Using Machine Learning Techniques to Improving Average Treatment Effect Estimates in Small-Scale Randomized Controlled Trials

2020 ◽  
Author(s):  
Isaac Opper
Keyword(s):  
Machine Learning ◽  
Randomized Controlled Trials ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Average Treatment ◽  
Learning Techniques

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.

scholarly journals Time to onset of cannabidiol (CBD) treatment effect in Lennox–Gastaut syndrome: Analysis from two randomized controlled trials

Epilepsia ◽  
2021 ◽  
Author(s):  
Michael Privitera ◽  
Hari Bhathal ◽  
Matthew Wong ◽  
J. Helen Cross ◽  
Elaine Wirrell ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Treatment Effect ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Time To Onset ◽  
Syndrome Analysis

Machine learning for estimating individual treatment effects in randomized controlled trials

2021 ◽  
Author(s):  
Kenneth Verstraete ◽  
Nilakash Das ◽  
Iwein Gyselinck ◽  
Maarten De Vos ◽  
Wim Janssens
Keyword(s):  
Machine Learning ◽  
Randomized Controlled Trials ◽  
Treatment Effects ◽  
Individual Treatment ◽  
Controlled Trials ◽  
Randomized Controlled

Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations

2021 ◽  
Vol 45 (1-2) ◽  
pp. 70-104
Author(s):  
Kaitlin Anderson ◽  
Gema Zamarro ◽  
Jennifer Steele ◽  
Trey Miller
Keyword(s):  
Randomized Controlled Trials ◽  
Research Design ◽  
Treatment Effect ◽  
Controlled Trials ◽  
Probability Weighting ◽  
Attrition Rates ◽  
Inverse Probability ◽  
Randomized Controlled ◽  
Two Samples ◽  
Weighting Methods

Background: In randomized controlled trials, attrition rates often differ by treatment status, jeopardizing causal inference. Inverse probability weighting methods and estimation of treatment effect bounds have been used to adjust for this bias. Objectives: We compare the performance of various methods within two samples, both generated through lottery-based randomization: one with considerable differential attrition and an augmented dataset with less problematic attrition. Research Design: We assess the performance of various correction methods within the dataset with problematic attrition. In addition, we conduct simulation analyses. Results: Within the more problematic dataset, we find the correction methods often performed poorly. Simulation analyses indicate that deviations from the underlying assumptions for bounding approaches damage the performance of estimated bounds. Conclusions: We recommend the verification of the underlying assumptions in attrition correction methods whenever possible and, when verification is not possible, using these methods with caution.

scholarly journals Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide

2018 ◽  
Vol 9 (4) ◽  
pp. 602-614 ◽  
Author(s):  
Iain J. Marshall ◽  
Anna Noel-Storr ◽  
Joël Kuiper ◽  
James Thomas ◽  
Byron C. Wallace
Keyword(s):  
Machine Learning ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled

Land-based Exercise for Osteoarthritis of the Knee: A Metaanalysis of Randomized Controlled Trials

2009 ◽  
Vol 36 (6) ◽  
pp. 1109-1117 ◽  
Author(s):  
MARLENE FRANSEN ◽  
SARA McCONNELL
Keyword(s):  
Randomized Controlled Trials ◽  
Physical Function ◽  
Knee Pain ◽  
Treatment Effect ◽  
Risk Of Bias ◽  
Controlled Trials ◽  
Therapeutic Exercise ◽  
Treatment Benefit ◽  
Randomized Controlled ◽  
Study Methodology

Objective.To determine if clinical guidelines recommending therapeutic exercise for people with knee osteoarthritis (OA) are supported by rigorous scientific evidence. To explore whether the magnitude of treatment benefit reported in randomized controlled trials (RCT) is associated with exercise dosage or study methodology.Methods.We conducted a metaanalysis of RCT comparing some form of land-based therapeutic exercise with a nonexercise group using pain and self-reported physical function outcomes.Results.The 32 included studies provided data on almost 3800 participants. Metaanalysis revealed a beneficial treatment effect: standardized mean difference (SMD) 0.40 [95% confidence interval (CI) 0.30 to 0.50] for knee pain; SMD 0.37 (95% CI 0.25 to 0.49) for physical function. While the pooled beneficial effects of the 9 RCT evaluating exercise programs providing fewer than 12 direct supervision occasions or the 9 RCT judged to have a low risk of bias remained significant and clinically relevant, the magnitude of treatment benefit pooled from these RCT was significantly smaller than the comparator group (12 or more supervision occasions, moderate to high risk of bias, respectively). The mode of treatment delivery (individual treatments, exercise classes, home program) was not significantly associated with the magnitude of treatment benefit.Conclusion.There is evidence that land-based therapeutic exercise has at least short-term benefit in terms of reduced knee pain and physical disability for people with knee OA. The magnitude of the treatment effect was significantly associated with the number of direct supervision occasions provided and study methodology (assessor blinding, adequate allocation concealment).

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