Analysis of clustered matched‐pair data for a non‐inferiority study design

Durkalski, Valerie; Palesch, Yuko Y.; Lipsitz, Stuart R.; Rust, Philip F.

doi:10.1002/sim.1385

Cited by 19 publications

(14 citation statements)

References 25 publications

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“…Data were trained and classified independently using leave-one-run-out cross validation. Statistics were calculated by comparing paired classifications of each image before and after priming in a one-tailed McNemar test, summed over all image pairs per subject; a final group McNemar statistic was formed using a correction for nonindependent clusters across subjects (43).…”

Section: Methodsmentioning

confidence: 99%

Imaging prior information in the brain

Gorlin

Meng

Sharma

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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In making sense of the visual world, the brain's processing is driven by two factors: the physical information provided by the eyes ("bottom-up" data) and the expectancies driven by past experience ("top-down" influences). We use degraded stimuli to tease apart the effects of bottom-up and top-down processes because they are easier to recognize with prior knowledge of undegraded images. Using machine learning algorithms, we quantify the amount of information that brain regions contain about stimuli as the subject learns the coherent images. Our results show that several distinct regions, including high-level visual areas and the retinotopic cortex, contain more information about degraded stimuli with prior knowledge. Critically, these regions are separate from those that exhibit classical priming, indicating that top-down influences are more than feature-based attention. Together, our results show how the neural processing of complex imagery is rapidly influenced by fleeting experiences.t what stage of visual processing does bottom-up information combine with top-down expectations to yield the eventual percept? This question lies at the heart of a mechanistic understanding of feed-forward/feed-back interactions, as they are implemented in the brain and as they might be instantiated by computational visual systems. Furthermore, this question is of central significance not only for vision, but also for all sensory modalities because the combination of current and prior data is ubiquitous as a processing principle.A compelling demonstration of the role of prior experience is obtained with images so degraded that they are initially perceived as devoid of meaning. However, after being shown their coherent versions, observers are readily able to parse the previously uninterpretable image. The well-known Dalmatian dog picture (1)-a black-and-white thresholded photograph-and the Mooney images (2) are classic examples of this phenomenon. Other examples of top-down knowledge facilitating sensory processing include phonemic restoration (3) and the interaction between depth perception and object recognition (4).The approach of comparing neural responses to degraded images before and after exposure to the fully coherent image has been used by several research groups to identify the correlates of top-down processing. For example, PET scans of brain activity elicited by Mooney images before and after disambiguation show that regions of the inferior temporal cortex, as well as the medial and lateral parietal regions, exhibit greater activity in response to recognized images (5). Progressive revealing paradigms, where an image gradually increases in coherence, elicit increased and accelerated functional magnetic resonance imaging (fMRI) activation in several regions, including the fusiform gyrus and the peristriate cortex, when subjects have prior experience with the images (6). In addition, EEG correlates show that distorted or schematic line drawings elicit face-specific N170 event-related potential components, which are belie...

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Section: Methodsmentioning

confidence: 99%

Imaging prior information in the brain

Gorlin

Meng

Sharma

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…We calculated an overall detection sensitivity using the total number of true detections divided by the total number of electrographic seizures (= 146 in 55 test subjects) in order to provide an estimation of the true sensitivity of the test algorithm. Because the outcome responses (detected or not detected) belong to clustered binary data, the standard error of the estimation needs to be adjusted by taking into account the correlation within each cluster (i.e., subject) (Rao and Scott, 1992; Durkalski et al, 2003). The equation for calculating the adjusted standard error is:

\sqrt{\frac{K}{false(K - 1 false) \cdot N^{2}} \sum_{i = 1}^{K} {false(x_{i} - k_{i} \cdot \hat{p} false)}^{2}}

where K = total number of test subjects (with seizures only); N = total number of test seizures; x i = number of true detections for subject i; k i = number of test seizures in subject i, and p@ = estimated overall sensitivity.…”

Section: Methodsmentioning

confidence: 99%

Assessment of a scalp EEG-based automated seizure detection system

Kelly

Shiau

Kern

et al. 2010

Clinical Neurophysiology

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Objective-The purpose of this study was to evaluate and validate an offline, automated scalp EEGbased seizure detection system and to compare its performance to commercially available seizure detection software.Methods-The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ~3653 hours with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (~1200 hours with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal ® , version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms.Results-The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24 hours, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24 hours, respectively. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p < 0.05) smaller FDR. NIH Public AccessConclusions-The study validates the performance of the IdentEvent™ .seizure detection system.Significance-With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

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“…Dukalski et al (2003) use a similar statistic for noninferiority testing on clustered binary data. By using the GEE approach, these test statistics do not require estimation of the correlation coefficients of the clustered binary data.…”

Section: Testing On Concordance Ratesmentioning

confidence: 99%

Sample Size for Comparing Correlated Concordance Rates

Jung

Barnhart

Sohn

et al. 2008

Journal of Biopharmaceutical Statistics

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We consider collecting the measurements of a gold standard and two methods with error from each site of subjects. We propose an asymptotic test statistic to compare the concordance rates of two methods with the gold standard and a closed-form sample size formula. Through simulations, we show that the test statistic accurately controls the type I error in small sample sizes, and the sample size formula accurately maintains the power in various settings. The proposed test statistic and the sample size formula can be easily modified for other indices for agreement such as sensitivity and specificity. A real eye study is taken as an example.

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Analysis of clustered matched‐pair data for a non‐inferiority study design

Cited by 19 publications

References 25 publications

Imaging prior information in the brain

Imaging prior information in the brain

Assessment of a scalp EEG-based automated seizure detection system

Sample Size for Comparing Correlated Concordance Rates

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