scholarly journals Sample size calculations for controlling the distribution of false discovery proportion in microarray experiments

Biostatistics ◽  
2009 ◽  
Vol 10 (4) ◽  
pp. 694-705 ◽  
Author(s):  
T. Oura ◽  
S. Matsui ◽  
K. Kawakami
Keyword(s):  
Sample Size ◽  
Microarray Experiments ◽  
False Discovery ◽  
Sample Size Calculations ◽  
False Discovery Proportion

Microarray experimental design: power and sample size considerations

2003 ◽  
Vol 16 (1) ◽  
pp. 24-28 ◽  
Author(s):  
M. C. K. Yang ◽  
J. J. Yang ◽  
R. A. McIndoe ◽  
J. X. She
Keyword(s):  
Sample Size ◽  
Gene Expression Analysis ◽  
Microarray Technology ◽  
Microarray Experiments ◽  
False Discovery ◽  
Powerful Approach ◽  
Design Power ◽  
Single Time Point ◽  
Time Point

Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.

scholarly journals Sample size reassessment for a two-stage design controlling the false discovery rate

2015 ◽  
Vol 14 (5) ◽  
Author(s):  
Sonja Zehetmayer ◽  
Alexandra C. Graf ◽  
Martin Posch
Keyword(s):  
Sample Size ◽  
False Discovery Rate ◽  
Effect Sizes ◽  
Rna Seq ◽  
Two Stage ◽  
Stage Design ◽  
False Discovery ◽  
Sample Size Calculations ◽  
Two Stage Design ◽  
High Uncertainty

AbstractSample size calculations for gene expression microarray and NGS-RNA-Seq experiments are challenging because the overall power depends on unknown quantities as the proportion of true null hypotheses and the distribution of the effect sizes under the alternative. We propose a two-stage design with an adaptive interim analysis where these quantities are estimated from the interim data. The second stage sample size is chosen based on these estimates to achieve a specific overall power. The proposed procedure controls the power in all considered scenarios except for very low first stage sample sizes. The false discovery rate (FDR) is controlled despite of the data dependent choice of sample size. The two-stage design can be a useful tool to determine the sample size of high-dimensional studies if in the planning phase there is high uncertainty regarding the expected effect sizes and variability.

scholarly journals Practical FDR-based sample size calculations in microarray experiments

2005 ◽  
Vol 21 (15) ◽  
pp. 3264-3272 ◽  
Author(s):  
J. Hu ◽  
F. Zou ◽  
F. A. Wright
Keyword(s):  
Sample Size ◽  
Microarray Experiments ◽  
Sample Size Calculations

scholarly journals Sample Size Calculation for Controlling False Discovery Proportion

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Shulian Shang ◽  
Qianhe Zhou ◽  
Mengling Liu ◽  
Yongzhao Shao
Keyword(s):  
Sample Size ◽  
Multiple Testing ◽  
Sample Size Calculation ◽  
Design Parameters ◽  
Cancer Dataset ◽  
False Discovery ◽  
The Mean ◽  
Study Designs ◽  
The Relationship ◽  
False Discovery Proportion

The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under weak-dependence assumptions to achieve the desired overall power. The relationship between design parameters and sample size is explored. The adequacy of the procedure is assessed by simulation. We illustrate the method using estimated correlations from a prostate cancer dataset.

Sample Size Calculations Based on Ranking and Selection in Microarray Experiments

Biometrics ◽  
2007 ◽  
Vol 64 (1) ◽  
pp. 217-226 ◽  
Author(s):  
Shigeyuki Matsui ◽  
Shu Zeng ◽  
Takeharu Yamanaka ◽  
John Shaughnessy
Keyword(s):  
Sample Size ◽  
Ranking And Selection ◽  
Microarray Experiments ◽  
Sample Size Calculations

scholarly journals Virtual reality-based relaxation for enhancement of perioperative well-being and quality of life: protocol for a randomised pilot trial

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e044193
Author(s):  
Matthias Christian Schrempf ◽  
Julian Quirin Petzold ◽  
Hugo Vachon ◽  
Morten Aagaard Petersen ◽  
Johanna Gutschon ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Virtual Reality ◽  
Sample Size ◽  
Stress Reduction ◽  
Pilot Trial ◽  
Well Being ◽  
Coping Resources ◽  
Patients With Cancer ◽  
Sample Size Calculations

IntroductionPatients with cancer undergoing surgery often suffer from reduced quality of life and various forms of distress. Untreated distress can negatively affect coping resources as well as surgical and oncological outcomes. A virtual reality-based stress reduction intervention may increase quality of life and well-being and reduce distress in the perioperative phase for patients with cancer. This pilot trial aims to explore the feasibility of the proposed intervention, assess patient acceptability and obtain estimates of effect to provide data for sample size calculations.Methods and analysisPatients with colorectal cancer and liver metastasis undergoing elective surgery will be recruited for this single-centre, randomised pilot trial with a three-arm design. A total of 54 participants will be randomised at 1:1:1 ratio to one of two intervention groups or a control receiving standard treatment. Those randomised to an intervention group will either receive perioperative virtual reality-based stress reduction exercises twice daily or listen to classical music twice daily. Primary feasibility outcomes are number and proportions of participants recruited, screened, consented and randomised. Furthermore, adherence to the intervention, compliance with the completion of the quality of life questionnaires and feasibility of implementing the trial procedures will be assessed. Secondary clinical outcomes are measurements of the effectiveness of the interventions to inform sample size calculations.Ethics and disseminationThe study protocol, the patient information and the informed consent form have been approved by the ethics committee of the Ludwigs-Maximilians-University, Munich, Germany (Reference Number: 19–915). Study findings will be submitted for publication in peer-reviewed journals.Trial registration numberDRKS00020909.

scholarly journals Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials

2021 ◽  
pp. 174077452110208
Author(s):  
Elizabeth Korevaar ◽  
Jessica Kasza ◽  
Monica Taljaard ◽  
Karla Hemming ◽  
Terry Haines ◽  
...  
Keyword(s):  
Sample Size ◽  
Discrete Time ◽  
Correlation Coefficients ◽  
Time Decay ◽  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised ◽  
Sample Size Calculations ◽  
Correlation Structures ◽  
Cluster Correlation

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

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