Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Transformation Discriminant Analysis for Constructing Optimal Biomarker Combinations.

Statistics in medicine·2026
Same author

Likelihood-based modeling of covariate-specific time-dependent receiver operating characteristic curves.

Statistical methods in medical research·2026
Same author

Rejoinder to the discussion on ''Nonparanormal Adjusted Marginal Inference''.

Biometrics·2026
Same author

Nonparanormal adjusted marginal inference.

Biometrics·2026
Same author

Smooth transformation models for survival analysis: A tutorial using R.

Statistical methods in medical research·2026
Same author

Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models.

Statistics in medicine·2026

Related Experiment Video

Updated: May 9, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Boosting structured additive quantile regression for longitudinal childhood obesity data.

Nora Fenske1, Ludwig Fahrmeir, Torsten Hothorn

  • 1Institut für Statistik, Ludwigs-Maximilians-Universität München, Ludwigstr. 33, München 80539, Germany. nora.fenske@stat.uni-muenchen.de

The International Journal of Biostatistics
|July 30, 2013
PubMed
Summary

This study introduces a new statistical method to analyze childhood obesity risk factors using longitudinal data. The approach helps understand individual body mass index (BMI) changes and related factors up to age 10.

More Related Videos

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Related Experiment Videos

Last Updated: May 9, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Area of Science:

  • Statistics
  • Public Health
  • Pediatrics

Background:

  • Childhood obesity is a significant public health concern.
  • Understanding risk factors is crucial for effective interventions.
  • Longitudinal data provides valuable insights into developmental trends.

Purpose of the Study:

  • To introduce a novel statistical method for analyzing longitudinal data on childhood body mass index (BMI).
  • To investigate risk factors associated with BMI trajectories in children.
  • To provide a flexible modeling approach for complex, individual-specific effects.

Main Methods:

  • Utilized a German longitudinal study with 2,226 children and up to ten BMI measurements from birth to age 10.
  • Developed and applied a boosting of structured additive quantile regression (BSQ) approach.
  • Employed a component-wise functional gradient descent boosting algorithm for penalized estimation.

Main Results:

  • The BSQ model effectively estimates nonlinear age curves for upper BMI quantiles at both population and individual levels.
  • Identified age-varying effects of categorical risk factors on BMI.
  • Demonstrated the model's ability to handle complex effects, including individual-specific intercepts and slopes.

Conclusions:

  • The proposed boosting of structured additive quantile regression offers a powerful, distribution-free method for analyzing complex longitudinal health data.
  • This approach enhances understanding of childhood obesity risk factors and individual BMI development.
  • The method is a valuable quantile regression analog to Gaussian additive mixed models for public health research.