certain point within the Covariate domain. Often, the Covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the Effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.
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testing for a changepoint in the cox survival regression model
Journal of statistical theory and practice, 2013Co-Authors: David M Zucker, Sarit Agami, Donna SpiegelmanAbstract:The Cox regression model is a popular model for analyzing the relationship between a Covariate and a survival endpoint. The standard Cox model assumes that the Covariate Effect is constant across the entire Covariate domain. However, in many epidemiological and other applications, there is interest in considering the possibility that the Covariate of main interest is subject to a threshold Effect: a change in the slope at a certain point within the Covariate domain. In this article, we discuss testing for a threshold Effect in the case where the potential threshold value is unknown. We consider a maximum efficiency robust test (MERT) of linear combination form and supremum type tests. We present the relevant theory, present a simulation study comparing the power of various test statistics, and illustrate the use of the tests on data from the Nurses Health Study (NHS) concerning the relationship between chronic exposure to particulate matter of diameter 10 m or less (PM ) and fatal myocardial infarction. W...
Anower Hossain - One of the best experts on this subject based on the ideXlab platform.
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missing continuous outcomes under Covariate dependent missingness in cluster randomised trials
Statistical Methods in Medical Research, 2017Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline Covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline Covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline Covariate Effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline Covariate adjusted cluster-level analysis give unbiased estimates of the intervention Effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline Covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline Covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline Covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
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missing binary outcomes under Covariate dependent missingness in cluster randomised trials
Statistics in Medicine, 2017Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline Covariate-adjusted cluster-level analysis, random Effects logistic regression and generalised estimating equations when binary outcomes are missing under a baseline Covariate-dependent missingness mechanism. Missing outcomes were handled using complete records analysis and multilevel multiple imputation. We analytically show that cluster-level analyses for estimating risk ratio using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same Covariate Effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline Covariate in the outcome model. On the basis of the simulation study and analytical results, we give guidance on the conditions under which each approach is valid. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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missing binary outcomes under Covariate dependent missingness in cluster randomised trials
arXiv: Methodology, 2016Co-Authors: Anower Hossain, Karla Diazordaz, Jonathan W BartlettAbstract:Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline Covariate adjusted cluster-level analysis, random Effects logistic regression (RELR) and generalised estimating equations (GEE) when binary outcomes are missing under a baseline Covariate dependent missingness mechanism. Missing outcomes were handled using complete records analysis (CRA) and multilevel multiple imputation (MMI). We analytically show that cluster-level analyses for estimating risk ratio (RR) using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same Covariate Effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline Covariate in the outcome model. Based on the simulation study and analytical results, we give guidance on the conditions under which each approach is valid.
Rui Xiao - One of the best experts on this subject based on the ideXlab platform.
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detecting cell type specific allelic expression imbalance by integrative analysis of bulk and single cell rna sequencing data
PLOS Genetics, 2021Co-Authors: Jiaxin Fan, Xuran Wang, Rui XiaoAbstract:Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling Covariate Effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.
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detecting cell type specific allelic expression imbalance by integrative analysis of bulk and single cell rna sequencing data
bioRxiv, 2020Co-Authors: Jiaxin Fan, Xuran Wang, Rui XiaoAbstract:Abstract Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling Covariate Effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases. Author Summary Detection of allelic expression imbalance (AEI), a phenomenon where the two alleles of a gene differ in their expression magnitude, is a key step towards the understanding of phenotypic variations among individuals. Existing methods detect AEI use bulk RNA sequencing (RNA-seq) data and ignore AEI variations among different cell types. Although single-cell RNA sequencing (scRNA-seq) has enabled the characterization of cell-to-cell heterogeneity in gene expression, the high costs have limited its application in AEI analysis. To overcome this limitation, we developed BSCET to characterize cell-type-specific AEI using the widely available bulk RNA-seq data by integrating cell-type composition information inferred from scRNA-seq samples. Since the degree of AEI may vary with disease phenotypes, we further extended BSCET to detect genes whose cell-type-specific AEIs are associated with clinical factors. Through extensive benchmark evaluations and analyses of two pancreatic islet bulk RNA-seq datasets, we demonstrated BSCET’s ability to refine bulk-level AEI to cell-type resolution, and to identify genes whose cell-type-specific AEIs are associated with the progression of type 2 diabetes. With the vast amount of easily accessible bulk RNA-seq data, we believe BSCET will be a valuable tool for elucidating cell type contributions in human diseases.
