Correlation among baseline variables yields non-uniformity of p-values.
Ontology highlight
ABSTRACT:
SUBMITTER: Betensky RA
PROVIDER: S-EPMC5590981 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:AimsEchocardiography is a key diagnostic tool for assessment of myocardial performance and haemodynamics. Seismocardiography (SCG) can potentially provide fast and reliable assessments of key components related to myocardial performance. The aims of this study were to investigate the correlation between SCG and echocardiographic measures, and a decrease in preload by raising the subjects to a 30° head-up tilt position would be detected by both echocardiography and SCG.Methods and resultsA total of 45 subjects were included in the study. SCG and electrocardiogram were recorded simultaneously and afterwards echocardiography was recorded. The SCG signals were divided into individual heart beats using a duration-dependent Markov model. Using a fiducial point detection algorithm, the diastolic fiducial points were identified. The amplitudes from the SCG showed a high correlation, especially with the variable e' from the echocardiography. The peak-to-peak amplitude of the diastolic SCG complex and e' had a high correlation of 0.713 (P < 0.001). The second minimum in diastolic occurring after the closing of the aortic valve was the only amplitude showing a high correlation when comparing supine with head-up tilt in the SCG. All the echocardiography variables but E/e' showed a high correlation when comparing supine with head-up tilt.ConclusionThe results found in this study showed a high correlation between the amplitudes from the diastolic SCG and the diastolic variable e' from the echocardiography, thus indicating that the SCG could potentially be utilized to evaluate the diastolic function.
| S-EPMC9707922 | biostudies-literature
Project description:IntroductionDiscovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.MethodsWe used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.ResultsWe found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.DiscussionOur findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.
| S-EPMC10725839 | biostudies-literature
Project description:It is known that when one (or both) variable is multiplicative, the choice of differencing intervals (n) (for example, differencing interval of n = 7 means a weekly datum which is the product of seven daily data) affects the Pearson correlation coefficient (ρ) between variables (often asset returns) and that ρ converges to zero as n increases. This fact can cause the resulting correlation to be arbitrary, hence unreliable. We suggest using Spearman correlation (r) and prove that as n increases Spearman correlation tends to a limit which only depends on Pearson correlation based on the original data (i.e., the value for a single period). In addition, we show, via simulation, that the relative variability (CV) of the estimator of ρ increases with n and that r does not share this disadvantage. Therefore, we suggest using Spearman when one (or both) variable is multiplicative.
| S-EPMC6224093 | biostudies-literature
Project description:BackgroundIn livestock, residual variance has been studied because of the interest to improve uniformity of production. Several studies have provided evidence that residual variance is partially under genetic control; however, few investigations have elucidated genes that control it. The aim of this study was to identify genomic regions associated with within-family residual variance of yearling weight (YW; N = 423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv = 0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals (ln_[Formula: see text]).ResultsThe dEBVm and dEBVv were highly correlated, resulting in common regions associated with mean and residual variance of YW. However, higher effects on variance than the mean showed that these regions had effects on the variance beyond scale effects. More independent association results between mean and residual variance were obtained when null rmv was assumed. While 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association (Bayes Factor > 20) with dEBVv and ln_[Formula: see text], respectively, only suggestive signals were found for dEBVv_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation.ConclusionsIt is necessary to use a strategy like assuming null rmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.
| S-EPMC6097312 | biostudies-literature
Project description:We focus on estimating the average treatment effect in a randomized trial. If baseline variables are correlated with the outcome, then appropriately adjusting for these variables can improve precision. An example is the analysis of covariance (ANCOVA) estimator, which applies when the outcome is continuous, the quantity of interest is the difference in mean outcomes comparing treatment versus control, and a linear model with only main effects is used. ANCOVA is guaranteed to be at least as precise as the standard unadjusted estimator, asymptotically, under no parametric model assumptions and also is locally semiparametric efficient. Recently, several estimators have been developed that extend these desirable properties to more general settings that allow any real-valued outcome (e.g., binary or count), contrasts other than the difference in mean outcomes (such as the relative risk), and estimators based on a large class of generalized linear models (including logistic regression). To the best of our knowledge, we give the first simulation study in the context of randomized trials that compares these estimators. Furthermore, our simulations are not based on parametric models; instead, our simulations are based on resampling data from completed randomized trials in stroke and HIV in order to assess estimator performance in realistic scenarios. We provide practical guidance on when these estimators are likely to provide substantial precision gains and describe a quick assessment method that allows clinical investigators to determine whether these estimators could be useful in their specific trial contexts.
| S-EPMC5018399 | biostudies-literature
Project description:A property and criticism of instrumental variable (IV) estimators is that they estimate the exposure effect in the compliers, individuals whose exposure is influenced by the instrument. It is conceivable that the exposure effect is different in individuals whose exposure is not influenced by the instrument. For that reason, it is useful to know who the compliers are. We present methods for reporting averages and other statistics and comparing them between the compliers and noncompliers, which are applicable to scenarios in which the instrument and exposure are fixed in time and dichotomous. The methods are illustrated in a comparison of outcomes between teaching and nonteaching hospitals in patients undergoing brain or spinal surgery.
| S-EPMC6362835 | biostudies-literature
Project description:OBJECTIVE:The aim of this study was to investigate whether baseline (pre-weight loss) metabolic variables can predict weight regain. METHODS:About 117 women with overweight completed a weight loss program to achieve BMI?<?25 kg/m2 and were followed for 2 years. Resting metabolic rate, respiratory quotient, insulin sensitivity, and serum leptin concentration were measured pre-weight loss, while on energy balance, and as predictors of weight regain at 1 and 2 years. Rate and amount of weight loss also were examined as predictors, as these outcomes may reflect metabolic phenotype. RESULTS:Average weight loss was 12 (SD 2.5) kg, and regain was 48% (SD 35%) and 80% (SD 52%) at 1 and 2 years, respectively. In regression modeling, metabolic variables (both pre-weight loss and changes with weight loss) did not predict weight regain. However, initial weight loss and time to achieve BMI?<?25 were significant predictors of weight regain at 1 and 2 years, even after adjusting for confounders. CONCLUSIONS:Baseline (pre-weight loss) resting metabolic rate, respiratory quotient, insulin sensitivity, and leptin did not predict weight regain. However, a larger and faster weight loss was associated with a lower weight regain. Understanding the mechanisms behind interindividual variation in magnitude and rate of weight loss is needed to ensure better weight loss maintenance.
| S-EPMC7668116 | biostudies-literature
Project description:BackgroundIdentifying atypical lower limb biomechanics may help prevent the occurrence or recurrence of running-related injuries. No reference values for spatiotemporal or kinetic variables in healthy recreational runners are available in the scientific literature to support clinical management.PurposeTo (1) present speed- and sex-stratified reference values for spatiotemporal and kinetic variables in healthy adult recreational runners; (2) identify the determinants of these biomechanical variables; and (3) develop reference regression equations that can be used as a guide in a clinical context.Study designDescriptive laboratory study.MethodsThis study involved 860 healthy recreational runners (age, 19-65 years [38.5% women]) tested on an instrumented treadmill at their preferred running speed in randomly allocated, standardized running shoes with either hard or soft cushioning. Twelve common spatiotemporal and kinetic variables-including contact time, flight time, duty factor, vertical oscillation, step cadence, step length, vertical impact peak (VIP), time to VIP, vertical average loading rate, vertical stiffness, peak vertical ground-reaction force (GRF), and peak braking force-were derived from GRF recordings. Reference values for each biomechanical variable were calculated using descriptive statistics and stratified by sex and running speed category (≤7, 8, 9, 10, 11, 12, 13, 14, and ≥15 km/h). Correlations and multiple regression analyses were performed to identify potential determinants independently associated with each biomechanical variable and generate reference equations.ResultsThe mean running speed was 10.5 ± 1.3 km/h and 9 ± 1.1 km/h in men and women, respectively. While all potential predictors were significantly correlated with many of the 12 biomechanical variables, only running speed showed high correlations (r > 0.7). The adjusted R2 of the multiple regression equations ranged from 0.19 to 0.88.ConclusionThis study provides reference values and equations that may guide clinicians and researchers in interpreting spatiotemporal and kinetic variables in recreational runners.Clinical relevanceThe reference values can be used as targets for clinicians working with recreational runners in cases where there is a clinical suspicion of a causal relationship between atypical biomechanics and running-related injury.
| S-EPMC10588426 | biostudies-literature
Project description:With recent developments of data technology in biomedicine, factor data such as diagnosis codes and genomic features, which can have tens to hundreds of discrete and unorderable categorical values, have emerged. While considered as a fundamental problem in statistical analyses, the estimation of probability distribution for such factor variables has not studied much because the previous studies have mainly focused on continuous variables and discrete factor variables with a few categories such as sex and race. In this work, we propose a nonparametric Bayesian procedure to estimate the probability distribution of factors with many categories. The proposed method was demonstrated through simulation studies under various conditions and showed significant improvements on the estimation errors from the previous conventional methods. In addition, the method was applied to the analysis of diagnosis data of intensive care unit patients, and generated interesting medical hypotheses. The overall results indicate that the proposed method will be useful in the analysis of biomedical factor data.
| S-EPMC6108477 | biostudies-literature
Project description:BackgroundCOVID-19 has significantly impacted humans worldwide in recent times. Weather variables have a remarkable effect on COVID-19 spread all over the universe.ObjectivesThe aim of this study was to find the correlation between weather variables with COVID-19 cases and COVID-19 deaths.MethodsFive electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022. Both fixed-effects and random-effects models were used to calculate pooled correlation and 95% confidence interval (CI) for both effect measures. Included studies heterogeneity was measured by Cochrane chi-square test statistic Q, I2 and τ2 . Funnel plot was used to measure publication bias. A Sensitivity analysis was also carried out.ResultsTotal 38 studies were analyzed in this study. The result of this analysis showed a significantly negative impact on COVID-19 fixed effect incidence and weather variables such as temperature (r = -0.113∗∗∗), relative humidity (r = -0.019∗∗∗), precipitation (r = -0.143∗∗∗), air pressure (r = -0.073∗), and sunlight (r = -0.277∗∗∗) and also found positive impact on wind speed (r = 0.076∗∗∗) and dew point (r = 0.115∗∗∗). From this analysis, significant negative impact was also found for COVID-19 fixed effect death and weather variables such as temperature (r = -0.094∗∗∗), wind speed (r = -0.048∗∗), rainfall (r = -0.158∗∗∗), sunlight (r = -0.271∗∗∗) and positive impact for relative humidity (r = 0.059∗∗∗).ConclusionThis meta-analysis disclosed significant correlations between weather and COVID-19 cases and deaths. The findings of this analysis would help policymakers and the health professionals to reduce the cases and fatality rate depending on weather forecast techniques and fight this pandemic using restricted assets.
| S-EPMC9387066 | biostudies-literature