scholarly journals An Efficient Message Evaluation Protocol: Two Empirical Analyses on Positional Effects and Optimal Sample Size

2019 ◽  
Vol 24 (10) ◽  
pp. 761-769 ◽  
Author(s):  
Minji Kim ◽  
Joseph N. Cappella
Keyword(s):  
Sample Size ◽  
Optimal Sample Size ◽  
Evaluation Protocol ◽  
Optimal Sample ◽  
Positional Effects ◽  
Message Evaluation ◽  
Empirical Analyses

Effectiveness and policies analysis of pool testing method for COVID-19

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Liu ◽  
Yi Chen ◽  
Kefan Xie ◽  
Jia Liu
Keyword(s):  
Sample Size ◽  
Infection Rate ◽  
Large Population ◽  
Optimal Sample Size ◽  
Content Type ◽  
Testing Method ◽  
Multiple Sampling ◽  
Optimal Sample ◽  
Test Result ◽  
Sampling Approach

PurposeThis research aims to figure out whether the pool testing method of SARS-CoV-2 for COVID-19 is effective and the optimal sample size is in one bunch. Additionally, since the infection rate was unknown at the beginning, this research aims to propose a multiple sampling approach that enables the pool testing method to be utilized successfully.Design/methodology/approachThe authors verify that the pool testing method of SARS-CoV-2 for COVID-19 is effective under the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected.FindingsIf the infection rate is extremely low, while the same number of detection kits is used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. The pool testing method is effective only when the infection rate is less than 0.3078. The higher the infection rate, the smaller the optimal sample size in one bunch. If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N).Originality/valueThis research proves that the pool testing method is not only suitable for the situation of the shortage of detection kits but also the situation of the overall or sampling detection for a large population. More importantly, it calculates the optimal sample size in one bunch corresponding to different infection rates. Additionally, a multiple sampling approach is proposed. In this approach, the whole testing process is divided into several rounds in which the sample sizes in one bunch are different. The actual infection rate is estimated gradually precisely by sampling inspection in each round.

Estimation of optimum sample size allocation: An illustration with body mass index for evaluating the effect of a dietetic supplement

2019 ◽  
Vol 12 (08) ◽  
pp. 1950086
Author(s):  
Carlos N. Bouza-Herrera ◽  
Sira M. Allende-Alonso ◽  
Gajendra K. Vishwakarma ◽  
Neha Singh
Keyword(s):  
Body Mass Index ◽  
Numerical Methods ◽  
Sample Size ◽  
Body Mass ◽  
Optimal Allocation ◽  
Computing Time ◽  
Optimal Sample Size ◽  
Optimum Sample Size ◽  
Optimal Sample ◽  
Sample Size Allocation

In many medical researches, it is needed to determine the optimal sample size allocation in a heterogeneous population. This paper proposes the algorithm for optimal sample size allocation. We consider the optimal allocation problem as an optimization problem and the solution is obtained by using Bisection, Secant, Regula–Falsi and other numerical methods. The performance of the algorithm for different numerical methods are analyzed and evaluated in terms of computing time, number of iterations and gain in accuracy using stratification. The efficacy of algorithm is evaluated for the response in terms of body mass index (BMI) to the dietetic supplement with diabetes mellitus, HIV/AIDS and cancer post-operatory recovery patients.

scholarly journals Multiple sensitive estimation and optimal sample size allocation in the item sum technique

2017 ◽  
Vol 60 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Pier Francesco Perri ◽  
María del Mar Rueda García ◽  
Beatriz Cobo Rodríguez
Keyword(s):  
Sample Size ◽  
Optimal Sample Size ◽  
Optimal Sample ◽  
Sample Size Allocation
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