The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional

Fagerland, Morten Wang; Lydersen, Stian; Laake, Petter

doi:10.1186/1471-2288-13-91

Cited by 285 publications

(227 citation statements)

References 16 publications

(22 reference statements)

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“…The test creates 2 × 2 contingency tables in order to compute scores. This test has a published past of usage by medical research community [30] and has recently been used for performance comparison in computer vision for the first time by Clark [7] and later for machine learning by Bostanci [27]. The test is significantly robust against Type-I error, i.e.…”

Section: Discussionmentioning

confidence: 99%

Resilient Image Feature Description through Evolution

Bostancı

2017

ijisae

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Feature description is an important stage in many different vision algorithms. Image features detected by various detectors can be described using descriptors either with a binary or floating-point structure. This study presents the use of evolutionary algorithms, namely Genetic Algorithms (GA), in order to improve the robustness of the feature descriptors against increasing levels of photographic distortions such as noise or JPEG compression. Original feature descriptors were evolved in order to reduce the descriptor distance for the mentioned test cases. Results, tested using a statistical framework, suggest that the evolved descriptors offer better matching performance for two state-of-the-art descriptors.

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Section: Discussionmentioning

confidence: 99%

Resilient Image Feature Description through Evolution

Bostancı

2017

ijisae

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“…To analyze the difference between presence and desirability of incentives, the McNemar test for binary matched-pairs data was used (Fagerland et al 2013).…”

Section: Statistical Analysesmentioning

confidence: 99%

Incentives for knowledge valorisation: a European benchmark

2017

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Studies on incentives to stimulate researcher engagement in knowledge valorisation have primarily focused on incentives for economic output and it remains unclear how universities configure incentives for a broad societal impact of knowledge. Therefore, this article explores the presence and design of incentives for the full range of knowledge valorisation activities by employing a bottom-up, mixed-methods design. In 17 semistructured interviews with representatives from highly ranked European universities, 11 distinct incentives for valorisation activities with an academic, civil society, entrepreneurial or state-governmental orientation were identified. Subsequently, a quantifying survey was conducted (n = 48). Perceived effectiveness did not directly correspond to presence and desirability, indicating that additional motives play a role in implementing incentives, including managing conflict of interest situations. For non-scalable (hour-based) activities broader bandwidths of allocated percentages were employed that were more dependent on case by case attribution of income and for which more conditions to limit conflicts of interest were in place. Interestingly, this study found a negative correlation between the number of such restricting conditions being in place for consultancy and the university's overall success in industry income. A flowchart is presented that university management may use to align the selection and design of their incentives with their motives.

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“…The pattern Q5 is for samples with missing data that occur only at the end of a study. Suissa and Shuster (1991) were the first to compute exact sample sizes for a matched-pairs study with complete data (Q1) by using T MC as the test statistic for sample space ordering (Lloyd, 2008b; Fagerland, Lydersen, & Laake, 2013; Shan & Wilding, 2014). …”

Section: Numerical Studymentioning

confidence: 99%

Sample size determination for a matched‐pairs study with incomplete data using exact approach

Shan

Bernick

Banks

2017

Brit J Math & Statis

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This research was motivated by a clinical trial design for a cognitive study. The pilot study was a matched-pairs design where some data are missing, specifically the missing data coming at the end of the study. Existing approaches to determine sample size are all based on asymptotic approaches (e.g., the GEE approach). When the sample size in a clinical trial is small to medium, these asymptotic approaches may not be appropriate for use due to the unsatisfactory type I and II error rates. For this reason, we consider the exact unconditional approach to compute the sample size for a matched-pairs study with incomplete data. Recommendations are made for each possible missing pattern by comparing the exact sample sizes based on three commonly used test statistics, with the existing sample size calculation based on the GEE approach. An example from a real surgeon-reviewers study is used to illustrate the application of the exact sample size calculation in study designs.

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The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional

Cited by 285 publications

References 16 publications

Resilient Image Feature Description through Evolution

Resilient Image Feature Description through Evolution

Incentives for knowledge valorisation: a European benchmark

Sample size determination for a matched‐pairs study with incomplete data using exact approach

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