Longitudinal Functional Data Analysis

We consider analysis of dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated time visits and for each visit we record a functional variable. We propose a novel parsimonious modeling framework for the repeatedly observed functional variables that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed for the study of the dynamic behavior of the underlying process, and is computationally fast. Theoretical properties of this framework are studied and numerical investigation confirms excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis. Using Shiny (Chang et al., 2015) we implement interactive plots to help visualize longitudinal functional data as well as the various components and prediction obtained using the proposed method.Comment: 32 pages, 4 figure

Longitudinal Analysis of Generic Substitution

Using an extensive longitudinal dataset extracted from the Norwegian Prescription Database (NorPD) containing all prescriptions written in the period January 2004 to June 2007, we selected two particular drugs (chemical substances) used against cholesterol. The two brand-name products on the Norwegian markets were Provachol (atc code C10AA03) and Zocor (atc code C10AA01). The generics are Provastatine and Simastatine. The model accounts for taste persistence and is estimated on panel data. We find that prices have a negative impact on transitions in the sense that an increase in the brand price will reduce the transition from generics to brand and likewise an increase in the generic price will reduce the transition from brand to generics.generics, substitution, microdata, random utility model, longitudinal data

Longitudinal Analysis of Android Ad Library Permissions

This paper investigates changes over time in the behavior of Android ad libraries. Taking a sample of 100,000 apps, we extract and classify the ad libraries. By considering the release dates of the applications that use a specific ad library version, we estimate the release date for the library, and thus build a chronological map of the permissions used by various ad libraries over time. We find that the use of most permissions has increased over the last several years, and that more libraries are able to use permissions that pose particular risks to user privacy and security.Comment: Most 201

Longitudinal Analyses of the Effects of Trade Unions

This paper examines how measurement error biases longitudinal estimates of union effects. It develops numerical examples, statistical models, and econometric estimates which indicate that measurement error is a major problem in longitudinal data sets, so that longitudinal analyses do not provide the research panacea for determining the effects of unionism (or other economic forces) some have suggested. There are three major findings:1) The difference between the cross-section and longitudinal estimates is attributable in large part to random error in the measurement of who changes union status. Given modest errors of measurement, of the magnitudes observed,and a moderate proportion of workers changing union status, also of the magnitudes observed, measurement error biases downward estimated effects of unions by substantial amounts. 2) Longitudinal analysis of the effects of unionism on nonwage and wage outcomes tends to confirm the significant impact of unionism found in cross-section studies, with the longitudinal estimates of both nonwage and wage outcomes lover in the longitudinal analysis than in the cross-section analysis of the same data set. 3) The likely upward bias of cross-section estimates of the effect of unions and the likely downward bias of longitudinal estimates suggests that,under reasonable conditions, the two sets of estimates bound the "true" union impact posited in standard models of what unions do.

An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data

In many medical studies, patients are followed longitudinally and interest is on assessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable. These joint modeling approaches become intractable with even a few longitudinal variables. In this paper we propose a regression calibration approach for jointly modeling multiple longitudinal measurements and discrete time-to-event data. Ideally, a two-stage modeling approach could be applied in which the multiple longitudinal measurements are modeled in the first stage and the longitudinal model is related to the time-to-event data in the second stage. Biased parameter estimation due to informative dropout makes this direct two-stage modeling approach problematic. We propose a regression calibration approach which appropriately accounts for informative dropout. We approximate the conditional distribution of the multiple longitudinal measurements given the event time by modeling all pairwise combinations of the longitudinal measurements using a bivariate linear mixed model which conditions on the event time. Complete data are then simulated based on estimates from these pairwise conditional models, and regression calibration is used to estimate the relationship between longitudinal data and time-to-event data using the complete data. We show that this approach performs well in estimating the relationship between multivariate longitudinal measurements and the time-to-event data and in estimating the parameters of the multiple longitudinal process subject to informative dropout. We illustrate this methodology with simulations and with an analysis of primary biliary cirrhosis (PBC) data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS339 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Improvement or selection? A longitudinal analysis of students' views about experimental physics in their lab courses

Laboratory courses represent a unique and potentially important component of the undergraduate physics curriculum, which can be designed to allow students to authentically engage with the process of experimental physics. Among other possible benefits, participation in these courses throughout the undergraduate physics curriculum presents an opportunity to develop students' understanding of the nature and importance of experimental physics within the discipline as a whole. Here, we present and compare both a longitudinal and pseudo-longitudinal analysis of students' responses to a research-based assessment targeting students' views about experimental physics -- the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) -- across multiple, required lab courses at a single institution. We find that, while pseudo-longitudinal averages showed increases in students' E-CLASS scores in each consecutive course, analysis of longitudinal data indicates that this increase was not driven by a cumulative impact of laboratory instruction. Rather, the increase was driven by a selection effect in which students who persisted into higher-level lab courses already had more expert-like beliefs, attitudes, and expectations than their peers when they started the lower-level courses.Comment: 6 pages, 1 figure, submitted as a short paper to Phys. Rev. PE

On the Parameterization of the Longitudinal Hadronic Shower Profiles in Combined Calorimetry

The extension of the longitudinal hadronic shower profile parameterization which takes into account non-compensations of calorimeters and the algorithm of the longitudinal hadronic shower profile curve making for a combined calorimeter are suggested. The proposed algorithms can be used for data analysis from modern combined calorimeters like in the ATLAS detector at the LHC.Comment: Latex, 5 pages, 1 figur