21 research outputs found
Functional data analytic approach of modeling ECG T-wave shape to measure cardiovascular behavior
The T-wave of an electrocardiogram (ECG) represents the ventricular
repolarization that is critical in restoration of the heart muscle to a
pre-contractile state prior to the next beat. Alterations in the T-wave reflect
various cardiac conditions; and links between abnormal (prolonged) ventricular
repolarization and malignant arrhythmias have been documented. Cardiac safety
testing prior to approval of any new drug currently relies on two points of the
ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few
beats are measured. Using functional data analysis, a statistical approach
extracts a common shape for each subject (reference curve) from a sequence of
beats, and then models the deviation of each curve in the sequence from that
reference curve as a four-dimensional vector. The representation can be used to
distinguish differences between beats or to model shape changes in a subject's
T-wave over time. This model provides physically interpretable parameters
characterizing T-wave shape, and is robust to the determination of the endpoint
of the T-wave. Thus, this dimension reduction methodology offers the strong
potential for definition of more robust and more informative biomarkers of
cardiac abnormalities than the QT (or QT corrected) interval in current use.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS273 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
About item response theory models and how they work
This article is about FMCSA data and its analysis. The article responds to the two-part question: How does an Item Response Theory (IRT) model work differently . . . or better than any other model? The response to the first part is a careful, completely non-technical exposition of the fundamentals for IRT models. It differentiates IRT models from other models by providing the rationale underlying IRT modeling and by using graphs to illustrate two key properties for data items. The response to the second part of the question about superiority of an IRT model is, “it depends.” For FMCSA data, serious challenges arise from complexity of the data and from heterogeneity of the carrier industry. Questions are posed that will need to be addressed to determine the success of the actual model developed and of the scoring system
Tree-Based Methods: A Tool for Modeling Nonlinear Complex Relationships and Generating New Insights from Data
Our paper introduces tree-based methods, specifically classification and regression trees (CRT), to study student achievement. CRT allows data analysis to be driven by the data’s internal structure. Thus, CRT can model complex nonlinear relationships and supplement traditional hypothesis-testing approaches to provide a fuller picture of the topic being studied. Using Early Childhood Longitudinal Study-Kindergarten 2011 data as a case study, our research investigated predictors from students’ demographic backgrounds to ascertain their relationships to students’ academic performance and achievement gains in reading and math. In our study, CRT displays complex patterns between predictors and outcomes; more specifically, the patterns illuminated by the regression trees differ across the subject areas (i.e., reading and math) and between the performance levels and achievement gains. Through the use of real-world assessment datasets, this article demonstrates the strengths and limitations of CRT when analyzing student achievement data as well as the challenges. When achievement data such as achievement gains in our case study are not linearly strongly related to any continuous predictors, regression trees may make more accurate predictions than general linear models and produce results that are easier to interpret. Our study illustrates scenarios when CRT on achievement data is most appropriate and beneficial
A Case Study of Nonresponse Bias Analysis
Nonresponse bias is a widely prevalent problem for data collections. We
develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in
cross-sectional studies, and apply these steps to the Early Childhood
Longitudinal Study, Kindergarten Class of 2010-11. A key step is the
construction of indices of nonresponse bias based on proxy pattern-mixture
models for survey variables of interest. A novel feature is to characterize the
strength of evidence about nonresponse bias contained in these indices, based
on the strength of relationship of the characteristics in the nonresponse
adjustment with the key survey variables. Our NRBA incorporates missing at
random and missing not at random mechanisms, and all analyses can be done
straightforwardly with standard statistical software
Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors
Putting data into the public domain is not the same thing as making those
data accessible for intelligent analysis. A distinguished group of editors and
experts who were already engaged in one way or another with the issues inherent
in making research data public came together with statisticians to initiate a
dialogue about policies and practicalities of requiring published research to
be accompanied by publication of the research data. This dialogue carried
beyond the broad issues of the advisability, the intellectual integrity, the
scientific exigencies to the relevance of these issues to statistics as a
discipline and the relevance of statistics, from inference to modeling to data
exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Toward a more ethical clinical trial
Current methods of conducting clinical trials require the patient to agree to have his treatment assigned randomly, where his individual characteristics are taken into account only to balance the treatment groups. A Bayesian alternative involves eliciting the prior opinions of the group of clinicians who designed the study. Each patient is then guaranteed that the treatment he will receive is the best for him either in the opinion of at least one individual clinician or as a consensus of several, given the patient's characteristics and all the information available from the trial when the assignment is made
Variance component analysis of a multi-site study for the reproducibility of multiple reaction monitoring measurements of peptides in human plasma.
In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.In this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models. The factors of peptides and transitions are shown to be major contributors to the variance of the measurements considering heavy (isotopic) peptides alone. For the light ((12)C) peptides alone, in addition to these factors, the factor of study*peptide also contributes significantly to the variance of the measurements. Heterogeneous peptide component models as well as influence analysis identify the outlier peptides in the study, which are then excluded from the analysis. Using a log-log scale transformation and subtracting the heavy/isotopic peptide [internal standard] measurement from the peptide measurements (i.e., taking the logarithm of the peak area ratio in the original scale establishes that), the MRM measurements are overall consistent across laboratories following the same standard operating procedures, and the variance components related to sites, transitions and higher-order interaction terms involving sites have greatly reduced impact. Thus the heavy peptides have been effective in reducing apparent inter-site variability. In addition, the estimates of intercepts and slopes of the calibration curves are calculated for the sub-studies.The MRM measurements are overall consistent across laboratories following the same standard operating procedures, and heavy peptides can be used as an effective internal standard for reducing apparent inter-site variability. Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research
Nonresponse Bias Analysis in Longitudinal Educational Assessment Studies
Longitudinal studies are subject to nonresponse when individuals fail to
provide data for entire waves or particular questions of the survey. We compare
approaches to nonresponse bias analysis (NRBA) in longitudinal studies and
illustrate them on the Early Childhood Longitudinal Study, Kindergarten Class
of 2010-11 (ECLS-K:2011). Wave nonresponse with attrition yields a monotone
missingness pattern, and we discuss weighting and multiple imputation (MI)
approaches to NRBA for monotone patterns when the missingness mechanism is
assumed missing at random (MAR). Weighting adjustments are effective when the
constructed weights are correlated to the survey variable of interest. MI
allows for incomplete variables to be included in the imputation model,
yielding potentially less biased and more efficient estimates when the
variables are predictive of the survey outcome. Multilevel models with maximum
likelihood estimation and marginal models estimated using generalized
estimating equations can also handle incomplete longitudinal data. We add
offsets in the MI results to provide sensitivity analyses to assess missing not
at random deviations from MAR. We conduct NRBA for descriptive summaries and
analytic model estimates and find that in the ECLS-K:2011 application NRBA
yields minor changes to the substantive conclusions. The strength of evidence
about our NRBA depends on the strength of the relationship between the
characteristics in the nonresponse adjustment and the key survey variables, so
the key to a successful NRBA is to include strong predictors