scholarly journals Doubly robust inference for targeted minimum loss-based estimation in randomized trials with missing outcome data

2017 ◽  
Vol 36 (24) ◽  
pp. 3807-3819 ◽  
Author(s):  
Iván Díaz ◽  
Mark J. van der Laan
Keyword(s):  
Randomized Trials ◽  
Outcome Data ◽  
Robust Inference ◽  
Minimum Loss ◽  
Doubly Robust

Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported outcome data

2018 ◽  
Vol 27 (9) ◽  
pp. 2125-2131 ◽  
Author(s):  
Melanie L. Bell ◽  
Nicholas J. Horton ◽  
Haryana M. Dhillon ◽  
Victoria J. Bray ◽  
Janette Vardy
Keyword(s):  
Generalized Estimating Equations ◽  
Randomized Trials ◽  
Estimating Equations ◽  
Patient Reported Outcome ◽  
Outcome Data ◽  
Patient Reported ◽  
Generalized Estimating

Antithrombotic Therapy After Revascularization in Patients With Peripheral Arterial Disease: What Is Here, What Is Next

2019 ◽  
Vol 53 (4) ◽  
pp. 325-336 ◽  
Author(s):  
Mostafa R. Amer ◽  
Surya Teja Chaturvedula ◽  
Saurabh Joshi ◽  
Joseph Ingrassia
Keyword(s):  
Clinical Practice ◽  
Peripheral Arterial Disease ◽  
Antithrombotic Therapy ◽  
Randomized Trials ◽  
Arterial Disease ◽  
Outcome Data ◽  
Significant Heterogeneity ◽  
Treatment Protocols ◽  
Peripheral Arterial

Objective: The optimal antithrombotic regimen in peripheral arterial disease (PAD) is not known, leading to significant variations in antithrombotic treatment protocols in randomized trials and clinical practice. In device trials, antithrombotic regimens in patients receiving peripheral vascular interventions have not been clearly reported on. This review summarizes and discusses the most recent evidence on this topic to provide a potential guide to clinical practice. Methods: A search of the literature was done for publications that reported outcomes of major PAD device trials. Reported outcomes and various antithrombotic regimens were studied. Results: Use of antithrombotic therapy varied significantly between various device trials. Reporting of antithrombotic regimens at the time of follow-up is lacking. Conclusion: Outcome data on optimal antithrombotic regimens are presently lacking largely due to the significant heterogeneity and underreporting of antithrombotic regimens at follow-up among prior clinical trials. Standardization and reporting of precise antithrombotic regimens at various points of follow-up in device trials of patients with PAD should be attempted so as to minimize differences in treatment patterns when evaluating new devices.

scholarly journals Randomized trials with missing outcome data: how to analyze and what to report

2014 ◽  
Vol 186 (15) ◽  
pp. 1153-1157 ◽  
Author(s):  
Rolf H.H. Groenwold ◽  
Karel G.M. Moons ◽  
Jan P. Vandenbroucke
Keyword(s):  
Randomized Trials ◽  
Outcome Data

scholarly journals Balancing Score Adjusted Targeted Minimum Loss-based Estimation

2015 ◽  
Vol 3 (2) ◽  
pp. 139-155 ◽  
Author(s):  
Samuel David Lendle ◽  
Bruce Fireman ◽  
Mark J. van der Laan
Keyword(s):  
Propensity Score ◽  
Bias Reduction ◽  
Average Treatment Effect ◽  
Causal Effects ◽  
Simulation Studies ◽  
Minimum Loss ◽  
Inverse Probability ◽  
Matching Estimator ◽  
Class Of Estimators ◽  
Doubly Robust

AbstractAdjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies.

scholarly journals Doubly Robust Inference With Nonprobability Survey Samples

2019 ◽  
Vol 115 (532) ◽  
pp. 2011-2021 ◽  
Author(s):  
Yilin Chen ◽  
Pengfei Li ◽  
Changbao Wu
Keyword(s):  
Robust Inference ◽  
Doubly Robust
Sign in / Sign up

Export Citation Format

Share Document