arXiv subjects

Nell Sedransk

Publications and source records attributed to Nell Sedransk.

4 recordsLinked to original sources

arXiv2024

Nonresponse Bias Analysis in Longitudinal Studies: A Comparative Review with an Application to the Early Childhood Longitudinal Study

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 often yields a monotone missingness pattern, and the missingness mechanism can be missing at random (MAR) or missing not at random (MNAR). We discuss weighting, multiple imputation (MI), incomplete data modeling, and Bayesian approaches to NRBA for monotone patterns. Weighting adjustments are effective when the constructed weights are correlated to the survey outcome of interest. MI allows for variables with missing values to be included in the imputation model, yielding potentially less biased and more efficient estimates. Multilevel models with maximum likelihood estimation and marginal models estimated using generalized estimating equations can also handle incomplete longitudinal data. Bayesian methods introduce prior information and potentially stabilize model estimation. We add offsets in the MAR results to provide sensitivity analyses to assess MNAR deviations. 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 outcomes, so the key to a successful NRBA is to include strong predictors.

stat.ME↗

arXiv2022

A Case Study of Nonresponse Bias Analysis In Educational Assessment Surveys

Nonresponse bias is a widely prevalent problem for data on education. 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 the relationship between the characteristics in the nonresponse adjustment and the key survey variables. Our NRBA improves existing methods by incorporating both missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software.

stat.ME↗

arXiv2010

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.

stat.ME↗

arXiv2010

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.

stat.AP↗