Unequal sample size allocation to optimal design for binomial logistic models

1987 ◽  
Vol 28 (1) ◽  
pp. 285-290 ◽  
Author(s):  
R. K. Jain
Keyword(s):  
Optimal Design ◽  
Sample Size ◽  
Logistic Models ◽  
Unequal Sample Size ◽  
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 Sample Size Determination and Optimal Design of Simple Pretest-Posttest Experimental Designs: Introduction, Software, and Illustrations

2021 ◽  
Author(s):  
Metin Bulus
Keyword(s):  
Optimal Design ◽  
Sample Size ◽  
Small Sample ◽  
Experimental Designs ◽  
Sample Size Determination ◽  
Size Determination ◽  
Sample Sizes ◽  
Control Groups ◽  
Small Sample Sizes ◽  
And Control

A recent systematic review of experimental studies conducted in Turkey between 2010 and 2020 reported that small sample sizes had been a significant drawback (Bulus and Koyuncu, 2021). A small chunk of the studies were small-scale true experiments (subjects randomized into the treatment and control groups). The remaining studies consisted of quasi-experiments (subjects in treatment and control groups were matched on pretest or other covariates) and weak experiments (neither randomized nor matched but had the control group). They had an average sample size below 70 for different domains and outcomes. These small sample sizes imply a strong (and perhaps erroneous) assumption about the minimum relevant effect size (MRES) of intervention before an experiment is conducted; that is, a standardized intervention effect of Cohen’s d < 0.50 is not relevant to education policy or practice. Thus, an introduction to sample size determination for pretest-posttest simple experimental designs is warranted. This study describes nuts and bolts of sample size determination, derives expressions for optimal design under differential cost per treatment and control units, provide convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50, and describe the relevant software along with illustrations.

Optimal design with variable cost and precision requirements

1989 ◽  
Vol 19 (12) ◽  
pp. 1591-1597
Author(s):  
Margaret Penner
Keyword(s):  
Biological Sciences ◽  
Optimal Design ◽  
Sample Size ◽  
Design Theory ◽  
Sampling Design ◽  
Unit Cost ◽  
Evaluation Process ◽  
Variable Cost ◽  
Design Flexibility ◽  
The Cost

A method for incorporating variable costs and differing precision requirements into optimal design theory is developed and discussed. In many studies and experiments, particularly in the biological sciences, the cost of each observation can vary considerably depending on the attributes of the sample. Ignoring observation costs leads to designs that maximize precision for a given sample size. However, by incorporating costs, efficiency is maximized by optimizing precision per unit cost. An example is presented that demonstrates the efficiency of a weighted optimal design in comparison with several alternatives. The weighted optimal design is most efficient at meeting the experimenter's precision objectives. Comparing designs allows the introduction of additional criteria such as design flexibility into the evaluation process. Explicitly incorporating both cost and precision in the search for a sampling design ensures time is wisely spent considering study objectives, including precision requirements.

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.

A Modification of Simon's Optimal Design for Phase II Trials When the Criterion Is Median Sample Size

1999 ◽  
Vol 20 (6) ◽  
pp. 555-566 ◽  
Author(s):  
John J. Hanfelt ◽  
Rebecca S. Slack ◽  
Edmund A. Gehan
Keyword(s):  
Optimal Design ◽  
Sample Size ◽  
Phase Ii ◽  
Phase Ii Trials

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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