Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis.
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ABSTRACT:
SUBMITTER: Falligant JM
PROVIDER: S-EPMC8016450 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description:Urban areas have complex thermal distribution. We examined the association between extreme temperature and mortality in urban Ontario, using two temperature data sources: high-resolution and weather station data. We used distributed lag non-linear Poisson models to examine census division-specific temperature-mortality associations between May and September 2005-2012. We used random-effect multivariate meta-analysis to pool results, adjusted for air pollution and temporal trends, and presented risks at the 99th percentile compared to minimum mortality temperature. As additional analyses, we varied knots, examined associations using different temperature metrics (humidex and minimum temperature), and explored relationships using different referent values (most frequent temperature, 75th percentile of temperature distribution). Weather stations yielded lower temperatures across study months. U-shaped associations between temperature and mortality were observed using both high-resolution and weather station data. Temperature-mortality relationships were not statistically significant; however, weather stations yielded estimates with wider confidence intervals. Similar findings were noted in additional analyses. In urban environmental health studies, high-resolution temperature data is ideal where station observations do not fully capture population exposure or where the magnitude of exposure at a local level is important. If focused upon temperature-mortality associations using time series, either source produces similar temperature-mortality relationships.
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Project description:BackgroundA cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species.ResultsIn this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency.ConclusionFunctional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.
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