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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.0K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

11.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
11.3K
Time Course of Drug Effect01:14

Time Course of Drug Effect

2.4K
The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
2.4K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

510
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
510
Time-Series Graph00:54

Time-Series Graph

4.7K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.7K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

371
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...
371

You might also read

Related Articles

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

Sort by
Same author

Survival Outcomes of a Smoking Cessation Treatment Program After Diagnosis of Bladder Cancer.

European urology oncology·2026
Same author

Emotion regulation or dual task? Dissociation of neural and behavioral measures.

bioRxiv : the preprint server for biology·2026
Same author

Personalizing smoking cessation pharmacotherapy using neuroaffective reactivity profiles: A randomized controlled trial.

Addiction (Abingdon, England)·2026
Same author

Implementation of a hybrid lung health program for Northeast Texas: study protocol.

Implementation science communications·2026
Same author

High- and low-dose topiramate for the treatment of persons with alcohol use disorder who smoke cigarettes: A randomized control trial.

Alcohol, clinical & experimental research·2025
Same author

Standardized research electronic cigarette acceptability among adult men and women who smoke combustible cigarettes.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors·2025

Related Experiment Video

Updated: Nov 7, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.4K

Python Package abstcal: An Open-Source Tool for Calculating Abstinence From Timeline Followback Data.

Yong Cui1, Jason D Robinson1, Rudel E Rymer1

  • 1Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX.

Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco
|April 29, 2021
PubMed
Summary
This summary is machine-generated.

A new open-source Python package, abstcal, standardizes the calculation of smoking abstinence using timeline followback (TLFB) data. This tool improves data rigor and reproducibility in smoking cessation research.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.7K

Related Experiment Videos

Last Updated: Nov 7, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.4K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.7K

Area of Science:

  • Addiction Research
  • Clinical Trials Methodology
  • Computational Biology

Background:

  • Timeline Followback (TLFB) interviews are standard for tracking smoking in cessation trials.
  • Current methods for calculating abstinence from TLFB data lack standardization, leading to variability and inefficiency.
  • Researchers often develop custom tools, consuming time and resources.

Purpose of the Study:

  • To develop a novel, open-source Python package for calculating smoking abstinence from TLFB data.
  • To address the need for standardized, reliable, and accessible abstinence calculation tools in addiction research.
  • To improve the rigor and reproducibility of smoking cessation studies.

Main Methods:

  • Developed the 'abstcal' Python package for TLFB data analysis.
  • Incorporated features for data verification, outlier detection, and missing data imputation.
  • Enabled calculation of various abstinence definitions (continuous, point-prevalence, prolonged) and integration of biochemical verification data.

Main Results:

  • The abstcal package accurately calculates abstinence, verified with data from a clinical smoking cessation study.
  • The package is available as a free, user-friendly web application, enhancing accessibility for researchers without coding expertise.
  • abstcal provides a standardized and validated tool for abstinence calculation.

Conclusions:

  • The abstcal package offers a reliable, open-source solution for calculating smoking abstinence from TLFB data.
  • Its availability as a web app lowers barriers to adoption for researchers.
  • Standardizing TLFB-based abstinence calculation with abstcal is expected to enhance the quality of smoking and addiction research.