Fast Univariate Inference for Longitudinal Functional Models

Cui, Erjia; Leroux, Andrew; Smirnova, Ekaterina; Crainiceanu, Ciprian M.

doi:10.1080/10618600.2021.1950006

Cited by 27 publications

(48 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider two competing methods for estimation and inference of model ( 1): fast univariate inference (FUI) (Cui et al, 2022) and functional additive mixed model (FAMM) (Scheipl et al, 2015). The two approaches are philosophically different.…”

Section: Methodsmentioning

confidence: 99%

“…Simulating from the multivariate normal distribution may be difficult when the dimension of the functional domain is very large. In this situation, Cui et al (2022) propose to use principal component decomposition of the B × K-dimensional matrix obtained by row binding the estimators { β(b) r }. This is not necessary in our application as the dimension of the covariance operator is 84 × 84.…”

Section: Estimation and Inference With Fuimentioning

confidence: 99%

“…Moreover, this approach allows for more flexible modeling of the covariance including latent subgroups (e.g., when certain subgroups have a different dependence structure). While FUI is a very powerful approach to inference, the original paper (Cui et al, 2022) does not provide (1) an assessment of the method in the presence of withinperiod correlations (which is the case for our HYPNOS data); (2) a one-number summary (e.g., p-value) for testing the null hypotheses about the functional effects, β r (•).…”

Section: Estimation and Inference With Fuimentioning

confidence: 99%

“…Our goal is to conduct inference on the HYPNOS data using computationally feasible methods that account for its multilevel functional structure and heterogeneity. We have identified only one method, the recently published Fast Univariate Inference (FUI) (Cui et al, 2022), that can be adapted to achieve all of these goals. We contrast this approach with the traditional functional additive mixed models framework (FAMM) (Scheipl et al, 2015) as implemented in refund R package (Goldsmith et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A case study of glucose levels during sleep using fast function on scalar regression inference

Sergazinov¹,

Leroux²,

Cui³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating study contains CGM data during sleep for 174 study participants with type II diabetes mellitus measured at a 5-minute frequency for an average of 10 nights. We aim to quantify the effects of diabetes medications and sleep apnea severity on glucose levels. Statistically, this is an inference question about the association between scalar covariates and functional responses. However, many characteristics of the data make analyses difficult, including (1) non-stationary within-day patterns; (2) substantial between-day heterogeneity, non-Gaussianity, and outliers; 3) large dimensionality due to the number of study participants, sleep periods, and time points. We evaluate and compare two methods: fast univariate inference (FUI) and functional additive mixed models (FAMM). We introduce a new approach for calculating p-values for testing a global null effect of covariates using FUI, and provide practical guidelines for speeding up FAMM computations, making it feasible for our data. While FUI and FAMM are philosophically different, they lead to similar point estimators in our study. In contrast to FAMM, FUI is fast, accounts for within-day correlations, and enables the construction of joint confidence intervals. Our analyses reveal that: (1) biguanide medication and sleep apnea severity significantly affect glucose trajectories during sleep, and (2) the estimated effects are time-invariant.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Estimation and Inference With Fuimentioning

confidence: 99%

Section: Estimation and Inference With Fuimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A case study of glucose levels during sleep using fast function on scalar regression inference

Sergazinov¹,

Leroux²,

Cui³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To add more flexible to our model, our approach uses a truncated Dirichlet process mixture (tDPM) of GAL as a likelihood for the responses. Subsequently, we consider two approaches to correct for measurement errors: a joint model based approach, similar to our previous work , and a fast regression calibration approach motivated by the work of Cui et al (2022a). The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Semi-Parametric Scalar-On-Function Quantile Regression with Measurement Error using the GAL

Zoh¹,

Yu²,

Tekwe³

2023

Preprint

View full text Add to dashboard Cite

Quantile regression provides a consistent approach to investigating the association between covariates and various aspects of the distribution of the response beyond the mean. When the regression covariates are measured with errors, measurement error (ME) adjustment steps are needed for valid inference. This is true for both scalar and functional covariates. Here, we propose extending the Bayesian measurement error and Bayesian quantile regression literature to allow for available covariates prone to potential complex measurement errors. Our approach uses the Generalized Asymmetric Laplace (GAL) distribution as a working likelihood. The family of GAL distribution has recently emerged as a more flexible distribution family in the Bayesian quantile regression modeling compared to their Asymmetric Laplace (AL) counterpart.We then compared and contrasted two approaches in our ME-adjusted steps through a battery of simulation scenarios. Finally, we apply our approach to the analysis of an NHANES dataset 2013-2014 to model quantiles of Body mass index (BMI) as a function of minute-level devicebased physical activity in a cohort of an adult 50 years and above..

show abstract

A semiparametric risk score for physical activity

Cui

Thompson

Carroll

et al. 2021

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modeling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey and quantify nonlinear and interpretable shapes of score components for all-cause mortality.

show abstract

Fast Univariate Inference for Longitudinal Functional Models

Cited by 27 publications

References 38 publications

A case study of glucose levels during sleep using fast function on scalar regression inference

A case study of glucose levels during sleep using fast function on scalar regression inference

A Bayesian Semi-Parametric Scalar-On-Function Quantile Regression with Measurement Error using the GAL

A semiparametric risk score for physical activity

Contact Info

Product

Resources

About