scholarly journals An Improved Sample Size Calculation Method for Score Tests in Generalized Linear Models

Author(s):  
Yongqiang Tang ◽  
Liang Zhu ◽  
Jiezhun Gu
2000 ◽  
Vol 28 (3) ◽  
pp. 563-570 ◽  
Author(s):  
Dianliang Deng ◽  
Sudhir R. Paul

2020 ◽  
Vol 99 (13) ◽  
pp. 1453-1460
Author(s):  
D. Qin ◽  
F. Hua ◽  
H. He ◽  
S. Liang ◽  
H. Worthington ◽  
...  

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.


2003 ◽  
Vol 57 (4) ◽  
pp. 391-409 ◽  
Author(s):  
Gauss M. Cordeiro ◽  
Denise A. Botter ◽  
Lucia P. Barroso ◽  
Silvia L. P. Ferrari

Author(s):  
Chung-I Li ◽  
Yu Shyr

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.


Biometrics ◽  
1988 ◽  
Vol 44 (1) ◽  
pp. 79 ◽  
Author(s):  
Steven G. Self ◽  
Robert H. Mauritsen

2014 ◽  
Vol 61 (Code Snippet 2) ◽  
Author(s):  
Antonio Hermes M. da Silva-Júnior ◽  
Damião Nóbrega da Silva ◽  
Silvia L. P. Ferrari

Author(s):  
Gauss M. Cordeiro ◽  
Silvia L. de Paula Ferrari ◽  
Gilberto A. Paula

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