A powerful test for the maximum treatment effect in thorough QT/QTc studies

2021 ◽  
Author(s):  
Yuhao Deng ◽  
Fangyi Chen ◽  
Yang Li ◽  
Kaihuan Qian ◽  
Rui Wang ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Powerful Test

A Locally Most Powerful Test for Detecting a Treatment Effect in the Presence of Nonresponders

1994 ◽  
Vol 36 (3) ◽  
pp. 373-384 ◽  
Author(s):  
Mehdi Razzaghi ◽  
A. Nanthakumar
Keyword(s):  
Treatment Effect ◽  
Powerful Test ◽  
Locally Most Powerful Test

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.

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

Treatment Effect of Omalizumab on Severe Pediatric Atopic Dermatitis (the ADAPT Randomized Clinical Trial)

PEDIATRICS ◽  
2020 ◽  
Vol 146 (Supplement 4) ◽  
pp. S342-S343
Author(s):  
Waheeda Samady ◽  
Ruchi Gupta
Keyword(s):  
Clinical Trial ◽  
Atopic Dermatitis ◽  
Randomized Clinical Trial ◽  
Treatment Effect

Plasma Treatment Effect on the Paramagnetic Species of Barley Seed Radical's Intensity: An EPR study

2020 ◽  
Author(s):  
Pankaj Attri ◽  
Anan Teruki ◽  
Ryo Arita ◽  
Takamasa Okumura ◽  
Hayate Tanaka ◽  
...  
Keyword(s):  
Plasma Treatment ◽  
Treatment Effect ◽  
Barley Seed ◽  
Paramagnetic Species
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