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.

Quality of Split-Mouth Trials in Dentistry: 1998, 2008, and 2018

2020 ◽  
Vol 99 (13) ◽  
pp. 1453-1460
Author(s):  
D. Qin ◽  
F. Hua ◽  
H. He ◽  
S. Liang ◽  
H. Worthington ◽  
...  
Keyword(s):  
Sample Size ◽  
Methodological Quality ◽  
Linear Models ◽  
Sample Size Calculation ◽  
Reporting Quality ◽  
Treatment Groups ◽  
Item Checklist ◽  
The Mean ◽  
Over Time

The objectives of this study were to assess the reporting quality and methodological quality of split-mouth trials (SMTs) published during the past 2 decades and to determine whether there has been an improvement in their quality over time. We searched the MEDLINE database via PubMed to identify SMTs published in 1998, 2008, and 2018. For each included SMT, we used the CONsolidated Standards Of Reporting Trials (CONSORT) 2010 guideline, CONSORT for within-person trial (WPT) extension, and a new 3-item checklist to assess its trial reporting quality (TRQ), WPT-specific reporting quality (WRQ), and SMT-specific methodological quality (SMQ), respectively. Multivariable generalized linear models were performed to analyze the quality of SMTs over time, adjusting for potential confounding factors. A total of 119 SMTs were included. The mean overall score for the TRQ (score range, 0 to 32), WRQ (0 to 15), and SMQ (0 to 3) was 15.77 (SD 4.51), 6.06 (2.06), and 1.12 (0.70), respectively. The primary outcome was clearly defined in only 28 SMTs (23.5%), and only 27 (22.7%) presented a replicable sample size calculation. Only 45 SMTs (37.8%) provided the rationale for using a split-mouth design. The correlation between body sites was reported in only 5 studies (4.2%) for sample size calculation and 4 studies (3.4%) for statistical results. Only 2 studies (1.7%) performed an appropriate sample size calculation, and 46 (38.7%) chose appropriate statistical methods, both accounting for the correlation among treatment groups and the clustering/multiplicity of measurements within an individual. Results of regression analyses suggested that the TRQ of SMTs improved significantly with time ( P < 0.001), while there was no evidence of improvement in WRQ or SMQ. Both the reporting quality and methodological quality of SMTs still have much room for improvement. Concerted efforts are needed to improve the execution and reporting of SMTs.

scholarly journals Control of the False Discovery Proportion for Independently Tested Null Hypotheses

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Yongchao Ge ◽  
Xiaochun Li
Keyword(s):  
Multiple Testing ◽  
Asymptotic Theory ◽  
Random Variable ◽  
Convergence Problem ◽  
Finite Samples ◽  
False Discovery ◽  
Classical Procedure ◽  
Multiple Testing Problem ◽  
Upper Confidence Bound ◽  
False Discovery Proportion

Consider the multiple testing problem of testingmnull hypothesesH1,…,Hm, among whichm0hypotheses are truly null. Given theP-values for each hypothesis, the question of interest is how to combine theP-values to find out which hypotheses are false nulls and possibly to make a statistical inference onm0. Benjamini and Hochberg proposed a classical procedure that can control the false discovery rate (FDR). The FDR control is a little bit unsatisfactory in that it only concerns the expectation of the false discovery proportion (FDP). The control of the actual random variable FDP has recently drawn much attention. For any level1−α, this paper proposes a procedure to construct an upper prediction bound (UPB) for the FDP for a fixed rejection region. When1−α=50%, our procedure is very close to the classical Benjamini and Hochberg procedure. Simultaneous UPBs for all rejection regions' FDPs and the upper confidence bound for the unknownm0are presented consequently. This new proposed procedure works for finite samples and hence avoids the slow convergence problem of the asymptotic theory.

scholarly journals Intraoral Maxillary Molar Distalization

2006 ◽  
Vol 76 (6) ◽  
pp. 923-929 ◽  
Author(s):  
Ingela Karlsson ◽  
Lars Bondemark
Keyword(s):  
Sample Size ◽  
Orthodontic Treatment ◽  
Treatment Time ◽  
Sample Size Calculation ◽  
Movement Rate ◽  
Molar Distalization ◽  
Maxillary Molars ◽  
Maxillary Molar ◽  
The Mean ◽  
Anchorage Loss

Abstract Objective: To evaluate the maxillary molar distalization and anchorage loss in two groups, one before (MD 1 group) and one after (MD 2 group) eruption of second maxillary molars. Materials and Methods: After a sample size calculation, 20 patients were recruited for each group from patients who fulfilled the following criteria: no orthodontic treatment before distal molar movement, Class II molar relationship defined by at least end-to-end molar relationship, space deficiency in the maxilla, and use of an intra-arch NiTi coil appliance with a Nance appliance to provide anchorage. Patients in the MD 1 group were without any erupted second molars during the distalization period, whereas in the MD 2 group both the first and second molars were in occlusion at start of treatment. The main outcome measures to be assessed were: treatment time, ie, time in months to achieve a normal molar relation, distal movement of maxillary first molars, and anterior movement of maxillary incisors (anchorage loss). The mean age in the MD 1 group was 11.4 years; in the MD 2 group, 14.6 years. Results: The amount of distal movement of the first molars was significantly greater (P &lt; .01) and the anchorage loss was significantly lower (P &lt; .01) in the group with no second molars erupted. The molar distalization time was also significantly shorter (P &lt; .001) in this group, and thus the movement rate was two times higher. Conclusions: It is more effective to distalize the first maxillary molars before the second molars have erupted.

scholarly journals Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Dongmei Li ◽  
Timothy D. Dye
Keyword(s):  
Data Analysis ◽  
Sample Size ◽  
High Throughput ◽  
Rate Control ◽  
Multiple Testing ◽  
Testing Procedures ◽  
High Throughput Data ◽  
False Discovery ◽  
Multiple Testing Procedures ◽  
High Throughput Data Analysis

Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations and gene oncology examples. The bootstrap single-step minPprocedure and the bootstrap step-down minPprocedure perform the best among all tested procedures, when sample size is as small as 3 in each group and either familywise error rate or false discovery rate control is desired. When sample size increases to 12 and false discovery rate control is desired, the permutation maxTprocedure and the permutation minPprocedure perform best. Our results provide guidance for high-throughput data analysis when sample size is small.

Uncertainty of Design Parameters From Viewpoint of Extreme Statistics

1992 ◽  
Vol 114 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Y. Goda
Keyword(s):  
Distribution Function ◽  
Standard Deviation ◽  
Sample Size ◽  
Design Parameters ◽  
Empirical Formulas ◽  
Extreme Statistics ◽  
Normal Distributions ◽  
The Mean ◽  
Log Normal ◽  
Return Value

Statistical variability of a sample of extreme data has been examined by means of Monte Carlo simulations. The Fisher-Tippett types I and II, the Weibull, and the log-normal distributions were chosen for the examination. The sample size covered from 10 to 400, and 10,000 runs were carried out for each combination of sample size, censoring rate, and distribution function. Empirical formulas and tables are presented for the mean and the coefficient of variation of the standard deviation of a sample, the standard deviation of return value, and the confidence interval of return period.

scholarly journals Adjusted Sample Size Calculation for RNA-seq Data in the Presence of Confounding Covariates

2021 ◽  
Vol 1 (2) ◽  
pp. 47-63
Author(s):  
Xiaohong Li ◽  
Shesh N. Rai ◽  
Eric C. Rouchka ◽  
Timothy E. O’Toole ◽  
Nigel G. F. Cooper
Keyword(s):  
Sample Size ◽  
Differential Expression ◽  
Expression Analysis ◽  
Negative Binomial ◽  
Differential Expression Analysis ◽  
Sample Size Calculation ◽  
Size Estimation ◽  
Rna Seq ◽  
Simulation Based ◽  
The Relationship

Sample size calculation for adequate power analysis is critical in optimizing RNA-seq experimental design. However, the complexity increases for directly estimating sample size when taking into consideration confounding covariates. Although a number of approaches for sample size calculation have been proposed for RNA-seq data, most ignore any potential heterogeneity. In this study, we implemented a simulation-based and confounder-adjusted method to provide sample size recommendations for RNA-seq differential expression analysis. The data was generated using Monte Carlo simulation, given an underlined distribution of confounding covariates and parameters for a negative binomial distribution. The relationship between the sample size with the power and parameters, such as dispersion, fold change and mean read counts, can be visualized. We demonstrate that the adjusted sample size for a desired power and type one error rate of α is usually larger when taking confounding covariates into account. More importantly, our simulation study reveals that sample size may be underestimated by existing methods if a confounding covariate exists in RNA-seq data. Consequently, this underestimate could affect the detection power for the differential expression analysis. Therefore, we introduce confounding covariates for sample size estimation for heterogeneous RNA-seq data.

scholarly journals Sample Size Calculation in Medical Research: A Primer

2021 ◽  
Author(s):  
Jaykaran Charan ◽  
Rimplejeet Kaur ◽  
Pankaj Bhardwaj ◽  
Kuldeep Singh ◽  
Sneha R. Ambwani ◽  
...  
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Research Question ◽  
Sample Size Calculation ◽  
Small Sample ◽  
Large Sample Size ◽  
Error Type ◽  
Type 1 Error ◽  
Size Type ◽  
Study Designs

AbstractQuality of research is determined by many factors and one such climacteric factor is sample size. Inability to use correct sample size in study might lead to fallacious results in the form of rejection of true findings or approval of false results. Too large sample size is wastage of resources and use of too small sample size might fail to answer the research question or provide imprecise results and may question the validity of study. Despite being such a paramount aspect of research, the knowledge about sample size calculation is sparse among researchers. Why is it important to calculate sample size; when to calculate it; how to calculate it and what details about sample size calculation should be reported in research protocols or articles; are the lesser known basics to majority of researchers. The present review is directed to address these aforementioned fundamentals about sample size. Sample size should be calculated during the initial phase of planning of study. Several components are required for sample size calculation such as effect size, type-1 error, type-2 error, and variance. Researchers must be aware that there are different formulas for calculating sample size for different types of study designs. The researcher must include details about sample size calculation in the methodology section, so that it can be justified and it also adds to the transparency of the study. The literature about calculation of sample size for different study designs is scattered over many textbooks and journals. Scrupulous literature search was conducted to find the passable information for this review. This paper presents the sample size calculation formulas in a single review in a simplified manner with relevant examples, so that researchers may adequately use them in their research.

scholarly journals Smoothing Corrections for Improving Sample Size Recalculation Rules in Adaptive Group Sequential Study Designs

2021 ◽  
Author(s):  
Carolin Herrmann ◽  
Geraldine Rauch
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Evaluation Criteria ◽  
High Variability ◽  
Conditional Power ◽  
Performance Score ◽  
Group Sequential ◽  
Conditional Performance ◽  
Sequential Study ◽  
Study Designs

Abstract Background An adequate sample size calculation is essential for designing a successful clinical trial. One way to tackle planning difficulties regarding parameter assumptions required for sample size calculation is to adapt the sample size during the ongoing trial.This can be attained by adaptive group sequential study designs. At a predefined timepoint, the interim effect is tested for significance. Based on the interim test result, the trial is either stopped or continued with the possibility of a sample size recalculation. Objectives Sample size recalculation rules have different limitations in application like a high variability of the recalculated sample size. Hence, the goal is to provide a tool to counteract this performance limitation. Methods Sample size recalculation rules can be interpreted as functions of the observed interim effect. Often, a “jump” from the first stage's sample size to the maximal sample size at a rather arbitrarily chosen interim effect size is implemented and the curve decreases monotonically afterwards. This jump is one reason for a high variability of the sample size. In this work, we investigate how the shape of the recalculation function can be improved by implementing a smoother increase of the sample size. The design options are evaluated by means of Monte Carlo simulations. Evaluation criteria are univariate performance measures such as the conditional power and sample size as well as a conditional performance score which combines these components. Results We demonstrate that smoothing corrections can reduce variability in conditional power and sample size as well as they increase the performance with respect to a recently published conditional performance score for medium and large standardized effect sizes. Conclusion Based on the simulation study, we present a tool that is easily implemented to improve sample size recalculation rules. The approach can be combined with existing sample size recalculation rules described in the literature.

scholarly journals Knee Fix or Replace Trial (KFORT): a randomized controlled feasibility study

2019 ◽  
Vol 101-B (11) ◽  
pp. 1408-1415 ◽  
Author(s):  
Peter D. Hull ◽  
Daud T. S. Chou ◽  
Sophie Lewis ◽  
Andrew D. Carrothers ◽  
Joseph M. Queally ◽  
...  
Keyword(s):  
Sample Size ◽  
Controlled Trial ◽  
Sample Size Calculation ◽  
Femoral Fractures ◽  
Full Scale ◽  
Willingness To Participate ◽  
Randomized Controlled ◽  
The Mean ◽  
Mean Cost

Aims The aim of this study was to assess the feasibility of conducting a full-scale, appropriately powered, randomized controlled trial (RCT) comparing internal fracture fixation and distal femoral replacement (DFR) for distal femoral fractures in older patients. Patients and Methods Seven centres recruited patients into the study. Patients were eligible if they were greater than 65 years of age with a distal femoral fracture, and if the surgeon felt that they were suitable for either form of treatment. Outcome measures included the patients’ willingness to participate, clinicians’ willingness to recruit, rates of loss to follow-up, the ability to capture data, estimates of standard deviation to inform the sample size calculation, and the main determinants of cost. The primary clinical outcome measure was the EuroQol five-dimensional index (EQ-5D) at six months following injury. Results Of 36 patients who met the inclusion criteria, five declined to participate and eight were not recruited, leaving 23 patients to be randomized. One patient withdrew before surgery. Of the remaining patients, five (23%) withdrew during the follow-up period and six (26%) died. A 100% response rate was achieved for the EQ-5D at each follow-up point, excluding one missing datapoint at baseline. In the DFR group, the mean cost of the implant outweighed the mean cost of many other items, including theatre time, length of stay, and readmissions. For a powered RCT, a total sample size of 1400 would be required with 234 centres recruiting over three years. At six months, the EQ-5D utility index was lower in the DFR group. Conclusion This study found that running a full-scale trial in this country would not be feasible. However, it may be feasible to undertake an international multicentre trial, and our findings provide some guidance about the power of such a study, the numbers required, and some challenges that should be anticipated and addressed. Cite this article: Bone Joint J 2019;101-B:1408–1415.

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