The Experts below are selected from a list of 342222 Experts worldwide ranked by ideXlab platform
Monica Hernandez Alava - One of the best experts on this subject based on the ideXlab platform.
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mapping the fact b instrument to eq 5d 3l in patients with breast cancer using adjusted limited Dependent Variable mixture models versus response mapping
2018Co-Authors: Laura A Gray, Allan Wailoo, Monica Hernandez AlavaAbstract:Abstract Background Preference-based measures of health, such as the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), are required to calculate quality-adjusted life-years for use in cost-effectiveness analysis, but are often not recorded in clinical studies. In these cases, mapping can be used to estimate preference-based measures. Objectives To model the relationship between the EQ-5D-3L and the Functional Assessment of Cancer Therapy—Breast Cancer (FACT-B) instrument, comparing indirect and direct mapping methods, and the use of FACT-B summary score versus FACT-B subscale scores. Methods We used data from three clinical studies for advanced breast cancer providing 11,958 observations with full information on FACT-B and the EQ-5D-3L. We compared direct mapping using adjusted limited Dependent Variable mixture models (ALDVMMs) with indirect mapping using seemingly unrelated ordered probit models. The EQ-5D-3L was estimated as a function of FACT-B and other patient-related covariates. Results The use of FACT-B subscale scores was better than using the total FACT-B score. A good fit to the observed data was observed across the entire range of disease severity in all models. ALDVMMs outperformed the indirect mapping. The breast cancer–specific scale had a strong influence in predicting the pain/discomfort and self-care dimensions of the EQ-5D-3L. Conclusions This article adds to the growing literature that demonstrates the performance of the ALDVMM method for mapping. Regardless of which model is used, the subscales of FACT-B should be included as inDependent Variables wherever possible. The breast cancer–specific subscale of FACT-B is important in predicting the EQ-5D-3L. This suggests that generic cancer measures should not be used for utility mapping in patients with breast cancer.
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tails from the peak district adjusted limited Dependent Variable mixture models of eq 5d questionnaire health state utility values
2012Co-Authors: Monica Hernandez Alava, Allan Wailoo, Roberta AraAbstract:Abstract Objectives Health utility data generated by using the EuroQol five-dimensional (EQ-5D) questionnaire are right bounded at 1 with a substantial gap to the next set of observations, left bounded, and multimodal. These features present challenges to the estimation of the effect of clinical and socioeconomic characteristics on health utilities. Our objective was to develop and demonstrate an appropriate method for dealing with these features. Methods We developed a statistical model that incorporates an adjusted limited Dependent Variable approach to reflect the upper bound and the large gap in feasible EQ-5D questionnaire values. Further flexibility was then gained by adopting a mixture modeling framework to address the multimodality of the EQ-5D questionnaire distribution. We compared the performance of these approaches with that of those frequently adopted in the literature (linear and Tobit models) by using data from a clinical trial of patients with rheumatoid arthritis. Results We found that three latent classes are appropriate in estimating EQ-5D questionnaire values from function, pain, and sociodemographic factors. Superior performance of the adjusted limited Dependent Variable mixture model was achieved in terms of Akaike and Bayesian information criteria, root mean square error, and mean absolute error. Unlike other approaches, the adjusted limited Dependent Variable mixture model fits the data well at high EQ-5D questionnaire levels and cannot predict unfeasible EQ-5D questionnaire values. Conclusions The distribution of the EQ-5D questionnaire is characterized by features that raise statistical challenges. It is well known that standard approaches do not perform well for this reason. This article developed an appropriate method to reflect these features by combining limited Dependent Variable and mixture modeling and demonstrated superior performance in a rheumatoid arthritis setting. Further refinement of the general framework and testing in other data sets are warranted. Analysis of utility data should apply methods that recognize the distributional features of the data.
Roberta Ara - One of the best experts on this subject based on the ideXlab platform.
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tails from the peak district adjusted limited Dependent Variable mixture models of eq 5d questionnaire health state utility values
2012Co-Authors: Monica Hernandez Alava, Allan Wailoo, Roberta AraAbstract:Abstract Objectives Health utility data generated by using the EuroQol five-dimensional (EQ-5D) questionnaire are right bounded at 1 with a substantial gap to the next set of observations, left bounded, and multimodal. These features present challenges to the estimation of the effect of clinical and socioeconomic characteristics on health utilities. Our objective was to develop and demonstrate an appropriate method for dealing with these features. Methods We developed a statistical model that incorporates an adjusted limited Dependent Variable approach to reflect the upper bound and the large gap in feasible EQ-5D questionnaire values. Further flexibility was then gained by adopting a mixture modeling framework to address the multimodality of the EQ-5D questionnaire distribution. We compared the performance of these approaches with that of those frequently adopted in the literature (linear and Tobit models) by using data from a clinical trial of patients with rheumatoid arthritis. Results We found that three latent classes are appropriate in estimating EQ-5D questionnaire values from function, pain, and sociodemographic factors. Superior performance of the adjusted limited Dependent Variable mixture model was achieved in terms of Akaike and Bayesian information criteria, root mean square error, and mean absolute error. Unlike other approaches, the adjusted limited Dependent Variable mixture model fits the data well at high EQ-5D questionnaire levels and cannot predict unfeasible EQ-5D questionnaire values. Conclusions The distribution of the EQ-5D questionnaire is characterized by features that raise statistical challenges. It is well known that standard approaches do not perform well for this reason. This article developed an appropriate method to reflect these features by combining limited Dependent Variable and mixture modeling and demonstrated superior performance in a rheumatoid arthritis setting. Further refinement of the general framework and testing in other data sets are warranted. Analysis of utility data should apply methods that recognize the distributional features of the data.
Allan Wailoo - One of the best experts on this subject based on the ideXlab platform.
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mapping the fact b instrument to eq 5d 3l in patients with breast cancer using adjusted limited Dependent Variable mixture models versus response mapping
2018Co-Authors: Laura A Gray, Allan Wailoo, Monica Hernandez AlavaAbstract:Abstract Background Preference-based measures of health, such as the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), are required to calculate quality-adjusted life-years for use in cost-effectiveness analysis, but are often not recorded in clinical studies. In these cases, mapping can be used to estimate preference-based measures. Objectives To model the relationship between the EQ-5D-3L and the Functional Assessment of Cancer Therapy—Breast Cancer (FACT-B) instrument, comparing indirect and direct mapping methods, and the use of FACT-B summary score versus FACT-B subscale scores. Methods We used data from three clinical studies for advanced breast cancer providing 11,958 observations with full information on FACT-B and the EQ-5D-3L. We compared direct mapping using adjusted limited Dependent Variable mixture models (ALDVMMs) with indirect mapping using seemingly unrelated ordered probit models. The EQ-5D-3L was estimated as a function of FACT-B and other patient-related covariates. Results The use of FACT-B subscale scores was better than using the total FACT-B score. A good fit to the observed data was observed across the entire range of disease severity in all models. ALDVMMs outperformed the indirect mapping. The breast cancer–specific scale had a strong influence in predicting the pain/discomfort and self-care dimensions of the EQ-5D-3L. Conclusions This article adds to the growing literature that demonstrates the performance of the ALDVMM method for mapping. Regardless of which model is used, the subscales of FACT-B should be included as inDependent Variables wherever possible. The breast cancer–specific subscale of FACT-B is important in predicting the EQ-5D-3L. This suggests that generic cancer measures should not be used for utility mapping in patients with breast cancer.
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tails from the peak district adjusted limited Dependent Variable mixture models of eq 5d questionnaire health state utility values
2012Co-Authors: Monica Hernandez Alava, Allan Wailoo, Roberta AraAbstract:Abstract Objectives Health utility data generated by using the EuroQol five-dimensional (EQ-5D) questionnaire are right bounded at 1 with a substantial gap to the next set of observations, left bounded, and multimodal. These features present challenges to the estimation of the effect of clinical and socioeconomic characteristics on health utilities. Our objective was to develop and demonstrate an appropriate method for dealing with these features. Methods We developed a statistical model that incorporates an adjusted limited Dependent Variable approach to reflect the upper bound and the large gap in feasible EQ-5D questionnaire values. Further flexibility was then gained by adopting a mixture modeling framework to address the multimodality of the EQ-5D questionnaire distribution. We compared the performance of these approaches with that of those frequently adopted in the literature (linear and Tobit models) by using data from a clinical trial of patients with rheumatoid arthritis. Results We found that three latent classes are appropriate in estimating EQ-5D questionnaire values from function, pain, and sociodemographic factors. Superior performance of the adjusted limited Dependent Variable mixture model was achieved in terms of Akaike and Bayesian information criteria, root mean square error, and mean absolute error. Unlike other approaches, the adjusted limited Dependent Variable mixture model fits the data well at high EQ-5D questionnaire levels and cannot predict unfeasible EQ-5D questionnaire values. Conclusions The distribution of the EQ-5D questionnaire is characterized by features that raise statistical challenges. It is well known that standard approaches do not perform well for this reason. This article developed an appropriate method to reflect these features by combining limited Dependent Variable and mixture modeling and demonstrated superior performance in a rheumatoid arthritis setting. Further refinement of the general framework and testing in other data sets are warranted. Analysis of utility data should apply methods that recognize the distributional features of the data.
Lung-fei Lee - One of the best experts on this subject based on the ideXlab platform.
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A spatial autoregressive model with a nonlinear transformation of the Dependent Variable
2015Co-Authors: Lung-fei LeeAbstract:Abstract This paper develops a nonlinear spatial autoregressive model. Of particular interest is a structural interaction model for share data. We consider possible instrumental Variable (IV) and maximum likelihood estimation (MLE) for this model, and analyze asymptotic properties of the IV and MLE based on the notion of spatial near-epoch dependence. We also design a statistical test to compare the nonlinear transformation against alternatives. Monte Carlo experiments are designed to investigate finite sample performance of the proposed estimates and the sizes and powers of the test.
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estimation of spatial panel data models with randomly missing data in the Dependent Variable
2013Co-Authors: Wei Wang, Lung-fei LeeAbstract:Abstract We suggest and compare different methods for estimating spatial autoregressive panel models with randomly missing data in the Dependent Variable. We start with a random effects model and then generalize the model by introducing the spatial Mundlak approach. A nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that the generalized nonlinear least squares, best generalized two-stage least squares with imputation, and best method of moments estimators have identical asymptotic variances. The robustness of these estimation methods against unknown heteroscedasticity is also stressed since the traditional maximum likelihood approach yields inconsistent estimates under unknown heteroscedasticity. We provide finite sample evidence through Monte Carlo experiments.
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estimation of spatial autoregressive models with randomly missing data in the Dependent Variable
2013Co-Authors: Wei Wang, Lung-fei LeeAbstract:Summary We suggest and compare different methods for estimating spatial autoregressive models with randomly missing data in the Dependent Variable. Aside from the traditional expectation-maximization (EM) algorithm, a nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that generalized nonlinear least squares, best generalized two-stage least squares with imputation and best method of moments estimators have identical asymptotic variances. These methods are less efficient than maximum likelihood estimation implemented with the EM algorithm. When unknown heteroscedasticity exists, however, EM estimation produces inconsistent estimates. Under this situation, these methods outperform EM. We provide finite sample evidence through Monte Carlo experiments.
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analysis of panels and limited Dependent Variable models
1999Co-Authors: Cheng Hsiao, Hashem M Pesaran, Kajal Lahiri, Lung-fei LeeAbstract:This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited Dependent Variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited Dependent Variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.
Masahiro Yamamoto - One of the best experts on this subject based on the ideXlab platform.
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On Time-Fractional Diffusion Equations with Space-Dependent Variable Order
2018Co-Authors: Yavar Kian, Eric Soccorsi, Masahiro YamamotoAbstract:This paper deals with mathematical problems related to space-Dependent anomalous diffusion processes. Namely, we investigate diffusion equations with time-fractional derivatives of space-Dependent Variable order. We establish that Variable-order time-fractional Cauchy problems admit a unique weak solution and prove that the space-Dependent Variable-order coefficient is uniquely determined by the knowledge of a suitable time sequence of partial initial-boundary maps.
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a uniqueness result for time fractional diffusion equations with space Dependent Variable order
2017Co-Authors: Yavar Kian, Eric Soccorsi, Masahiro YamamotoAbstract:We investigate diffusion equations with time-fractional derivatives of space-Dependent Variable order. We examine the well-posedness issue and prove that the space-Dependent Variable order coefficient is uniquely determined among other coefficients of these equations, by the knowledge of a suitable time-sequence of partial Dirichlet-to-Neumann maps.
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on time fractional diffusion equations with space Dependent Variable order
2017Co-Authors: Yavar Kian, Eric Soccorsi, Masahiro YamamotoAbstract:We investigate diffusion equations with time-fractional derivatives of space-Dependent Variable order. We examine the well-posedness issue and prove that the space-Dependent Variable order coefficient is uniquely determined among other coefficients of these equations, by the knowledge of a suitable time-sequence of partial Dirichlet-to-Neumann maps.
