A Joint Model for Unbalanced Nested Repeated Measures with Informative Drop-Out Applied to Ambulatory Blood Pressure Monitoring Data.

Enas M Ghulam,Rhonda D Szczesniak,Raouf S Amin,Jane C Khoury,Eleni-Rosalina Andrinopoulou,Roman Jandarov

doi:10.1155/2022/4452158

Abstract

This study proposes a Bayesian joint model with extended random effects structure that incorporates nested repeated measures and provides simultaneous inference on treatment effects over time and drop-out patterns. The proposed model includes flexible splines to characterize the circadian variation inherent in blood pressure sequences, and we assess the effectiveness of an intervention to resolve pediatric obstructive sleep apnea. We demonstrate that the proposed model and its conventional two-stage counterpart provide similar estimates of nighttime blood pressure but estimates on the mean evolution of daytime blood pressure are discrepant. Our simulation studies tailored to the motivating data suggest reasonable estimation and coverage probabilities for both fixed and random effects. Computational challenges of model implementation are discussed.

Highlights

Hypertension is one of the major causes and most important modifiable risk factors for the prevention of cardiovascular disease
The results show that the joint model and the two-stage approach both performed with excellent accuracy, e.g., mean absolute percentage error (MAPE) for the joint and two-stage models
According to the coverage probability (CP), the results indicate that overall, our model performs adequately under simulated circumstances

Summary

Introduction

Hypertension is one of the major causes and most important modifiable risk factors for the prevention of cardiovascular disease. Sequences of ABPM measurements are often recorded intensively over a period of time, under different medical conditions or occasions on the same subject to assess the variation of these sequences in relation to health outcomes and/or interventions. This allows close study of clinical progression over different monitoring occasions but results in nested repeated measures (NRM) data that require more complex correlation structures [3, 4]. Several studies have found a strong association between obstructive sleep apnea (OSA) and cardiovascular morbidity and mortality [2, 5, 6]. Diagnosis and subsequent treatment of OSA, especially at young ages, are essential in decreasing cardiovascular morbidity and mortality and improving overall prognosis [5, 6]

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Conclusion