Statistical Inference for Streamed Longitudinal Data

TL;DR

This paper introduces a computationally efficient streaming inference method for longitudinal data that updates estimates without re-analyzing the entire dataset, suitable for large-scale wearable device data.

Contribution

It develops a recursive estimation framework for longitudinal data that maintains statistical properties while reducing computational burden.

Findings

The method is consistent and asymptotically normal as data batches grow.

Simulations show improved efficiency over traditional methods.

Application to accelerometry data reveals insights into physical activity and disease.

Abstract

Modern longitudinal data, for example from wearable devices, measures biological signals on a fixed set of participants at a diverging number of time points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analyzing the cumulatively growing dataset each time new data is collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors across serially collected dependent datasets. The key technique is a decomposition of the extended score function of the quadratic inference function constructed over the cumulative longitudinal data into a sum of summary statistics over data batches. We show how this sum can be recursively updated without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. We prove consistency and asymptotic normality of our streaming estimator as the number of data batches diverges, even as the number of independent participants remains fixed. Simulations highlight the advantages of our approach over traditional statistical methods that assume independence between data batches. Finally, we investigate the relationship between physical activity and several diseases through the analysis of accelerometry data from the National Health and Nutrition Examination Survey.