Sample Size Considerations for Multiple Comparison Procedures in ANOVA

Brooks, Gordon P.; Johanson, George A.

doi:10.22237/jmasm/1304222940

Cited by 41 publications

(33 citation statements)

References 20 publications

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“…57 Therefore, for a multi-arm study or a study with repeated measures, it would be useful to conduct a pilot study to provide sufficient details about the pattern of mean, mean difference, and the variances of response variable at each time point. 58 Inadequate description of sample size calculation continues to be reported 7-11 despite the recommendations of the CON-SORT 2 and the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 59 groups. Adherence to these recommendations has been reported to be suboptimal in anaesthesia, 60 critical care, 61 and other medical journals.…”

Section: Discussionmentioning

confidence: 99%

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Abdulatif

Mukhtar

Obayah

2015

British Journal of Anaesthesia

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We have evaluated the pitfalls in reporting sample size calculation in randomized controlled trials (RCTs) published in the 10 highest impact factor anaesthesia journals.Superiority RCTs published in 2013 were identified and checked for the basic components required for sample size calculation and replication. The difference between the reported and replicated sample size was estimated. The sources used for estimating the expected effect size (Δ) were identified, and the difference between the expected and observed effect sizes (Δ gap) was estimated.We enrolled 194 RCTs. Sample size calculation was reported in 91.7% of studies. Replication of sample size calculation was possible in 80.3% of studies. The original and replicated sample sizes were identical in 67.8% of studies. The difference between the replicated and reported sample sizes exceeded 10% in 28.7% of studies. The expected and observed effect sizes were comparable in RCTs with positive outcomes (P=0.1). Studies with negative outcome tended to overestimate the effect size (Δ gap 42%, 95% confidence interval 32-51%), P<0.001. Post hoc power of negative studies was 20.2% (95% confidence interval 13.4-27.1%). Studies using data derived from pilot studies for sample size calculation were associated with the smallest Δ gaps (P=0.008).Sample size calculation is frequently reported in anaesthesia journals, but the details of basic elements for calculation are not consistently provided. In almost one-third of RCTs, the reported and replicated sample sizes were not identical and the assumptions for the expected effect size and variance were not supported by relevant literature or pilot studies.

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Section: Discussionmentioning

confidence: 99%

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Abdulatif

Mukhtar

Obayah

2015

British Journal of Anaesthesia

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“…However, there is a continuing debate on whether population-based or subject-based AIF performance is more robust. Fourth, multiple comparisons using Bonferroni correction were not performed as the study was underpowered to do so (n = 37) [ 53 ]. That being said, this study can be taken as a starting point for future research with larger sample sizes to further validate our findings.…”

Section: Discussionmentioning

confidence: 99%

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Ochoa‐Albiztegui

Sevilimedu

Horvat

et al. 2020

Cancers

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The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.

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“…Typically, effect sizes are calculated as a statistical measure between two classes and the number of classes in this experiment exceeded this limitation. Previous studies [3,20] suggest that to overcome this problem, it is necessary to transfer the statistical inference by testing between the two outermost classes, i.e. 'Normal' (C1) and 'Pathologic' (C3).…”

Section: Experimental Set-upmentioning

confidence: 99%

A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets

Saul

Rostami

2018

Advances in Intelligent Systems and Computing

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Class imbalance is a common challenge when dealing with pattern classification of real-world medical data-sets. An effective countermeasure typically used is a method known as re-sampling. In this paper we implement an ANN with different re-sampling techniques to subsequently compare and evaluate the performances. Re-sampling strategies included a control, under-sampling, over-sampling, and a combination of the two. We found that over-sampling and the combination of under-and over-sampling both led to a significantly superior classifier performance compared to under-sampling only in correctly predicting labelled classes.

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Sample Size Considerations for Multiple Comparison Procedures in ANOVA

Cited by 41 publications

References 20 publications

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets

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