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

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:
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.
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by
Same author

Perceived life events and presentation of depression symptoms: An examination of the pathoplasticity model.

Journal of affective disorders·2026
Same author

Preliminary Psychometric Findings and Feasibility of the Diagnostic Interview for Adolescents and Adults With Intellectual Disability (DIAAID): An Interview Schedule of Mental Disorders.

Journal of applied research in intellectual disabilities : JARID·2026
Same author

#EnufSnuff.TXT-FirstResponder: a pilot randomized controlled trial of a text message intervention for smokeless tobacco cessation among First Responders.

Frontiers in public health·2026
Same author

Personality and presentation of depression symptoms: A preliminary examination of the pathoplasticity model.

Clinical psychological science : a journal of the Association for Psychological Science·2025
Same author

Prevalence and predictors of chronic disease among rural and medically underserved populations using smokeless tobacco.

Frontiers in public health·2025
Same author

The Comprehensive Adaptive Multisite Prevention of University Student Suicide Trial: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2025

Related Experiment Video

Updated: Jun 16, 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

Modeling missing binary outcome data in a successful web-based smokeless tobacco cessation program.

Keith Smolkowski1, Brian G Danaher, John R Seeley

  • 1Oregon Research Institute, 1715 Franklin Boulevard, Eugene, OR 97403, USA. keiths@ori.org

Addiction (Abingdon, England)
|February 13, 2010
PubMed
Summary

Different methods for handling missing data in tobacco cessation trials significantly impact results. Choosing the right imputation method is crucial for accurate treatment effect estimation in intent-to-treat analyses.

Related Experiment Videos

Last Updated: Jun 16, 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

Area of Science:

  • Biostatistics
  • Public Health
  • Digital Health Interventions

Background:

  • Web-based interventions are increasingly used for tobacco cessation.
  • High attrition rates in online studies lead to missing outcome data.
  • Accurate analysis of missing data is essential for reliable results.

Purpose of the Study:

  • To evaluate various imputation methods for missing binary outcomes in a web-based smoking cessation trial.
  • To assess the impact of different missing-data handling techniques on treatment effect estimates.

Main Methods:

  • Secondary analysis of the ChewFree randomized controlled trial (N=2523).
  • Comparison of an enhanced web-based intervention versus a basic information control.
  • Measurement of point-prevalence tobacco abstinence at 3- and 6-month follow-ups.

Main Results:

  • Different imputation methods produced varying estimates of treatment effect size and standard errors.
  • The choice of imputation significantly influenced the statistical significance of the findings.
  • Substantial attrition necessitates careful consideration of missing-data approaches for intent-to-treat analyses.

Conclusions:

  • Imputation model selection critically affects the estimated treatment effect and its statistical significance.
  • Without external information, imputation methods may overestimate treatment efficacy.
  • Multiple imputation methods, including sensitivity analyses, are recommended for missing binary outcomes.