Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Hazard Ratio

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Hazard Rate

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

You might also read

Related Articles

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

Sort by

Same author

Estimating the global burden of mental health disorders associated with atopic dermatitis.

Journal of the European Academy of Dermatology and Venereology : JEADV·2026

Same author

PROCESS: a multimethods protocol to develop principles to operationalise community engagement, equity and sustainability in South Asian Health Research in Canada.

BMJ open·2026

Same author

Combining structured exercise with a low-energy diet to attenuate lean mass loss in South Asian adults living with type 2 diabetes: the COMBINE randomised trial protocol.

BMJ open·2026

Same author

Rationale and design of a parallel randomised trial of a plant-based intensive lifestyle intervention for diabetes remission: The REmission of diabetes using a PlAnt-based weight loss InteRvention (REPAIR) trial.

Diabetes, obesity & metabolism·2026

Same author

Associations of pregnancy complications with paternal cardiovascular risk: a retrospective cohort study.

Journal of epidemiology and community health·2025

Same author

Global burden of major depressive disorder attributable to psoriasis, 2000-2021.

Journal of the European Academy of Dermatology and Venereology : JEADV·2025

Same journal

Trends in Pharmacist-Prescribed Dispensing Records of HIV Pre-Exposure (2020-2025) and Post-Exposure Prophylaxis (2020-2024) in Brazil: A Time Series Analysis.

Pharmacoepidemiology and drug safety·2026

Same journal

French Consumption of Methylphenidate in Primary Care From 2016 to 2023, Impact of Prescribing Policy Changes-A Time-Series Analysis.

Pharmacoepidemiology and drug safety·2026

Same journal

Uptake and Use of Biologic Therapies in Paediatric Immune-Mediated Inflammatory Diseases: An Australian Population-Based Study.

Pharmacoepidemiology and drug safety·2026

Same journal

Comparative Effectiveness of Oral Fluoropyrimidines Versus FOLFOX as Adjuvant Therapy for Stage III Colon Cancer: A Retrospective Cohort Study Using Overlap-Weighted Restricted Mean Survival Time Analysis.

Pharmacoepidemiology and drug safety·2026

Same journal

Association Between EGFR-TKI-Associated Skin Rash and Recorded Mortality in Non-Small Cell Lung Cancer: A Real-World Analysis Accounting for Immortal Time Bias.

Pharmacoepidemiology and drug safety·2026

Same journal

Nationwide Trends in Opioid Consumption in Costa Rica, 2017-2024: Implications for Regulatory Policy and Public Health.

Pharmacoepidemiology and drug safety·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Correcting hazard ratio estimates for outcome misclassification using multiple imputation with internal validation

Jiayi Ni^1,2, Aaron Leong¹, Kaberi Dasgupta^1,3

¹Research Institute of the McGill University Health Centre, Montréal, QC, Canada.

Pharmacoepidemiology and Drug Safety

|May 16, 2017

Summary

This summary is machine-generated.

Multiple imputation corrects outcome misclassification in observational studies using validation data. This method reduces bias in hazard ratio estimates for time-to-event analyses.

Keywords:

diabetes hazard ratio internal validation misclassification multiple imputation statin

More Related Videos

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

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

Published on: January 8, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Mar 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

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

Published on: January 8, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics

Background:

Observational studies using administrative databases can suffer from outcome misclassification.
This bias can affect the accuracy of hazard ratio (HR) estimates in Cox regression models.

Purpose of the Study:

To evaluate a two-step multiple imputation approach for reducing bias in HR estimates.
The method utilizes complementary internal validation data from subsamples.

Main Methods:

Applied a two-step multiple imputation technique to a study of statin-diabetes association in Quebec.
Corrected diabetes status and onset using self-reported data and fasting blood glucose (FBG) levels from validation subsamples.
Assessed performance via simulation, varying key parameters like true HR, sensitivity, specificity, and validation subsample size.

Main Results:

Corrected HR estimates for new onset diabetes among statin users were lower than those from administrative data alone.
Multiple imputation yielded less biased HR estimates and appropriate coverage in simulations for both non-differential and differential misclassification.
Low participation in validation subsamples led to variable corrected HR estimates; bias correction was sometimes offset by uncertainty in event timing.

Conclusions:

Multiple imputation is a valuable tool for correcting outcome misclassification in time-to-event analyses.
The effectiveness of this approach depends on the availability and quality of complementary validation data from subsamples.