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

Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Comparing the Survival Analysis of Two or More Groups01:20

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 Cox...

You might also read

Related Articles

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

Sort by
Same author

Variable selection-combined causal mediation analysis for continuous treatments with application to large-dimensional biomedical data.

PLoS computational biology·2026
Same author

Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.

One health (Amsterdam, Netherlands)·2026
Same author

Relevance of Kidney-Metabolic Multimorbidity Pattern to Metabolic Health and Mortality Among Elderly Inpatients in China.

Food science & nutrition·2026
Same author

Angiography-derived fractional flow reserve versus coronary angiography to guide coronary artery bypass grafting in patients undergoing surgical valve procedures with concomitant coronary artery disease in China (FAVOR IV-QVAS): a multicentre, triple-blind, randomised trial.

Lancet (London, England)·2026
Same author

Efficacy and safety of ruxolitinib for graft-versus-host disease prophylaxis in patients with aplastic anemia undergoing PBSC-only allogeneic stem cell transplantation: a prospective phase II study.

Experimental hematology & oncology·2026
Same author

Association between cholesterol and liver injury risk in solid cancer patients treated with PD-1 inhibitors: evidence from two cohort studies.

BMC cancer·2026

Related Experiment Video

Updated: Jun 6, 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

Partial linear inference for a 2-stage outcome-dependent sampling design with a continuous outcome.

Guoyou Qin1, Haibo Zhou

  • 1Department of Biostatistics, School of Public Health and Key Laboratory of Public Health Safety, Ministry of Education of China, Fudan University, Shanghai 200032, People's Republic of China.

Biostatistics (Oxford, England)
|December 16, 2010
PubMed
Summary

Outcome-dependent sampling (ODS) offers cost efficiency by linking exposure observation to outcomes. This study introduces a new penalized likelihood method for partial linear models under a 2-stage ODS design, demonstrating its effectiveness through simulations and a real-world environmental study.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jun 6, 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

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Biostatistics
  • Environmental Health
  • Statistical Modeling

Background:

  • Outcome-dependent sampling (ODS) designs are recognized for their cost-efficiency in observational studies.
  • These designs allow the observation of exposure variables to be influenced by the study's outcome.
  • Partial linear models are frequently used in statistical analysis, particularly in environmental health research.

Purpose of the Study:

  • To propose a novel statistical inference method for partial linear models within a 2-stage outcome-dependent sampling framework.
  • To address the complexities of continuous outcomes in conjunction with ODS designs.
  • To provide a robust estimation technique for analyzing data collected via ODS.

Main Methods:

  • Development of an estimated penalized likelihood method for statistical inference.
  • Application of the method to a partial linear model under a 2-stage ODS setting.
  • Asymptotic properties of the proposed estimator were rigorously developed.

Main Results:

  • Simulation studies were conducted to evaluate the performance of the proposed estimator.
  • The method demonstrated effective performance in the simulated scenarios.
  • The proposed statistical inference method was illustrated using a real environmental study dataset.

Conclusions:

  • The estimated penalized likelihood method provides a viable approach for statistical inference in partial linear models with 2-stage ODS.
  • The method is shown to be effective through both theoretical development and empirical validation.
  • This research contributes a valuable tool for analyzing environmental health data collected using cost-efficient ODS designs.