Authors' reply to comments on ‘Sample size calculation for comparing two negative binomial rates’

2014 ◽  
Vol 33 (16) ◽  
pp. 2880-2880
Author(s):  
Haiyuan Zhu ◽  
Hassan Lakkis
Keyword(s):  
Sample Size ◽  
Negative Binomial ◽  
Sample Size Calculation

Trends in annualized relapse rates in relapsing–remitting multiple sclerosis and consequences for clinical trial design

2011 ◽  
Vol 17 (10) ◽  
pp. 1211-1217 ◽  
Author(s):  
Richard Nicholas ◽  
Sebastian Straube ◽  
Heinz Schmidli ◽  
Simon Schneider ◽  
Tim Friede
Keyword(s):  
Multiple Sclerosis ◽  
Sample Size ◽  
Randomized Trials ◽  
Negative Binomial ◽  
Controlled Trial ◽  
Sample Size Calculation ◽  
Clinical Trial Design ◽  
Relapsing Remitting ◽  
Remitting Multiple Sclerosis ◽  
Relapsing Remitting Multiple Sclerosis

Background: Sample size calculation is a key aspect in the planning of any trial. Planning a randomized placebo-controlled trial in relapsing–remitting multiple sclerosis (RRMS) requires knowledge of the annualized relapse rate (ARR) in the placebo group. Objectives: This paper aims (i) to characterize the uncertainty in ARR by conducting a systematic review of placebo-controlled, randomized trials in RRMS and by modelling the ARR over time; and (ii) to assess the feasibility and utility of blinded sample size re-estimation (BSSR) procedures in RRMS. Methods: A systematic literature review was carried out by searching PubMed, Ovid Medline and the Cochrane Register of Controlled Trials. The placebo ARRs were modelled by negative binomial regression. Computer simulations were conducted to assess the utility of BSSR in RRMS. Results: Data from 26 placebo-controlled randomized trials were included in this analysis. The placebo ARR decreased by 6.2% per year ( p < 0.0001; 95% CI (4.2%; 8.1%)) resulting in substantial uncertainty in the planning of future trials. BSSR was shown to be feasible and to maintain power at a prespecified level also if the ARR was misspecified in the planning phase. Conclusions: Our investigations confirmed previously reported trends in ARR. In this context adaptive strategies such as BSSR designs are recommended for consideration in the planning of future trials in RRMS.

Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data

2016 ◽  
Vol 15 (6) ◽  
Author(s):  
Chung-I Li ◽  
Yu Shyr
Keyword(s):  
Sample Size ◽  
Linear Models ◽  
Negative Binomial ◽  
Differential Expression Analysis ◽  
Sample Size Calculation ◽  
Rna Seq ◽  
Optimal Sample Size ◽  
Optimal Sample ◽  
Gene Count ◽  
Downstream Analysis

AbstractAs RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study’s optimal sample size is now a vital step in experimental design. Current methods for calculating a study’s required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.

Sample size calculation for comparing two negative binomial rates

2013 ◽  
Vol 33 (3) ◽  
pp. 376-387 ◽  
Author(s):  
Haiyuan Zhu ◽  
Hassan Lakkis
Keyword(s):  
Sample Size ◽  
Negative Binomial ◽  
Sample Size Calculation

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/ .

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.

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