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Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis.

ABSTRACT: Scheithauer et al. (2020) recently demonstrated that differences in the source of baseline data extracted from a functional analysis (FA) may not affect subsequent clinical decision-making in comparison to a standard baseline. These outcomes warrant additional quantitative examination, as correspondence of visual analysis has sometimes been reported to be unreliable. In the current study, we quantified the occurrence of false positives within a dataset of FA and baseline data using the dual-criteria (DC) and conservative dual-criteria (CDC) methods. Results of this quantitative analysis suggest that false positives were more likely when using FA data (rather than original baseline data) as the initial treatment baseline. However, both sources of baseline data may have acceptably low levels of false positives for practical use. Overall, the findings provide preliminary quantitative support for the conclusion that determinations of effective treatment may be easily obtained using different sources of baseline data.

SUBMITTER: Falligant JM

PROVIDER: S-EPMC8016450 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis.

Falligant John Michael JM McNulty Molly K MK Kranak Michael P MP Hausman Nicole L NL Rooker Griffin W GW

Journal of applied behavior analysis 20200426 4

Scheithauer et al. (2020) recently demonstrated that differences in the source of baseline data extracted from a functional analysis (FA) may not affect subsequent clinical decision-making in comparison to a standard baseline. These outcomes warrant additional quantitative examination, as correspondence of visual analysis has sometimes been reported to be unreliable. In the current study, we quantified the occurrence of false positives within a dataset of FA and baseline data using the dual-crit ...[more]

PMID: 32337720

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Project description:SIGNIFICANCE:The Bifocal & Atropine in Myopia (BAM) study aims to determine whether combining 0.01% atropine and +2.50-diopter add center-distance soft bifocal contact lenses (SBCL) slows myopia progression more than SBCL alone. The results could provide significant information on the myopia control effect of combining optical and pharmacological treatments. PURPOSE:This article describes the subject characteristics at baseline, the study methods, and the short-term effects of this combination treatment on visual acuity (VA) and vision-related outcomes. METHODS:Subjects from the BAM study who met the baseline eligibility criteria were dispensed the combination treatment for 2 weeks to determine final eligibility. Outcome measures included VA at near and distance (Bailey-Lovie logMAR charts), near phoria (modified Thorington), accommodative lag (Grand Seiko WAM-5500), and pupil size (NeurOptics VIP-200 Pupillometer). Compliance was monitored using surveys. Two subgroups in the Bifocal Lenses In Nearsighted Kids study, single-vision contact lens wearers and those who wore +2.50-diopter add SBCL, will serve as the age-matched historical controls for BAM study. RESULTS:Forty-nine BAM subjects (9.6 ± 1.4 years) were enrolled; mean spherical equivalent cycloplegic autorefraction was -2.33 ± 1.03 diopters. After 2 weeks of treatment, the best-corrected low-contrast (10% Michelson) distance VA was reduced (pre-treatment, +0.09 ± 0.07; post-treatment, +0.16 ± 0.08; P < .0001), but the high-contrast VA at near or distance was unaffected. Near phoria increased by approximately 2 in the exo direction (P = .01), but the accommodative lag was unchanged. The pupil size was not significantly different between pre-treatment and post-treatment of either the photopic or mesopic condition. Surveys indicated that the subjects wore SBCL 77 ± 22% of waking hours and used atropine 6.4 ± 0.7 days per week. CONCLUSIONS:Two weeks of combination treatment reduced low-contrast distance VA and increased near exophoria slightly, but the subjects were compliant and tolerated the treatment well.

Project description:BackgroundIn bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: "molecular identification" (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices.ResultsWe describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events.ConclusionsThe paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors.

Dataset Information

Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis.

Publications

Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis.

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