scholarly journals On qualitative analysis of a discrete time SIR epidemical model

2021 ◽  
pp. 100067
Author(s):  
J. Hallberg Szabadváry ◽  
Y. Zhou
Keyword(s):  
Qualitative Analysis ◽  
Discrete Time

Qualitative analysis of switched discrete-time descriptor systems

2009 ◽  
Vol 7 (4) ◽  
pp. 512-519 ◽  
Author(s):  
Guisheng Zhai ◽  
Xuping Xu ◽  
Joe Imae ◽  
Tomoaki Kobayashi
Keyword(s):  
Qualitative Analysis ◽  
Discrete Time ◽  
Descriptor Systems

scholarly journals Correction to: Qualitative analysis of a discrete-time phytoplankton–zooplankton model with Holling type-II response and toxicity

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Muhammad Salman Khan ◽  
Maria Samreen ◽  
Hassen Aydi ◽  
Manuel De la Sen
Keyword(s):  
Qualitative Analysis ◽  
Discrete Time ◽  
Type Ii

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.

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