Treatment Effect Estimation in Adaptive Clinical Trials: A Review

2015 ◽  
pp. 197-202
Author(s):  
Ying Yang ◽  
Huyuan Yang
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Adaptive Clinical Trials ◽  
Treatment Effect Estimation ◽  
Effect Estimation

scholarly journals Resist the Temptation of Response-Adaptive Randomization

2020 ◽  
Vol 71 (11) ◽  
pp. 3002-3004 ◽  
Author(s):  
Michael Proschan ◽  
Scott Evans
Keyword(s):  
Infectious Disease ◽  
Clinical Trials ◽  
Selection Bias ◽  
Treatment Effect ◽  
Temporal Trends ◽  
Size Distributions ◽  
Adaptive Randomization ◽  
Long Duration ◽  
Treatment Effect Estimation ◽  
Effect Estimation

Abstract Response-adaptive randomization (RAR) has recently gained popularity in clinical trials. The intent is noble: minimize the number of participants randomized to inferior treatments and increase the amount of information about better treatments. Unfortunately, RAR causes many problems, including (1) bias from temporal trends, (2) inefficiency in treatment effect estimation, (3) volatility in sample-size distributions that can cause a nontrivial proportion of trials to assign more patients to an inferior arm, (4) difficulty of validly analyzing results, and (5) the potential for selection bias and other issues inherent to being unblinded to ongoing results. The problems of RAR are most acute in the very setting for which RAR has been proposed, namely long-duration “platform” trials and infectious disease settings where temporal trends are ubiquitous. Response-adaptive randomization can eliminate the benefits that randomization, the most powerful tool in clinical trials, provides. Use of RAR is discouraged.

On learning disentangled representations for individual treatment effect estimation

2021 ◽  
pp. 103940
Author(s):  
Jiebin Chu ◽  
Zhoujian Sun ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Zhengxing Huang
Keyword(s):  
Treatment Effect ◽  
Individual Treatment ◽  
Treatment Effect Estimation ◽  
Effect Estimation

scholarly journals A Distillation Approach to Data Efficient Individual Treatment Effect Estimation

2019 ◽  
Vol 33 ◽  
pp. 4544-4551 ◽  
Author(s):  
Maggie Makar ◽  
Adith Swaminathan ◽  
Emre Kıcıman
Keyword(s):  
Treatment Effect ◽  
Newborn Health ◽  
Real Data ◽  
Machine Learning Algorithms ◽  
Test Time ◽  
Individual Treatment ◽  
Test Case ◽  
Treatment Effect Estimation ◽  
Synthetic Datasets ◽  
Effect Estimation

The potential for using machine learning algorithms as a tool for suggesting optimal interventions has fueled significant interest in developing methods for estimating heterogeneous or individual treatment effects (ITEs) from observational data. While several methods for estimating ITEs have been recently suggested, these methods assume no constraints on the availability of data at the time of deployment or test time. This assumption is unrealistic in settings where data acquisition is a significant part of the analysis pipeline, meaning data about a test case has to be collected in order to predict the ITE. In this work, we present Data Efficient Individual Treatment Effect Estimation (DEITEE), a method which exploits the idea that adjusting for confounding, and hence collecting information about confounders, is not necessary at test time. DEITEE allows the development of rich models that exploit all variables at train time but identifies a minimal set of variables required to estimate the ITE at test time. Using 77 semi-synthetic datasets with varying data generating processes, we show that DEITEE achieves significant reductions in the number of variables required at test time with little to no loss in accuracy. Using real data, we demonstrate the utility of our approach in helping soon-to-be mothers make planning and lifestyle decisions that will impact newborn health.

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