Correction: Estimation accuracy in the psychological sciences.
Ontology highlight
ABSTRACT:
SUBMITTER: Davis-Stober CP
PROVIDER: S-EPMC6394978 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Sample means comparisons are a fundamental and ubiquitous approach to interpreting experimental psychological data. Yet, we argue that the sample and effect sizes in published psychological research are frequently so small that sample means are insufficiently accurate to determine whether treatment effects have occurred. Generally, an estimator should be more accurate than any benchmark that systematically ignores information about the relations among experimental conditions. We consider two such benchmark estimators: one that randomizes the relations among conditions and another that always assumes no treatment effects. We show conditions under which these benchmark estimators estimate the true parameters more accurately than sample means. This perverse situation can occur even when effects are statistically significant at traditional levels. Our argument motivates the need for regularized estimates, such as those used in lasso, ridge, and hierarchical Bayes techniques.
| S-EPMC6261010 | biostudies-literature
Project description:The multiplexing capabilities of isobaric mass tag based protein quantification, such as Tandem Mass Tags (TMT) or Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) have dramatically increased the scope of Mass Spectrometry (MS) based proteomics studies. Not only does the technology allow for the simultaneous quantification of multiple samples in a single MS injection, but its seamless compatibility with extensive sample pre-fractionation methods allow for comprehensive study of complex proteomes. However, reporter ion based quantification has often been criticized for limited quantification accuracy due to interference from co-eluting peptides and peptide fragments. In this study, we investigate the extent of this problem and propose an effective and easy-to-implement remedy relying on spiking a 6-protein calibration mixture to the samples subject to relative quantification. Our ratio adjustment approach was evaluated on a large scale TMT 10-plex dataset derived from cancer cell lines with chromosome instability. Furthermore, we analyzed a complex 2-proteome artificial sample mixture and investigated the precision of TMT and precursor ion intensity based Label Free Quantification. Comparing the two methods we observed that the isobaric tag based quantification workflow, due to its superior precision, was able to confirm more and smaller protein abundance changes, at a fixed False Positive Rate.
2016-07-01 | PXD003346 | Pride
Project description: Not available
| S-EPMC11332099 | biostudies-literature
Project description:An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.
| S-EPMC6736701 | biostudies-literature
Project description:High-risk mechanisms in trauma usually dictate certain treatment and evaluation in protocolized care. A 10-15 feet (ft) fall is traditionally cited as an example of a high-risk mechanism, triggering trauma team activations and costly work-ups. The height and other details of mechanism are usually reported by lay bystanders or prehospital personnel. This small observational study was designed to evaluate how accurate or inaccurate height estimation may be among typical bystanders.This was a blinded, prospective study conducted on the grounds of a community hospital. Four panels with lines corresponding to varying heights from 1-25 ft were hung within a building structure that did not have stories or other possibly confounding factors by which to judge height. The participants were asked to estimate the height of each line using a multiple-choice survey-style ballot. Participants were adult volunteers composed of various hospital and non-hospital affiliated persons, of varying ages and genders. In total, there were 96 respondents.For heights equal to or greater than 15 ft, less than 50% of participants of each job description were able to correctly identify the height. When arranged into a scatter plot, as height increased, the likelihood to underestimate the correct height was evident, having a strong correlation coefficient (R=+0.926) with a statistically significant p value = <0.001.The use of vertical height as a predictor of injury severity is part of current practice in trauma triage. This data is often an estimation provided by prehospital personnel or bystanders. Our small study showed bystanders may not estimate heights accurately in the field. The greater the reported height, the less likely it is to be accurate. Additionally, there is a higher likelihood that falls from greater than 15 ft may be underestimated.
| S-EPMC6123090 | biostudies-other
Project description:[This corrects the article on p. 74 in vol. 106, PMID: 29339936.].
| S-EPMC5886519 | biostudies-other
Project description:Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports.This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time.The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (± standard deviation [±SD]) absolute error was 4.8 (±7.3) minutes using linear arc, 3.5 (±5.4) minutes using Google Maps, and 4.4 (±5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p < 0.01). Estimation accuracy was lower for each method among transports more than 20 minutes (mean [±SD] absolute error was 12.7 [±11.7] minutes for linear arc, 9.8 [±10.5] minutes for Google Maps, and 11.6 [±10.9] minutes for ArcGIS). Estimates were within 5 minutes of observed transport time for 79% of linear arc estimates, 86.6% of Google Maps estimates, and 81.3% of ArcGIS estimates. The regression-based approach did not substantially improve estimation. There were large differences in hospital catchment areas estimated by each method.Route-based transport time estimates demonstrate moderate accuracy. These methods can be valuable for informing a host of decisions related to the system organization and patient access to emergency medical care; however, they should be employed with sensitivity to their limitations.
| S-EPMC4251659 | biostudies-literature
Project description:Background and objectivesAdequate estimation of renal function in obese patients is essential for the classification of patients in CKD category as well as the dose adjustment of drugs. However, the body size descriptor for GFR indexation is still debatable, and formulas are not validated in patients with extreme variations of weight.Design, setting, participants, & measurementsThis study included 209 stages 1-5 CKD obese patients referred to the Department of Renal Function Study at the University Hospital in Lyon between 2010 and 2013 because of suspected renal dysfunction. GFR was estimated with the Chronic Kidney Disease and Epidemiology equation (CKD-EPI) and measured with a gold standard method (inulin or iohexol) not indexed (mGFR) or indexed to body surface area determined by the Dubois and Dubois formula with either real (mGFRr) or ideal (mGFRi) body weight. Mean bias (eGFR-mGFR), precision, and accuracy of mGFR were compared with the results obtained for nonobese participants (body mass index between 18.5 and 24.9) who had a GFR measurement during the same period of time.ResultsMean mGFRr (51.6 ± 24.2 ml/min per 1.73 m(2)) was significantly lower than mGFR, mGFRi, and eGFRCKD-EPI. eGFRCKD-EPI had less bias with mGFR (0.29; -1.7 to 2.3) and mGFRi (-1.62; -3.1 to 0.45) compared with mGFRr (8.7; 7 to 10). This result was confirmed with better accuracy for the whole cohort (78% for mGFR, 84% for mGFRi, and 72% for mGFRr) and participants with CKD stages 3-5. Moreover, the Bland Altman plot showed better agreement between mGFR and eGFRCKD-EPI. The bias between eGFRCKD-EPI and mGFRr was greater in obese than nonobese participants (8.7 versus 0.58, P<0.001).ConclusionsThis study shows that, in obese CKD patients, the performance of eGFRCKD-EPI is good for GFR ? 60 ml/min per 1.73 m(2). Indexation of mGFR with body surface area using ideal body weight gives less bias than mGFR scaled with body surface area using real body weight.
| S-EPMC3974350 | biostudies-literature
Project description:BackgroundFoamy viruses (FVs) are the most genetically stable viruses of the retrovirus family. This is in contrast to the in vitro error rate found for recombinant FV reverse transcriptase (RT). To investigate the accuracy of FV genome copying in vivo we analyzed the occurrence of mutations in HEK 293T cell culture after a single round of reverse transcription using a replication-deficient vector system. Furthermore, the frequency of FV recombination by template switching (TS) and the cross-packaging ability of different FV strains were analyzed.ResultsWe initially sequenced 90,000 nucleotides and detected 39 mutations, corresponding to an in vivo error rate of approximately 4 x 10-4 per site per replication cycle. Surprisingly, all mutations were transitions from G to A, suggesting that APOBEC3 activity is the driving force for the majority of mutations detected in our experimental system. In line with this, we detected a late but significant APOBEC3G and 3F mRNA by quantitative PCR in the cells. We then analyzed 170,000 additional nucleotides from experiments in which we co-transfected the APOBEC3-interfering foamy viral bet gene and observed a significant 50% drop in G to A mutations, indicating that APOBEC activity indeed contributes substantially to the foamy viral replication error rate in vivo. However, even in the presence of Bet, 35 out of 37 substitutions were G to A, suggesting that residual APOBEC activity accounted for most of the observed mutations. If we subtract these APOBEC-like mutations from the total number of mutations, we calculate a maximal intrinsic in vivo error rate of 1.1 x 10-5 per site per replication. In addition to the point mutations, we detected one 49 bp deletion within the analyzed 260000 nucleotides.Analysis of the recombination frequency of FV vector genomes revealed a 27% probability for a template switching (TS) event within a 1 kilobase (kb) region. This corresponds to a 98% probability that FVs undergo at least one additional TS event per replication cycle. We also show that a given FV particle is able to cross-transfer a heterologous FV genome, although at reduced efficiency than the homologous vector.ConclusionOur results indicate that the copying of the FV genome is more accurate than previously thought. On the other hand recombination among FV genomes appears to be a frequent event.
| S-EPMC2678077 | biostudies-literature
Project description:The multiplexing capabilities of isobaric mass tag based protein quantification, such as Tandem Mass Tags (TMT) or Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) have dramatically increased the scope of Mass Spectrometry (MS) based proteomics studies. Not only does the technology allow for the simultaneous quantification of multiple samples in a single MS injection, but its seamless compatibility with extensive sample pre-fractionation methods allow for comprehensive study of complex proteomes. However, reporter ion based quantification has often been criticized for limited quantification accuracy due to interference from co-eluting peptides and peptide fragments. In this study, we investigate the extent of this problem and propose an effective and easy-to-implement remedy relying on spiking a 6-protein calibration mixture to the samples subject to relative quantification. Our ratio adjustment approach was evaluated on a large scale TMT 10-plex dataset derived from cancer cell lines with chromosome instability. Furthermore, we analyzed a complex 2-proteome artificial sample mixture and investigated the precision of TMT and precursor ion intensity based Label Free Quantification. Comparing the two methods we observed that the isobaric tag based quantification workflow, due to its superior precision, was able to confirm more and smaller protein abundance changes, at a fixed False Positive Rate.
2018-08-27 | MSV000082855 | MassIVE