What we want versus what we can get: a closer look at failure time endpoints for cardiovascular studies

Rui Song1, Thomas D Cook, Michael R Kosorok

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, USA. rsong@bios.unc.edu

Insights

This review examines primary endpoints in clinical trials, suggesting a recurrent composite endpoint as a valuable alternative to traditional all-cause mortality or composite endpoints. Further clarification on composite endpoint usage is recommended for trialists.

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • All-cause mortality is a common primary endpoint in clinical trials.
  • Composite endpoints are frequently used but can present interpretation challenges.

Purpose of the Study:

  • To review the current use of all-cause mortality and composite endpoints.
  • To propose a recurrent composite endpoint as a novel primary endpoint alternative.
  • To provide guidance for clinical trialists and practitioners.

Main Methods:

  • Literature review of primary endpoint selection in clinical trials.
  • Conceptual proposal of a recurrent composite endpoint.
  • Discussion of practical implications for trial design and analysis.

Main Results:

  • Current practices regarding primary endpoints require clarification.
  • Recurrent composite endpoints offer a potential improvement for specific trial designs.
  • Guidance is offered to enhance the rigor of endpoint selection.

Conclusions:

  • The selection and interpretation of primary endpoints, particularly composite endpoints, need further standardization.
  • Recurrent composite endpoints represent a promising area for future research and application in clinical trials.
  • Clearer guidelines are essential for optimizing the design and reporting of clinical trials.

Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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, controlled...
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.
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.