Related Experiment Video
Updated: Mar 28, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
11.0K
Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition
Peter B Gilbert1, Erin E Gabriel2, Ying Huang1
1Vaccine Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, U.S.A. ; Department of Biostatistics, University of Washington, Seattle, Washington, 98105, U.S.A.
Journal of Causal Inference
|January 2, 2016
Summary
Evaluating surrogate endpoints in clinical trials is crucial for future research. This study explores causal effect predictiveness criteria, like average causal necessity and sufficiency, for validating surrogate endpoints, ensuring reliable treatment effect inferences.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- Surrogate endpoints in randomized clinical trials aim to predict clinical outcomes, simplifying future data collection.
- Principal stratification framework offers methods to evaluate surrogate endpoint validity using causal inference.
- Existing criteria for surrogate endpoints, including causal effect predictiveness (CEP), require careful examination.
Purpose of the Study:
- To assess criteria for useful surrogate endpoints based on the CEP surface within the principal stratification framework.
- To investigate the relationships between average causal necessity (ACN), average causal sufficiency (ACS), Prentice definition, and consistency criterion.
- To determine the influence of assumptions on treatment effects for surrogate endpoint validation.
Main Methods:
- Utilizing the principal stratification framework and causal effect predictiveness (CEP) surface.
- Analyzing proposed criteria: average causal necessity (ACN), average causal sufficiency (ACS), and Prentice definition.
- Examining the consistency criterion to prevent the 'surrogate paradox'.
Main Results:
- ACN and a strong version of ACS do not generally imply the Prentice definition or consistency, except in specific cases.
- The converse relationship does not hold, except for binary surrogates under certain conditions.
- Principal strata are identifiable in common practical scenarios, enhancing the utility of the framework for effect modification analysis.
Conclusions:
- Assumptions about pre-surrogate treatment effects significantly impact conclusions regarding Prentice definition and consistency.
- The principal stratification framework is valuable for effect modification analysis when principal strata are identifiable.
- Application to a vaccine trial showed ACN and ACS consistent with data, supporting Prentice definition and consistency for an antibody marker.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
529
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,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
529
01:16Stratified Sampling Method
16.1K
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...
To choose a stratified sample, divide the population into groups called strata and then take a...
16.1K
01:25Strategies for Assessing and Addressing Confounding
531
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...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
531
01:13Randomized Experiments
9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
9.3K
01:20Comparing the Survival Analysis of Two or More Groups
704
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...
704
01:15Study Design in Statistics
10.3K
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...
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...
10.3K