Semiparametric Analysis of Treatment Effect via Failure Probability Ratio and the Ratio of Cumulative Hazards

2013 ◽  
pp. 329-351
Author(s):  
Song Yang
Keyword(s):  
Failure Probability ◽  
Treatment Effect ◽  
Probability Ratio ◽  
Semiparametric Analysis

Parametric Reliability Sensitivity Analysis Using Failure Probability Ratio Function

2016 ◽  
Vol 13 (04) ◽  
pp. 1641005 ◽  
Author(s):  
Pengfei Wei ◽  
Zhenzhoug Lu
Keyword(s):  
Sensitivity Analysis ◽  
Importance Sampling ◽  
Failure Probability ◽  
Probability Ratio ◽  
Sensitivity Indices ◽  
Analysis Technique ◽  
Parametric Reliability ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Distribution Parameters

Reducing the failure probability is an important task in the design of engineering structures. In this paper, a reliability sensitivity analysis technique, called failure probability ratio function, is firstly developed for providing the analysts quantitative information on failure probability reduction while one or a set of distribution parameters of model inputs are changed. Then, based on the failure probability ratio function, a global sensitivity analysis technique, called R-index, is proposed for measuring the average contribution of the distribution parameters to the failure probability while they vary in intervals. The proposed failure probability ratio function and R-index can be especially useful for failure probability reduction, reliability-based optimization and reduction of the epistemic uncertainty of parameters. The Monte Carlo simulation (MCS), Importance Sampling (IS) and Truncated Importance Sampling (TIS) procedures, which need only a set of samples for implementing them, are introduced for efficiently computing the proposed sensitivity indices. A numerical example is introduced for illustrating the engineering significance of the proposed sensitivity indices and verifying the efficiency and accuracy of the MCS, IS and TIS procedures. At last, the proposed sensitivity techniques are applied to a planar 10-bar structure for achieving a targeted 80% reduction of the failure probability.

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

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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