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Strategies for Assessing and Addressing Confounding01:25

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...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Framing Effects03:26

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in different ways based on the...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

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Related Experiment Videos

Interpreting the Estimand Framework From a Causal Inference Perspective.

Jinghong Zeng1,2

  • 1Department of Statistics, University of Auckland, 38 Princes Street, Auckland, 1010, New Zealand, 86 15992428924.

Jmirx Med
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

The estimand framework clarifies treatment effects in clinical trials. This study interprets it using causal inference, showing how intercurrent events mediate effects and strengthening the framework's foundation.

Keywords:
causal inferenceclinical trialestimandintercurrent eventtreatment effect

Related Experiment Videos

Area of Science:

  • Pharmaceutical Sciences
  • Biostatistics
  • Clinical Trials

Background:

  • The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) published the estimand framework in 2019 to standardize treatment effect definitions in clinical trials.
  • The estimand framework defines an estimand using five attributes: treatments, variables, target populations, population-level summaries, and intercurrent events.
  • Intercurrent events are handled using strategies like treatment policy, hypothetical, composite variable, while-on-treatment, and principal stratum.

Purpose of the Study:

  • To interpret the ICH estimand framework through the lens of causal inference.
  • To elucidate the distinctions between estimands and genuine causal treatment effects.
  • To propose a novel method for integrating causal inference principles into the estimand framework.

Main Methods:

  • Utilized a causal inference framework based on potential outcomes to define individual treatment effects (ITE) and average treatment effects (ATE).
  • Compared the statistical presentation of ATE with the attributes of an estimand.
  • Proposed mapping estimand attributes onto the statistical presentation of ATE, with intercurrent events acting as mediation mechanisms.

Main Results:

  • The statistical presentation of ATE is not equivalent to an estimand, primarily due to the handling of intercurrent events.
  • Intercurrent events influence the statistical presentation of ATE by modifying treatments, variables, and target populations.
  • The proposed mapping provides a novel way to integrate causal inference into the estimand framework, enhancing its theoretical foundation.

Conclusions:

  • Interpreting the estimand framework using causal inference offers a stronger theoretical basis for its application in clinical trials.
  • This causal perspective is valuable for both academic and pharmaceutical industry clinical trials.
  • The insights may also benefit observational studies seeking to understand causal inference theories.