scholarly journals Inference on mean quality-adjusted lifetime using joint models for continuous quality of life process and time to event

2020 ◽  
Vol 53 (2) ◽  
pp. 165-189
Author(s):  
XIAOTIAN GAO ◽  
XINXIN DONG ◽  
CHAERYON KANG KANG ◽  
ABDUS S. WAHED
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Time To Event ◽  
Joint Models ◽  
Average Treatment ◽  
Continuous Quality ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

scholarly journals Bootstrap bias correction for average treatment effects with inverse propensity weights

2019 ◽  
Vol 52 (2) ◽  
pp. 187-200
Author(s):  
GUBHINDER KUNDHI ◽  
MARCEL VOIA
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Bias Correction ◽  
Treatment Effects ◽  
Potential Problem ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Control Groups ◽  
Average Treatment ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

Propensity score specification for optimal estimation of average treatment effect with binary response

2020 ◽  
Vol 29 (12) ◽  
pp. 3623-3640
Author(s):  
John A Craycroft ◽  
Jiapeng Huang ◽  
Maiying Kong
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Optimal Estimation ◽  
Theoretical Explanation ◽  
Average Treatment Effect ◽  
Adaptive Lasso ◽  
Confounding Bias ◽  
Average Treatment ◽  
The Impact

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject’s covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database.

scholarly journals Time-to-event comparative effectiveness of NOACs vs VKAs in newly diagnosed non-valvular atrial fibrillation patients.

2021 ◽  
Author(s):  
Blanca Gallego Luxan ◽  
Jie Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Major Bleeding ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Ischaemic Event ◽  
Newly Diagnosed ◽  
Time To Event ◽  
Oral Anticoagulant Treatment ◽  
Average Treatment ◽  
The Difference

Objective: To investigate the difference in the time-to-event probabilities of ischaemic events, major bleeding and death of NOAC vs VKAs in newly diagnosed non-valvular atrial fibrillation patients. Design: Retrospective observational cohort study. Setting: UK's Clinical Practice Research Data linked to the Hospital Episode Statistics inpatient and outpatient data, mortality data and the Patient Level Index of Multiple Deprivation. Participants: Patients over 18 years of age, with an initial diagnosis of atrial fibrillation between 1st-Mar-2011 and 31-July-2017, without a record for a valve condition, prosthesis or procedure previous to initial diagnosis, and without a record of oral anticoagulant treatment in the previous year. Intervention: Oral anticoagulant treatment with either vitamin K antagonists (VKAs) or the newer target-specific oral anticoagulants (NOACs). Main Outcome Measures: Ischaemic event, major bleeding event and death from 15 days from initial prescription up to two years follow-up. Statistical Analysis: Treatment effect was defined as the difference in time-to-event probability between NOAC and VKA treatment groups. Treatment and outcomes were modelled using an ensemble of parametric and non-parametric models, and the average and conditional average treatment effects were estimated using one-step Targeted Maximum Likelihood Estimation (TMLE). Heterogeneity of treatment effect was examined using variable importance methods in Bayesian Additive Regression Trees (BART). Results: The average treatment effect of NOAC vs VKA was consistently close to zero across all times, with a temporal average of $0.00[95\%0.00,0.00]$ for ischaemic event, $0.00\%[95\%-0.01,0.01]$ for major bleeding and $0.00[95\%-0.01,0.01]$ for death. Only history of major bleeding was found to influence the distribution of treatment effect for major bleeding, but its impact on the associated conditional average treatment effect was not significant. Conclusions: This study found no statistically significant difference between NOAC and VKA users up to two years of medication use for the prevention of ischaemic events, major bleeding or death.

Propensity score weighting analysis and treatment effect discovery

2018 ◽  
Vol 28 (8) ◽  
pp. 2439-2454 ◽  
Author(s):  
Huzhang Mao ◽  
Liang Li ◽  
Tom Greene
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Statistical Power ◽  
Variance Estimation ◽  
Propensity Score Analysis ◽  
Inverse Probability Weighting ◽  
Average Treatment Effect ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Average Treatment

Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.

scholarly journals Regression-adjusted average treatment effect estimates in stratified randomized experiments

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 935-948
Author(s):  
Hanzhong Liu ◽  
Yuehan Yang
Keyword(s):  
Treatment Effect ◽  
Asymptotic Variance ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Asymptotically Normal ◽  
Variance Estimators ◽  
Average Treatment ◽  
Effect Estimation ◽  
Regression Adjustment ◽  
And Control

Summary Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimator and the regression-adjusted average treatment effect estimator are consistent and asymptotically normal; the asymptotic variance of the latter is no greater and typically less than that of the former. We also provide conservative variance estimators that can be used to construct large-sample confidence intervals for the average treatment effect.

scholarly journals Impact of Training of Small-Scale Dairy Farmers on Milk Production and Income in Ethiopia

2020 ◽  
Vol 24 (3) ◽  
pp. 1-8
Author(s):  
Wongel Getachew Seble ◽  
Kubota Satoko ◽  
Kanayama Toshihisa ◽  
Tiana Navalona Randrianantoandro ◽  
Hiroichi Kono
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Milk Production ◽  
Treatment Effect ◽  
Smallholder Farmers ◽  
Average Treatment Effect ◽  
Dairy Farmers ◽  
Average Treatment ◽  
Management Condition ◽  
Smallholder Dairy

This paper examined dairy husbandry training impact on milk production and milk income under smallholder farmers’ management condition. A cross-sectional survey was conducted in two districts in Ethiopia and the data was collected from a total of 180 smallholder dairy farmers (60 of the participants were trained on dairy husbandry practices). Propensity Score Matching (PSM) technique was employed to construct suitable comparable group and to calculate the average treatment effect on the treated sample. The average treatment effect on the treated shows that dairy husbandry training increased milk production, volume of milk processed and milk income by about 21.7%, 56.5% and 22.5% respectively. This study confirms that training on dairy husbandry plays great role to bring change in dairy technology adoption which further enhance milk production and milk income under smallholder farmers’ management condition. Keywords: milk income; milk production; Ethiopia; propensity score matching; smallholder dairy farmers, training

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