Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.We consider their estimation in observation...Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates.To obtain valid and efficient treatment effect estimators,we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes.These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific.We justify our choice from efficiency point of view.We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.展开更多
In personalised medicine,the goal is tomake a treatment recommendation for each patient with a given set of covariates tomaximise the treatment benefitmeasured by patient’s response to the treatment.In application,su...In personalised medicine,the goal is tomake a treatment recommendation for each patient with a given set of covariates tomaximise the treatment benefitmeasured by patient’s response to the treatment.In application,such a treatment assignment rule is constructed using a sample training data consisting of patients’responses and covariates.Instead of modelling responses using treatments and covariates,an alternative approach is maximising a response-weighted target function whose value directly reflects the effectiveness of treatment assignments.Since the target function involves a loss function,efforts have been made recently on the choice of the loss function to ensure a computationally feasible and theoretically sound solution.We propose to use a smooth hinge loss function so that the target function is convex and differentiable,which possesses good asymptotic properties and numerical advantages.To further simplify the computation and interpretability,we focus on the rules that are linear functions of covariates and discuss their asymptotic properties.We also examine the performances of our method with simulation studies and real data analysis.展开更多
We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A specia...We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.展开更多
In this article, we consider a semiparametric model for contrast function which is defined asthe conditional expected outcome difference under comparative treatments. The contrast function can be used to recommend tre...In this article, we consider a semiparametric model for contrast function which is defined asthe conditional expected outcome difference under comparative treatments. The contrast function can be used to recommend treatment for better average outcomes. Existing approachesmodel the contrast function either parametrically or nonparametrically. We believe our approachimproves interpretability over the non-parametric approach while enhancing robustness overthe parametric approach. Without explicit estimation of the nonparametric part of our model,we show that a kernel-based method can identify the parametric part up to a multiplying constant. Such identification suffices for treatment recommendation. Our method is also extendedto high-dimensional settings. We study the asymptotics of the resulting estimation procedure inboth low- and high-dimensional cases. We also evaluate our method in simulation studies andreal data analyses.展开更多
基金supported by the National Natural Science Foundation of China(11871287,11831008)the Natural Science Foundation of Tianjin(18JCYBJC41100)+3 种基金the Fundamental Research Funds for the Central Universitiesthe Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin,the Chinese 111 Project(B14019)the U.S.National Science Foundation(DMS-1612873 and DMS-1914411partially supported through a Patient-Centered Outcomes Research Institute(PCORI)Award(ME-1409-21219).
文摘Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation.We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates.To obtain valid and efficient treatment effect estimators,we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes.These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific.We justify our choice from efficiency point of view.We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.
基金Research reported in this article was partially funded through a Patient-Centered Outcomes Research Institute(PCORI)Award[ME-1409-21219]The second author’s research was also partially supported by the Chinese 111 Project[B14019]the US National Science Foundation[grant number DMS-1612873].
文摘In personalised medicine,the goal is tomake a treatment recommendation for each patient with a given set of covariates tomaximise the treatment benefitmeasured by patient’s response to the treatment.In application,such a treatment assignment rule is constructed using a sample training data consisting of patients’responses and covariates.Instead of modelling responses using treatments and covariates,an alternative approach is maximising a response-weighted target function whose value directly reflects the effectiveness of treatment assignments.Since the target function involves a loss function,efforts have been made recently on the choice of the loss function to ensure a computationally feasible and theoretically sound solution.We propose to use a smooth hinge loss function so that the target function is convex and differentiable,which possesses good asymptotic properties and numerical advantages.To further simplify the computation and interpretability,we focus on the rules that are linear functions of covariates and discuss their asymptotic properties.We also examine the performances of our method with simulation studies and real data analysis.
基金This research was partially supported through a PatientCentered Outcomes Research Institute(PCORI)Award(ME-1409-21219)This research was also supported by the National Natural Science Foundation of China(11501208)+2 种基金Fundamental Research Funds for the Central Universities,National Social Science Foundation(13BTJ009)the Chinese 111 Project grant(B14019)the U.S.National Science Foundation(DMS-1305474 and DMS-1612873).
文摘We consider the estimation of causal treatment effect using nonparametric regression orinverse propensity weighting together with sufficient dimension reduction for searching lowdimensional covariate subsets. A special case of this problem is the estimation of a responseeffect with data having ignorable missing response values. An issue that is not well addressedin the literature is whether the estimation of the low-dimensional covariate subsets by sufficient dimension reduction has an impact on the asymptotic variance of the resulting causaleffect estimator. With some incorrect or inaccurate statements, many researchers believe thatthe estimation of the low-dimensional covariate subsets by sufficient dimension reduction doesnot affect the asymptotic variance. We rigorously establish a result showing that this is nottrue unless the low-dimensional covariate subsets include some covariates superfluous for estimation, and including such covariates loses efficiency. Our theory is supplemented by somesimulation results.
基金This research was partially supported through a PatientCentered Outcomes Research Institute(PCORI)award[ME-1409-21219]The first and third authors’research was partially supported by the Chinese Ministry of Education 111 Project[B14019]+1 种基金the US National Science Foundation[grant number DMS-1305474][grant number DMS-1612873].
文摘In this article, we consider a semiparametric model for contrast function which is defined asthe conditional expected outcome difference under comparative treatments. The contrast function can be used to recommend treatment for better average outcomes. Existing approachesmodel the contrast function either parametrically or nonparametrically. We believe our approachimproves interpretability over the non-parametric approach while enhancing robustness overthe parametric approach. Without explicit estimation of the nonparametric part of our model,we show that a kernel-based method can identify the parametric part up to a multiplying constant. Such identification suffices for treatment recommendation. Our method is also extendedto high-dimensional settings. We study the asymptotics of the resulting estimation procedure inboth low- and high-dimensional cases. We also evaluate our method in simulation studies andreal data analyses.
