scholarly journals Sample size determination and power analysis using the G*Power software

2021 ◽  
Vol 18 ◽  
pp. 17
Author(s):  
Hyun Kang
Keyword(s):  
Sample Size ◽  
Statistical Methods ◽  
Power Analysis ◽  
Alternative Hypothesis ◽  
Statistical Tests ◽  
Sample Size Calculation ◽  
Sample Size Determination ◽  
Power Calculation ◽  
Type I ◽  
Exact Tests

Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “Calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.

scholarly journals RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment

2014 ◽  
Vol 13s6 ◽  
pp. CIN.S17688 ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Chung-I Li ◽  
Quanhu Sheng ◽  
Yu Shyr
Keyword(s):  
Experimental Design ◽  
Sample Size ◽  
High Throughput ◽  
Power Analysis ◽  
High Throughput Sequencing ◽  
Genome Wide Association Study ◽  
Negative Binomial ◽  
Sample Size Calculation ◽  
Power Calculation ◽  
Biological Studies

Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/ .

Sample size calculation for agreement between two raters with binary endpoints using exact tests

2016 ◽  
Vol 27 (7) ◽  
pp. 2132-2141 ◽  
Author(s):  
Guogen Shan
Keyword(s):  
Sample Size ◽  
Error Control ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Sample Size Determination ◽  
Type I ◽  
Test Statistic ◽  
Testing Procedures ◽  
Alternative Hypotheses ◽  
The One

In an agreement test between two raters with binary endpoints, existing methods for sample size calculation are always based on asymptotic approaches that use limiting distributions of a test statistic under null and alternative hypotheses. These calculated sample sizes may be not reliable due to the unsatisfactory type I error control of asymptotic approaches. We propose a new sample size calculation based on exact approaches which control for the type I error rate. The two exact approaches are considered: one approach based on maximization and the other based on estimation and maximization. We found that the latter approach is generally more powerful than the one based on maximization. Therefore, we present the sample size calculation based on estimation and maximization. A real example from a clinical trial to diagnose low back pain of patients is used to illustrate the two exact testing procedures and sample size determination.

scholarly journals On sample size calculation in testing treatment efficacy in clinical trials

2021 ◽  
Vol 58 (2) ◽  
pp. 133-147
Author(s):  
Rownak Jahan Tamanna ◽  
M. Iftakhar Alam ◽  
Ahmed Hossain ◽  
Md Hasinur Rahaman Khan
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Statistical Significance ◽  
Sample Size Calculation ◽  
Clinical Trial Design ◽  
Sample Size Determination ◽  
Cluster Sampling ◽  
Effective Sample Size ◽  
Type I

Summary Sample size calculation is an integral part of any clinical trial design, and determining the optimal sample size for a study ensures adequate power to detect statistical significance. It is a critical step in designing a planned research protocol, since using too many participants in a study is expensive, exposing more subjects to the procedure. If a study is underpowered, it will be statistically inconclusive and may cause the whole protocol to fail. Amidst the attempt to maximize power and the underlying effort to minimize the budget, the optimization of both has become a significant issue in the determination of sample size for clinical trials in recent decades. Although it is hard to generalize a single method for sample size calculation, this study is an attempt to offer something that might be a basis for finding a permanent answer to the contradictions of sample size determination, by the use of simulation studies under simple random and cluster sampling schemes, with different sizes of power and type I error. The effective sample size is much higher when the design effect of the sampling method is smaller, particularly less than 1. Sample size increases for cluster sampling when the number of clusters increases.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

scholarly journals Required sample size for comparing two independent means

2020 ◽  
Vol 6 (2) ◽  
pp. 106-113
Author(s):  
A. M. Grjibovski ◽  
M. A. Gorbatova ◽  
A. N. Narkevich ◽  
K. A. Vinogradov
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Biomedical Literature ◽  
Type I ◽  
Research Practice ◽  
False Null Hypothesis ◽  
Different Levels ◽  
Russian Research

Sample size calculation in a planning phase is still uncommon in Russian research practice. This situation threatens validity of the conclusions and may introduce Type I error when the false null hypothesis is accepted due to lack of statistical power to detect the existing difference between the means. Comparing two means using unpaired Students’ ttests is the most common statistical procedure in the Russian biomedical literature. However, calculations of the minimal required sample size or retrospective calculation of the statistical power were observed only in very few publications. In this paper we demonstrate how to calculate required sample size for comparing means in unpaired samples using WinPepi and Stata software. In addition, we produced tables for minimal required sample size for studies when two means have to be compared and body mass index and blood pressure are the variables of interest. The tables were constructed for unpaired samples for different levels of statistical power and standard deviations obtained from the literature.

P062 A SYSTEMATIC REVIEW ON POWER ESTIMATION TO SUPPORT SAMPLE SIZE CALCULATION FOR RANDOMISED TRIALS IN INFLAMMATORY BOWEL DISEASE

2020 ◽  
Vol 26 (Supplement_1) ◽  
pp. S9-S9
Author(s):  
Svetlana Lakunina ◽  
Zipporah Iheozor-Ejiofor ◽  
Morris Gordon ◽  
Daniel Akintelure ◽  
Vassiliki Sinopoulou
Keyword(s):  
Inflammatory Bowel Disease ◽  
Sample Size ◽  
Bowel Disease ◽  
Sample Size Calculation ◽  
Sample Size Estimation ◽  
Power Calculation ◽  
Size Estimation ◽  
Target Sample ◽  
Inflammatory Bowel ◽  
Randomised Controlled

Abstract Inflammatory bowel disease is a collection of disorders of the gastrointestinal tract, characterised by relapsing and remitting inflammation. Studies have reported several pharmacological or non-pharmacological interventions being effective in the management of the disease. Sample size estimation with power calculation is necessary for a trial to detect the effect of an intervention. This project critically evaluates the sample size estimation and power calculation reported by randomised controlled studies of inflammatory bowel disease management to effectively conclude appropriateness of the studies results. We conducted a literature search in the Cochrane database to identify systematic literature reviews. Their reference lists were screened, and studies were selected if they met the inclusion criteria. The data was extracted based on power calculation parameters and outcomes, results were analysed and summarised in percentages, means and graphs. We screened almost all trials about the management of inflammatory bowel disease published in the past 25 years. 232 studies were analysed, of which 167 reported power calculation. Less than half (48%) of these studies achieved their target sample size, needed for them to accurately conclude that the interventions were effective. Moreover, the average minimal difference those studies were aimed to detect was 30%, which could be not enough to prove the effect of an intervention. To conclude inaccurate power calculations and failure to achieve the target sample sizes can lead to errors in the results on how effective an intervention is in the management of inflammatory bowel disease.

Use of interval estimations in design and evaluation of multiregional clinical trials with continuous outcomes

2018 ◽  
Vol 28 (7) ◽  
pp. 2179-2195 ◽  
Author(s):  
Chieh Chiang ◽  
Chin-Fu Hsiao
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Interval Estimation ◽  
Error Rates ◽  
New Drugs ◽  
Sample Size Determination ◽  
Type I ◽  
Size Determination ◽  
Interval Estimators

Multiregional clinical trials have been accepted in recent years as a useful means of accelerating the development of new drugs and abridging their approval time. The statistical properties of multiregional clinical trials are being widely discussed. In practice, variance of a continuous response may be different from region to region, but it leads to the assessment of the efficacy response falling into a Behrens–Fisher problem—there is no exact testing or interval estimator for mean difference with unequal variances. As a solution, this study applies interval estimations of the efficacy response based on Howe’s, Cochran–Cox’s, and Satterthwaite’s approximations, which have been shown to have well-controlled type I error rates. However, the traditional sample size determination cannot be applied to the interval estimators. The sample size determination to achieve a desired power based on these interval estimators is then presented. Moreover, the consistency criteria suggested by the Japanese Ministry of Health, Labour and Welfare guidance to decide whether the overall results from the multiregional clinical trial obtained via the proposed interval estimation were also applied. A real example is used to illustrate the proposed method. The results of simulation studies indicate that the proposed method can correctly determine the required sample size and evaluate the assurance probability of the consistency criteria.

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