Optimality of testing procedures for survival data in the nonproportional hazards setting

Biometrics ◽  
2020 ◽  
Author(s):  
Andrea Arfè ◽  
Brian Alexander ◽  
Lorenzo Trippa
Keyword(s):  
Survival Data ◽  
Nonproportional Hazards ◽  
Testing Procedures

A Method for Sequential Analysis of Survival Data with Nonproportional Hazards

Biometrics ◽  
1998 ◽  
Vol 54 (3) ◽  
pp. 1072 ◽  
Author(s):  
M. R. Sooriyarachchi ◽  
John Whitehead
Keyword(s):  
Survival Data ◽  
Sequential Analysis ◽  
Nonproportional Hazards

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.

Automation of Testing Procedures.

1969 ◽  
Vol 24 (12) ◽  
pp. 1174-1174
Author(s):  
Joseph L. Moses
Keyword(s):  
Testing Procedures

Rubber Fracture Characterization Using J-Integral

1988 ◽  
Vol 16 (1) ◽  
pp. 44-60 ◽  
Author(s):  
C. L. Chow ◽  
J. Wang ◽  
P. N. Tse
Keyword(s):  
Crack Initiation ◽  
Carbon Black ◽  
Natural Rubber ◽  
Fracture Parameter ◽  
J Integral ◽  
Specimen Length ◽  
Fracture Characterization ◽  
Premature Failure ◽  
Testing Procedures ◽  
Material Evaluation

Abstract The use of the J-integral to investigate fracture characterization in a carbon black reinforced natural rubber is described. Three applications to crack initiation are included: two based on the use of a hypothetical zero specimen length and one on conventional testing procedures for metals. While the validity of the zero-length methods is questionable, the conventional method yielded a consistent Jc value of 1.01 N/mm for a typical tire compound. This value was obtained from 24 combinations of varying specimen geometries and pre-crack lengths. The J-integral is revealed as a valid fracture parameter that is applicable not only for material evaluation but also for designing tire structures to resist premature failure. These conclusions disagree with those from an earlier investigation, so the causes for the discrepancies are examined and discussed.

Dynamics of the parachute sling - Testing procedures and evaluations

1991 ◽  
Author(s):  
D. PREVORSEK ◽  
H. CHIN ◽  
Y. KWON
Keyword(s):  
Testing Procedures
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