Explore the vast range of titles available.

The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high-dimensional statistics. Functional Principal Components Analysis (FPCA) and Functional Linear Mixed Model (FLMM) are two major statistical tools used to address the estimation of large functional and longitudinal data; however, the former suffers from a dramatically increasing computational burden while the latter does not have clear asymptotic properties. In this paper, we propose a computationally effective estimator of large functional and longitudinal data within the framework of FLMM, in which all the parameters can be automatically estimated. Under certain regularity assumptions, we prove that the mean function estimation and individual trajectory prediction reach the minimax lower bounds of all nonparametric estimations. Through numerous simulations and real data analysis, we show that our new estimator outperforms the traditional FPCA in terms of mean function estimation, individual trajectory prediction, variance estimation, covariance function estimation, and computational effectiveness.

The interpretation of a regression coefficient obtained from a longitudinal data analysis is a combination of a within-subject part and a between-subject part. The hybrid model is used to disentangle the two components. The purpose of this article was to illustrate and discuss the use of the hybrid model in epidemiologic studies. In the hybrid model the between-subject part of the relationship is obtained using the individual mean value over time, whereas the within-subject part is obtained using the deviation score, that is, the differences between the observations and the individual mean value. It was shown that the regression coefficient of a standard mixed model analysis is a sort of weighted average of the between- and within-subject part of the relationship. When the outcome was continuous the separate analyses to estimate the two components of a longitudinal relationship were equal to the estimation in the hybrid model. However, for dichotomous outcome, the estimations were slightly different. The hybrid model is an elegant, easy to perform method to disentangle the within- and between-subject part of a relationship in longitudinal studies. •The between-subject part is obtained by the individual mean value over time.•The within-subject part is obtained by using the deviation score.•The deviation score is the difference between observations and individual mean.•The results of a hybrid logistic model should be interpreted with caution.

Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject’s age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that ( i ) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and ( ii ) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.

Background In the development of prediction models for a clinical event, it is common to use the static prediction modeling (SPM), a regression model that relates baseline predictors to the time to event. In many situations, the data used in training and validation are from longitudinal studies, where predictor variables are time-varying and measured at clinical visits. But these data are not used in SPM. The landmark analysis (LA), previously proposed for dynamic prediction with longitudinal data, has interpretational difficulty when the baseline is not a risk-changing clinical milestone, as is often the case in observational studies of chronic disease without intervention. Methods This paper studies the generalized landmark analysis (GLA), a statistical framework to develop prediction models for longitudinal data. The GLA includes the LA as a special case, and generalizes it to situations where the baseline is not a risk-changing clinical milestone with a more useful interpretation. Unlike the LA, the landmark variable does not have to be time since baseline in the GLA, but can be any time-varying prognostic variable. The GLA can also be viewed as a longitudinal generalization of localized prediction, which has been studied in the context of low-dimensional cross-sectional data. We studied the GLA using data from the Chronic Renal Insufficiency Cohort (CRIC) Study and the Wisconsin Allograft Replacement Database (WisARD) and compared the prediction performance of SPM and GLA. Results In various validation populations from longitudinal data, the GLA generally had similarly or better predictive performance than SPM, with notable improvement being seen when the validation population deviated from the baseline population. The GLA also demonstrated similar or better predictive performance than LA, due to its more general model specification. Conclusions GLA is a generalization of the LA such that the landmark variable does not have to be the time since baseline. It has better interpretation when the baseline is not a risk-changing clinical milestone. The GLA is more adaptive to the validation population than SPM and is more flexible than LA, which may help produce more accurate prediction.

In longitudinal studies, it is of fundamental importance to understand the dynamics in the mean function, variance function and correlations of the repeated or clustered measurements. For modelling the covariance structure, Cholesky‐type decomposition‐based approaches have been demonstrated to be effective. However, parsimonious approaches for directly revealing the correlation structure between longitudinal measurements remain less well explored, and existing joint modelling approaches may encounter difficulty in interpreting the covariation structure. We propose a novel joint mean–variance correlation modelling approach for longitudinal studies. By applying hyperspherical co‐ordinates, we obtain an unconstrained parameterization for the correlation matrix that automatically guarantees its positive definiteness, and we develop a regression approach to model the correlation matrix of the longitudinal measurements by exploiting the parameterization. The modelling framework proposed is parsimonious, interpretable and flexible for analysing longitudinal data. Extensive data examples and simulations support the effectiveness of the approach proposed.

ObjectiveThe statistical analysis for a 2-arm randomised controlled trial (RCT) with a baseline outcome followed by a few assessments at fixed follow-up times typically invokes traditional analytic methods (eg, analysis of covariance (ANCOVA), longitudinal data analysis (LDA)). ‘Constrained’ longitudinal data analysis (cLDA) is a well-established unconditional technique that constrains means of baseline to be equal between arms. We use an analysis of fasting lipid profiles from the Group Medical Clinics (GMC) longitudinal RCT on patients with diabetes to illustrate applications of ANCOVA, LDA and cLDA to demonstrate theoretical concepts of these methods including the impact of missing data.MethodsFor the analysis of the illustrated example, all models were fit using linear mixed models to participants with only complete data and to participants using all available data.ResultsWith complete data (n=195), 95% CI coverage are equivalent for ANCOVA and cLDA with an estimated 11.2 mg/dL (95% CI −19.2 to −3.3; p=0.006) lower mean low-density lipoprotein (LDL) cholesterol in GMC compared with usual care. With all available data (n=233), applying the cLDA model yielded an LDL improvement of 8.9 mg/dL (95% CI −16.7 to −1.0; p=0.03) for GMC compared with usual care. The less efficient, LDA analysis yielded an LDL improvement of 7.2 mg/dL (95% CI −17.2 to 2.8; p=0.15) for GMC compared with usual care.ConclusionsUnder reasonable missing data assumptions, cLDA will yield efficient treatment effect estimates and robust inferential statistics. It may be regarded as the method of choice over ANCOVA and LDA.

To measure the parallel interactive development of latent ability and processing speed using longitudinal item response accuracy (RA) and longitudinal response time (RT) data, we proposed three longitudinal joint modeling approaches from the structural equation modeling perspective, namely unstructured-covariance-matrix-based longitudinal joint modeling, latent growth curve-based longitudinal joint modeling, and autoregressive cross-lagged longitudinal joint modeling. The proposed modeling approaches can not only provide the developmental trajectories of latent ability and processing speed individually, but also exploit the relationship between the change in latent ability and processing speed through the across-time relationships of these two constructs. The results of two empirical studies indicate that (1) all three models are practically applicable and have highly consistent conclusions in terms of the changes in ability and speed in the analysis of the same data set, and (2) additional analysis of the RT data and acquisition of individual processing speed measurements can reveal the parallel interactive development phenomena that are difficult to detect using RA data alone. Furthermore, the results of our simulation study demonstrate that the proposed Bayesian Markov chain Monte Carlo estimation algorithm can ensure accurate model parameter recovery for all three proposed longitudinal joint models. Finally, the implications of our findings are discussed from the research and practice perspectives.

Longitudinal cohort studies of cognitive aging must confront several sources of within-person variability in scores. In this article, we compare several neuropsychological measures in terms of longitudinal error variance and relationships with biomarker-assessed brain amyloidosis (Aβ). Analyses used data from the Wisconsin Registry for Alzheimer's Prevention. We quantified within-person longitudinal variability and age-related trajectories for several global and domain-specific composites and their constituent scores. For a subset with cerebrospinal fluid or amyloid positron emission tomography measures, we examined how Aβ modified cognitive trajectories. Global and theoretically derived composites exhibited lower intraindividual variability and stronger age × Aβ interactions than did empirically derived composites or raw scores from single tests. For example, the theoretical executive function outperformed other executive function scores on both metrics. These results reinforce the need for careful selection of cognitive outcomes in study design, and support the emerging consensus favoring composites over single-test measures. •Identifying early cognitive change requires tests with low error variance.•In a middle-aged sample, composites were less noisy than single tests.•Global and theory-driven composites outperformed data-driven composites.

The impact of the pandemic is being very significant psychologically, especially for people who were already vulnerable in these aspects, such as adults with Autism Spectrum Disorders (ASD) and Intellectual Disability (ID). A longitudinal analysis of motor aspects such as balance and gait, executive functions in daily life, severity of symptoms characteristic of autism, and degree of subjective well-being was performed in 53 adults with ASD and ID. A repeated measures ANOVA was performed and three measures were taken, the first in December 2019, the second in March 2020, and the last in July 2020. The results demonstrated a significant decrease in balance on the latter measure, along with a deterioration in well-being and ASD symptoms in the period of seclusion and an improvement in executive functions after seclusion.

This study provides a new test of time-use diary methodology, comparing diaries with a pair of objective criterion measures: wearable cameras and accelerometers. A volunteer sample of respondents (n = 148) completed conventional self-report paper time-use diaries using the standard UK Harmonised European Time Use Study (HETUS) instrument. On the diary day, respondents wore a camera that continuously recorded images of their activities during waking hours (approximately 1,500–2,000 images/day) and also an accelerometer that tracked their physical activity continuously throughout the 24-hour period covered by the diary. Of the initial 148 participants recruited, 131 returned usable diary and camera records, of whom 124 also provided a usable whole-day accelerometer record. The comparison of the diary data with the camera and accelerometer records strongly supports the use of diary methodology at both the aggregate (sample) and individual levels. It provides evidence that time-use data could be used to complement physical activity questionnaires for providing population-level estimates of physical activity. It also implies new opportunities for investigating techniques for calibrating metabolic equivalent of task (MET) attributions to daily activities using large-scale, population-representative time-use diary studies.