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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.2K
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:
1.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

337
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
337
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

5.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
5.7K
Observational Studies01:11

Observational Studies

11.5K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
11.5K

You might also read

Related Articles

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

Sort by
Same author

Estimating clinical trial hazard functions.

Clinical trials (London, England)·2026
Same author

Behavioral pharmacology study to inform the switching potential and abuse liability of tobacco-flavored and menthol-flavored pod-based electronic nicotine delivery systems with 5% nicotine concentration.

Experimental and clinical psychopharmacology·2026
Same author

A Note on the Complementary Mixture Pareto II Distribution.

Communications in statistics: theory and methods·2026
Same author

US adults' complete switching away from cigarettes by menthol- and tobacco-flavored ENDS and by menthol cigarette preference: testing robustness to missing data.

Internal and emergency medicine·2026
Same author

Factors influencing the selection of an SGLT2i vs. a GLP-1RA as cardioprotective agent in patients with type 2 diabetes.

Frontiers in cardiovascular medicine·2025
Same author

Repeated point-prevalence of switching away from smoking after electronic nicotine delivery systems (ENDS) purchase.

Harm reduction journal·2025

Related Experiment Video

Updated: Apr 10, 2026

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K

Model-based imputation of latent cigarette counts using data from a calibration study.

Sandra D Griffith1, Saul Shiffman2, Yimei Li3

  • 1Flatiron Health.

International Journal of Methods in Psychiatric Research
|June 18, 2015
PubMed
Summary

This study improves cigarette count accuracy in smoking research using a novel calibration method. It enhances data analysis efficiency and reliability for better understanding smoking reduction interventions.

Keywords:
addictionmethodologynicotinestatisticstobacco

More Related Videos

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.9K
Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice
10:37

Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice

Published on: January 16, 2015

13.8K

Related Experiment Videos

Last Updated: Apr 10, 2026

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.9K
Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice
10:37

Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice

Published on: January 16, 2015

13.8K

Area of Science:

  • Addiction Research
  • Biostatistics
  • Psychopharmacology

Background:

  • Dichotomous abstinence measures in smoking studies lack detailed analysis potential.
  • Retrospective recall of daily cigarette consumption (e.g., Timeline Followback, TLFB) suffers from measurement error and heaping.
  • Ecological Momentary Assessment (EMA) provides precise, instantaneous smoking data but is resource-intensive.

Purpose of the Study:

  • To develop and apply a statistical method for imputing precise daily cigarette counts using less accurate retrospective data.
  • To enhance the efficiency and reliability of analyzing smoking cessation trial data.
  • To validate the method's applicability to other self-reported count data with available calibration samples.

Main Methods:

  • A doubly-coded dataset with TLFB and EMA measurements was used as a calibration dataset.
  • A predictive model was developed to estimate EMA cigarette counts based on TLFB data and baseline factors.
  • Multiple imputation techniques were employed to generate precise cigarette counts for a bupropion trial, accounting for repeated measures.
  • Longitudinal data analysis incorporated random subject effects and zero-inflation to analyze imputed data.

Main Results:

  • Both raw and imputed data demonstrated a significant bupropion effect in reducing non-abstinence odds and cigarette consumption among non-abstainers.
  • The multiply imputed data offered significant efficiency gains in the analysis.
  • The proposed method successfully addressed measurement error and heaping issues in self-reported smoking data.

Conclusions:

  • This novel imputation method enables robust analysis of daily cigarette consumption data previously considered unreliable.
  • The approach enhances statistical power and provides more detailed insights into smoking behavior and intervention effects.
  • The methodology is adaptable for various self-reported count data where calibration datasets are available.