Sample-constrained partial identification with application to selection bias
Biometrika. 2022 Jul 25;110(2):485-498. doi: 10.1093/biomet/asac042. eCollection 2023 Jun.ABSTRACTMany partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Matthew J Tudball Rachael A Hughes Kate Tilling Jack Bowden Qingyuan Zhao Source Type: research
A multiplicative structural nested mean model for zero-inflated outcomes
Biometrika. 2022 Aug 19;110(2):519-536. doi: 10.1093/biomet/asac050. eCollection 2023 Jun.ABSTRACTZero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated p...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Miao Yu Wenbin Lu Shu Yang Pulak Ghosh Source Type: research
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Biometrika. 2022 Aug 13;110(2):395-410. doi: 10.1093/biomet/asac047. eCollection 2023 Jun.ABSTRACTWe propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomia...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Hunyong Cho Shannon T Holloway David J Couper Michael R Kosorok Source Type: research
Gradient-based sparse principal component analysis with extensions to online learning
Biometrika. 2022 Jul 12;110(2):339-360. doi: 10.1093/biomet/asac041. eCollection 2023 Jun.ABSTRACTSparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficient...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Yixuan Qiu Jing Lei Kathryn Roeder Source Type: research
Sample-constrained partial identification with application to selection bias
Biometrika. 2022 Jul 25;110(2):485-498. doi: 10.1093/biomet/asac042. eCollection 2023 Jun.ABSTRACTMany partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Matthew J Tudball Rachael A Hughes Kate Tilling Jack Bowden Qingyuan Zhao Source Type: research
A multiplicative structural nested mean model for zero-inflated outcomes
Biometrika. 2022 Aug 19;110(2):519-536. doi: 10.1093/biomet/asac050. eCollection 2023 Jun.ABSTRACTZero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated p...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Miao Yu Wenbin Lu Shu Yang Pulak Ghosh Source Type: research
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Biometrika. 2022 Aug 13;110(2):395-410. doi: 10.1093/biomet/asac047. eCollection 2023 Jun.ABSTRACTWe propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomia...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Hunyong Cho Shannon T Holloway David J Couper Michael R Kosorok Source Type: research
Gradient-based sparse principal component analysis with extensions to online learning
Biometrika. 2022 Jul 12;110(2):339-360. doi: 10.1093/biomet/asac041. eCollection 2023 Jun.ABSTRACTSparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficient...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Yixuan Qiu Jing Lei Kathryn Roeder Source Type: research
Sample-constrained partial identification with application to selection bias
Biometrika. 2022 Jul 25;110(2):485-498. doi: 10.1093/biomet/asac042. eCollection 2023 Jun.ABSTRACTMany partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Matthew J Tudball Rachael A Hughes Kate Tilling Jack Bowden Qingyuan Zhao Source Type: research
A multiplicative structural nested mean model for zero-inflated outcomes
Biometrika. 2022 Aug 19;110(2):519-536. doi: 10.1093/biomet/asac050. eCollection 2023 Jun.ABSTRACTZero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated p...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Miao Yu Wenbin Lu Shu Yang Pulak Ghosh Source Type: research
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Biometrika. 2022 Aug 13;110(2):395-410. doi: 10.1093/biomet/asac047. eCollection 2023 Jun.ABSTRACTWe propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomia...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Hunyong Cho Shannon T Holloway David J Couper Michael R Kosorok Source Type: research
Gradient-based sparse principal component analysis with extensions to online learning
Biometrika. 2022 Jul 12;110(2):339-360. doi: 10.1093/biomet/asac041. eCollection 2023 Jun.ABSTRACTSparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficient...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Yixuan Qiu Jing Lei Kathryn Roeder Source Type: research
Sample-constrained partial identification with application to selection bias
Biometrika. 2022 Jul 25;110(2):485-498. doi: 10.1093/biomet/asac042. eCollection 2023 Jun.ABSTRACTMany partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Matthew J Tudball Rachael A Hughes Kate Tilling Jack Bowden Qingyuan Zhao Source Type: research
A multiplicative structural nested mean model for zero-inflated outcomes
Biometrika. 2022 Aug 19;110(2):519-536. doi: 10.1093/biomet/asac050. eCollection 2023 Jun.ABSTRACTZero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated p...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Miao Yu Wenbin Lu Shu Yang Pulak Ghosh Source Type: research
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Biometrika. 2022 Aug 13;110(2):395-410. doi: 10.1093/biomet/asac047. eCollection 2023 Jun.ABSTRACTWe propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time-point. The estimator is constructed using generalized random survival forests and can have polynomia...
Source: Biometrika - May 17, 2023 Category: Biotechnology Authors: Hunyong Cho Shannon T Holloway David J Couper Michael R Kosorok Source Type: research
