Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors

BACKGROUND

Technology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-up is often small. Such data allow explorations of the role of intra-person variability in understanding disease etiology and characterizing disease processes. A specific example was to characterize pathogenesis of bacterial vaginosis (BV) using weekly vaginal microbiota Nugent assay scores collected over 2 years in post-menarcheeal women from Rakai, Uganda, and to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV.

METHODS

We use a fully data-driven approach to characterize the longitudinal patters of vaginal microbiota by considering the densely sampled Nugent scores to be random functions over time and performing dimension reduction by functional principal components. Extending a current functional data clustering method, we use a hierarchical functional clustering framework considering multiple data features to help identify clinically meaningful patterns of vaginal microbiota fluctuations. Additionally, multinomial logistic regression was used to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV.

RESULTS

Using weekly Nugent scores over 2 years of 211 sexually active and post-menarcheal women in Rakai, four patterns of vaginal microbiota variation were identified: persistent with a BV state (high Nugent scores), persistent with normal ranged Nugent scores, large fluctuation of Nugent scores which however are predominantly in the BV state; large fluctuation of Nugent scores but predominantly the scores are in the normal state. Higher Nugent score at the start of an interval, younger age group of less than 20 years, unprotected source for bathing water, a woman's partner's being not circumcised, use of injectable/Norplant hormonal contraceptives for family planning were associated with higher odds of persistent BV in women.

CONCLUSION

The hierarchical functional data clustering method can be used for fully data driven unsupervised clustering of densely sampled longitudinal data to identify clinically informative clusters and risk-factors associated with each cluster.

Multi-country clustering-based forecasting of healthy life expectancy

Healthy life expectancy (HLE) is an indicator that measures the number of years individuals at a given age are expected to live free of disease or disability. HLE forecasting is essential for planning the provision of health care to elderly populations and appropriately pricing Long Term Care insurance products. In this paper, we propose a methodology that simultaneously forecasts HLE for groups of countries and allows for investigating similarities in their HLE patterns. We firstly apply a functional data clustering to the multivariate time series of HLE at birth of different countries for the years 1990-2019 provided by the Global Burden of Disease Study. Three clusters are identified for both genders. Then, we carry out the HLE simultaneous forecasting of the populations within each cluster by a multivariate random walk with drift. Numerical results and the statistical significance of the parameters of the identified multivariate processes are shown. Demographic evidences on the different evolution of HLE between countries are commented.

Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative

OBJECTIVE

The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in 1 year to predict disease progression over 8 years.

DESIGN

To model OA progression, we used a mixed-effects mixture model and 8-year data from the Osteoarthritis Initiative (OAI)-specifically, joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. We included 1243 subjects who at enrollment were classified as being at high risk of developing OA based on age, body mass index (BMI), and medical and occupational histories. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection (LASSO) regression models for predicting the probabilities of belonging to each cluster. Areas under the receiver operating characteristic curve (AUC) represent predictive performance on held-out data.

RESULTS

Based on joint space narrowing, subjects clustered as progressing or non-progressing. Based on pain scores, they clustered as stable, improving, or worsening. Radiographic progression could be predicted with high accuracy (AUC = .86) using data from two visits spanning 1 year, whereas pain progression could be predicted with high accuracy (AUC = .95) using data from a single visit. Joint space narrowing and pain progression were not associated.

CONCLUSION

Statistical models for characterizing and predicting OA progression promise to improve clinical trial design and OA prevention efforts in the future.

Massively parallel nonparametric regression, with an application to developmental brain mapping

We propose a penalized spline approach to performing large numbers of parallel non-parametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70000 brain locations. Supplementary materials, including an appendix and an R package, are available online.