scholarly journals Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

2019 ◽  
pp. 1-10
Author(s):  
Alex Karanevich ◽  
Richard Meier ◽  
Stefan Graw ◽  
Anna McGlothlin ◽  
Byron Gajewski
Keyword(s):  
Sample Size ◽  
Two Stage ◽  
Sample Size Allocation

Spatial variability-based sample size allocation for stratified sampling

CATENA ◽  
2021 ◽  
Vol 206 ◽  
pp. 105509
Author(s):  
Shuangshuang Shao ◽  
Huan Zhang ◽  
Manman Fan ◽  
Baowei Su ◽  
Jingtao Wu ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Sample Size ◽  
Stratified Sampling ◽  
Sample Size Allocation

Sample size allocation in two-phase sampling

1974 ◽  
Vol 3 (11) ◽  
pp. 1025-1040
Author(s):  
J. Sedransk ◽  
Bahadur Singh
Keyword(s):  
Sample Size ◽  
Two Phase ◽  
Sample Size Allocation ◽  
Phase Sampling ◽  
Two Phase Sampling

scholarly journals The Determination of Sample Size for Sensitive Issue Successive Survey with the Multiplication RRT Model under Two-Stage and Stratified Two-Stage Random Sampling and Its Application in Medical Survey

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Bo Yu ◽  
Xiaonan Liang ◽  
Ying Wang ◽  
Yun Liu ◽  
Qiao Chang ◽  
...  
Keyword(s):  
Sample Size ◽  
Sampling Method ◽  
Sampling Errors ◽  
Two Stage ◽  
Successive Sampling ◽  
Optimal Sample ◽  
Sensitive Issue ◽  
Survey Accuracy ◽  
The Cost

When designing the sample scheme, it is important to determine the sample size. The survey accuracy and cost of survey and sampling method should be considered comprehensively. In this article, we discuss the method of determining the sample size of complex successive sampling with rotation sample for sensitive issue and deduce the formulas for the optimal sample size under two-stage sampling and stratified two-stage sampling by using Cauchy-Schwartz inequality, respectively, so as to minimize the cost for given sampling errors and to minimize the sampling errors for given cost.

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

On Sample Size and Inference for Two‐Stage Adaptive Designs

Biometrics ◽  
2001 ◽  
Vol 57 (1) ◽  
pp. 172-177 ◽  
Author(s):  
Qing Liu ◽  
George Y. H. Chi
Keyword(s):  
Sample Size ◽  
Adaptive Designs ◽  
Two Stage

Comparison of a Two-Stage and Three-Stage Interim-Analysis Procedure

1992 ◽  
Vol 71 (1) ◽  
pp. 3-14 ◽  
Author(s):  
John E. Overall ◽  
Robert S. Atlas
Keyword(s):  
Sample Size ◽  
Interim Analysis ◽  
Type I Error ◽  
Substantial Reduction ◽  
Error Rates ◽  
Sampling Plan ◽  
Type I ◽  
Two Stage ◽  
Expected Sample Size ◽  
Analysis Strategy

A statistical model for combining p values from multiple tests of significance is used to define rejection and acceptance regions for two-stage and three-stage sampling plans. Type I error rates, power, frequencies of early termination decisions, and expected sample sizes are compared. Both the two-stage and three-stage procedures provide appropriate protection against Type I errors. The two-stage sampling plan with its single interim analysis entails minimal loss in power and provides substantial reduction in expected sample size as compared with a conventional single end-of-study test of significance for which power is in the adequate range. The three-stage sampling plan with its two interim analyses introduces somewhat greater reduction in power, but it compensates with greater reduction in expected sample size. Either interim-analysis strategy is more efficient than a single end-of-study analysis in terms of power per unit of sample size.

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