The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
James R. Carpenter - One of the best experts on this subject based on the ideXlab platform.
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estimating treatment effects with partially Observed Covariates using outcome regression with missing indicators
Biometrical Journal, 2020Co-Authors: Helen A Blake, Clemence Leyrat, James R. Carpenter, Kathryn E Mansfield, Laurie A Tomlinson, Elizabeth A WilliamsonAbstract:Missing data is a common issue in research using observational studies to investigate the effect of treatments on health outcomes. When missingness occurs only in the Covariates, a simple approach is to use missing indicators to handle the partially Observed Covariates. The missing indicator approach has been criticized for giving biased results in outcome regression. However, recent papers have suggested that the missing indicator approach can provide unbiased results in propensity score analysis under certain assumptions. We consider assumptions under which the missing indicator approach can provide valid inferences, namely, (1) no unmeasured confounding within missingness patterns; either (2a) covariate values of patients with missing data were conditionally independent of treatment or (2b) these values were conditionally independent of outcome; and (3) the outcome model is correctly specified: specifically, the true outcome model does not include interactions between missing indicators and fully Observed Covariates. We prove that, under the assumptions above, the missing indicator approach with outcome regression can provide unbiased estimates of the average treatment effect. We use a simulation study to investigate the extent of bias in estimates of the treatment effect when the assumptions are violated and we illustrate our findings using data from electronic health records. In conclusion, the missing indicator approach can provide valid inferences for outcome regression, but the plausibility of its assumptions must first be considered carefully.
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propensity score analysis with partially Observed Covariates how should multiple imputation be used
Statistical Methods in Medical Research, 2019Co-Authors: Clemence Leyrat, Matthieu Rescherigon, Shaun R Seaman, Ian J Douglas, Liam Smeeth, Elizabeth A Williamson, Ian R White, James R. CarpenterAbstract:Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially Observed Covariates. Multiple imputation is a natural approach to handle missing data on Covariates: Covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.
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multiple imputation of Covariates by fully conditional specification accommodating the substantive model
Statistical Methods in Medical Research, 2015Co-Authors: Jonathan W Bartlett, Shaun R Seaman, Ian R White, James R. CarpenterAbstract:Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially Observed Covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute Covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that Covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available.
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multiple imputation of Covariates by fully conditional specification accommodating the substantive model
arXiv: Methodology, 2012Co-Authors: Jonathan W Bartlett, Shaun R Seaman, Ian R White, James R. CarpenterAbstract:Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation (MI). Imputation of partially Observed Covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of MI may impute Covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing MI, can be modified so that Covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it to existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible.
Ralph B Dagostino - One of the best experts on this subject based on the ideXlab platform.
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modeling frailty as a function of Observed Covariates
Journal of statistical theory and practice, 2007Co-Authors: Usha Govindarajulu, Mark E Glickman, Ralph B DagostinoAbstract:In survival analysis, frailty models are potential choices for modeling unexplained heterogeneity in a population, which exists due to missing covariate information or to differential survival patterns among members of a population. Typically, in these models, the frailty term, which is a random effect, is unconditional on the Observed Covariates. In our model, we allow the frailty effect to be modulated by the Observed Covariates. In this way, the frailty effect is no longer rendered separate from the Covariates, allowing the model to capture the frailty effect as function of unObserved as well as Observed information. We demonstrate this model on a set of subjects in the Framingham Heart Study who had atrial fibrillation events and who were followed forward in time for the development of stroke. As assessed via performance measures, our model performs better on this data than the other models considered. It also captures unique hazard configurations not produced by the other models.
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propensity score methods for bias reduction in the comparison of a treatment to a non randomized control group
Statistics in Medicine, 1998Co-Authors: Ralph B DagostinoAbstract:In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their Observed Covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the Covariates, can be used to balance the Covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the Observed Covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
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propensity score methods for bias reduction in the comparison of a treatment to a non randomized control group
Statistics in Medicine, 1998Co-Authors: Ralph B DagostinoAbstract:In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their Observed Covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the Covariates, can be used to balance the Covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the Observed Covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples. © 1998 John Wiley & Sons, Ltd.
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tutorial in biostatistics propensity score methods for bias reduction in the comparison of a treatment to a non randomized control group
1998Co-Authors: Ralph B DagostinoAbstract:SUMMARY In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large di⁄erences on their Observed Covariates, and these di⁄erences can lead to biased estimates of treatment e⁄ects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the Covariates, can be used to balance the Covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the Observed Covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples. ( 1998 John Wiley & Sons, Ltd.
Elizabeth A Williamson - One of the best experts on this subject based on the ideXlab platform.
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estimating treatment effects with partially Observed Covariates using outcome regression with missing indicators
Biometrical Journal, 2020Co-Authors: Helen A Blake, Clemence Leyrat, James R. Carpenter, Kathryn E Mansfield, Laurie A Tomlinson, Elizabeth A WilliamsonAbstract:Missing data is a common issue in research using observational studies to investigate the effect of treatments on health outcomes. When missingness occurs only in the Covariates, a simple approach is to use missing indicators to handle the partially Observed Covariates. The missing indicator approach has been criticized for giving biased results in outcome regression. However, recent papers have suggested that the missing indicator approach can provide unbiased results in propensity score analysis under certain assumptions. We consider assumptions under which the missing indicator approach can provide valid inferences, namely, (1) no unmeasured confounding within missingness patterns; either (2a) covariate values of patients with missing data were conditionally independent of treatment or (2b) these values were conditionally independent of outcome; and (3) the outcome model is correctly specified: specifically, the true outcome model does not include interactions between missing indicators and fully Observed Covariates. We prove that, under the assumptions above, the missing indicator approach with outcome regression can provide unbiased estimates of the average treatment effect. We use a simulation study to investigate the extent of bias in estimates of the treatment effect when the assumptions are violated and we illustrate our findings using data from electronic health records. In conclusion, the missing indicator approach can provide valid inferences for outcome regression, but the plausibility of its assumptions must first be considered carefully.
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propensity score analysis with partially Observed Covariates how should multiple imputation be used
Statistical Methods in Medical Research, 2019Co-Authors: Clemence Leyrat, Matthieu Rescherigon, Shaun R Seaman, Ian J Douglas, Liam Smeeth, Elizabeth A Williamson, Ian R White, James R. CarpenterAbstract:Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially Observed Covariates. Multiple imputation is a natural approach to handle missing data on Covariates: Covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.
Daniel F. Heitjan - One of the best experts on this subject based on the ideXlab platform.
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sensitivity of the hazard ratio to nonignorable treatment assignment in an observational study
Statistics in Medicine, 2007Co-Authors: Nandita Mitra, Daniel F. HeitjanAbstract:In non-randomized studies, estimation of treatment effects generally requires adjustment for imbalances in Observed Covariates. One such method, based on the propensity score, is useful in many applications but may be biased when the assumption of strongly ignorable treatment assignment is violated. Because it is not possible to evaluate this assumption from the data, it is advisable to assess the sensitivity of conclusions to violations of strong ignorability. Lin et al. (Biomet. 1998; 54:948–963) have implemented this idea by investigating how an unmeasured covariate may affect the conclusions of an observational study. We extend their method to assess sensitivity of the treatment hazard ratio to hidden bias under a range of covariate distributions. We derive simple formulas for approximating the true from the apparent treatment hazard ratio estimated under a specific survival model, and assess the validity of these formulas in simulation studies. We demonstrate the method in an analysis of SEER-Medicare data on the effects of chemotherapy in elderly colon cancer patients.Copyright © 2006 John Wiley & Sons, Ltd.
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sensitivity of the hazard ratio to nonignorable treatment assignment in an observational study
Statistics in Medicine, 2007Co-Authors: Nandita Mitra, Daniel F. HeitjanAbstract:In non-randomized studies, estimation of treatment effects generally requires adjustment for imbalances in Observed Covariates. One such method, based on the propensity score, is useful in many applications but may be biased when the assumption of strongly ignorable treatment assignment is violated. Because it is not possible to evaluate this assumption from the data, it is advisable to assess the sensitivity of conclusions to violations of strong ignorability. Lin et al. (Biomet. 1998; 54:948-963) have implemented this idea by investigating how an unmeasured covariate may affect the conclusions of an observational study. We extend their method to assess sensitivity of the treatment hazard ratio to hidden bias under a range of covariate distributions. We derive simple formulas for approximating the true from the apparent treatment hazard ratio estimated under a specific survival model, and assess the validity of these formulas in simulation studies. We demonstrate the method in an analysis of SEER-Medicare data on the effects of chemotherapy in elderly colon cancer patients.
Donald B Rubin - One of the best experts on this subject based on the ideXlab platform.
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propensity score methods for creating covariate balance in observational studies
Revista Espanola De Cardiologia, 2011Co-Authors: Cassandra Wolos Pattanayak, Donald B Rubin, Elizabeth R ZellAbstract:Randomization of treatment assignment in experiments generates treatment groups with approximately balanced baseline Covariates. However, in observational studies, where treatment assignment is not random, patients in the active treatment and control groups often differ on crucial Covariates that are related to outcomes. These covariate imbalances can lead to biased treatment effect estimates. The propensity score is the probability that a patient with particular baseline characteristics is assigned to active treatment rather than control. Though propensity scores are unknown in observational studies, by matching or subclassifying patients on estimated propensity scores, we can design observational studies that parallel randomized experiments, with approximate balance on Observed Covariates. Observational study designs based on estimated propensity scores can generate approximately unbiased treatment effect estimates. Critically, propensity score designs should be created without access to outcomes, mirroring the separation of study design and outcome analysis in randomized experiments. This paper describes the potential outcomes framework for causal inference and best practices for designing observational studies with propensity scores. We discuss the use of propensity scores in two studies assessing the effectiveness and risks of antifibrinolytic drugs during cardiac surgery.
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the design versus the analysis of observational studies for causal effects parallels with the design of randomized trials
Statistics in Medicine, 2007Co-Authors: Donald B RubinAbstract:For estimating causal effects of treatments, randomized experiments are generally considered the gold standard. Nevertheless, they are often infeasible to conduct for a variety of reasons, such as ethical concerns, excessive expense, or timeliness. Consequently, much of our knowledge of causal effects must come from non-randomized observational studies. This article will advocate the position that observational studies can and should be designed to approximate randomized experiments as closely as possible. In particular, observational studies should be designed using only background information to create subgroups of similar treated and control units, where 'similar' here refers to their distributions of background variables. Of great importance, this activity should be conducted without any access to any outcome data, thereby assuring the objectivity of the design. In many situations, this objective creation of subgroups of similar treated and control units, which are balanced with respect to Covariates, can be accomplished using propensity score methods. The theoretical perspective underlying this position will be presented followed by a particular application in the context of the US tobacco litigation. This application uses propensity score methods to create subgroups of treated units (male current smokers) and control units (male never smokers) who are at least as similar with respect to their distributions of Observed background characteristics as if they had been randomized. The collection of these subgroups then 'approximate' a randomized block experiment with respect to the Observed Covariates.
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combining propensity score matching with additional adjustments for prognostic Covariates
Journal of the American Statistical Association, 2000Co-Authors: Donald B Rubin, Neal ThomasAbstract:Abstract Propensity score matching refers to a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many Covariates. This matching is the observational study analog of randomization in ideal experiments, but is far less complete as it can only balance the distribution of Observed Covariates, whereas randomization balances the distribution of all Covariates, both Observed and unObserved. An important feature of propensity score matching is that it can be easily combined with model-based regression adjustments or with matching on a subset of special prognostic Covariates or combinations of prognostic Covariates that have been identified as being especially predictive of the outcome variables. We extend earlier results by developing approximations for the distributions of Covariates in matched samples created with linear propensity score methods for the practically important situation where matching uses both the estimat...
