ArticleStatistics in medicine2019
Instrumental variable approach to estimating the scalar-on-function regression model with measurement error with application to energy expenditure assessment in childhood obesity.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.Statistics in medicine · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.Statistics in medicine · 2024Article
- A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.Statistics in medicine · 2024Article
- Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.eLife · 2024Review
- Adjusting for bias due to measurement error in functional quantile regression models with error-prone functional and scalar covariates.Biostatistics & epidemiology · 2024Article
- Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Instrumental variable estimation for functional concurrent regression models.Journal of applied statistics · 2024Article
- Modeling approaches for assessing device-based measures of energy expenditure in school-based studies of body weight status.Frontiers in applied mathematics and statistics · 2024Article
- PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS.Statistica Sinica · 2023Article
- A function-based approach to model the measurement error in wearable devices.Statistics in medicine · 2022Article
- Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.Biostatistics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Wearable device technology allows continuous monitoring of biological markers and thereby enables study of time-dependent relationships. For example, in this paper, we are interested in the impact of daily energy expenditure over a period of time on subsequent progression toward obesity among children. Data from these devices appear as either sparsely or densely observed functional data and methods of functional regression are often used for their statistical analyses. We study the scalar-on-function regression model with imprecisely measured values of the predictor function. In this setting, we have a scalar-valued response and a function-valued covariate that are both collected at a single time period. We propose a generalized method of moments-based approach for estimation, while an instrumental variable belonging in the same time space as the imprecisely measured covariate is used for model identification. Additionally, no distributional assumptions regarding the measurement errors are assumed, while complex covariance structures are allowed for the measurement errors in the implementation of our proposed methods. We demonstrate that our proposed estimator is L
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.