scholarly journals Confounder selection strategies targeting stable treatment effect estimators

2020 ◽  
Vol 40 (3) ◽  
pp. 607-630
Author(s):  
Wen Wei Loh ◽  
Stijn Vansteelandt
Keyword(s):  
Treatment Effect ◽  
Selection Strategies ◽  
Confounder Selection

Simulation Study of Confounder-Selection Strategies

1993 ◽  
Vol 138 (11) ◽  
pp. 923-936 ◽  
Author(s):  
George Maldonado ◽  
Sander Greenland
Keyword(s):  
Simulation Study ◽  
Selection Strategies ◽  
Confounder Selection

scholarly journals Variable Selection in Causal Inference using a Simultaneous Penalization Method

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Ashkan Ertefaie ◽  
Masoud Asgharian ◽  
David A. Stephens
Keyword(s):  
Variable Selection ◽  
Treatment Effect ◽  
Robust Regression ◽  
Allocation Model ◽  
Treatment Allocation ◽  
Average Growth ◽  
Penalization Method ◽  
Confounder Selection ◽  
Doubly Robust ◽  
Assignment Models

AbstractIn the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in estimation of the treatment effect. We propose a variable selection method using a penalized objective function that is based on both the outcome and treatment assignment models. The proposed method facilitates confounder selection in high-dimensional settings. We show that under some mild conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. Using the proposed method we analyze a set of data on economic growth and study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Response selection strategies and calibration of confidence judgments.

1984 ◽  
Author(s):  
Carl M. Allwood ◽  
Henry Montgomery
Keyword(s):  
Response Selection ◽  
Confidence Judgments ◽  
Selection Strategies

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

Using Discount Rate to Predict Treatment Effect and Relapse

2007 ◽  
Author(s):  
Yun Zhang ◽  
Min Yu ◽  
Xinyue Zhou ◽  
Jingjing Lin ◽  
Yi Ni
Keyword(s):  
Discount Rate ◽  
Treatment Effect
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