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Related Concept Videos

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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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.
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Censoring Survival Data01:09

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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...
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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.
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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Sample Size Calculation When Planning Clinical Trials with Intercurrent Events.

Yixin Fang1, Man Jin2

  • 1AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA. yixin.fang@abbvie.com.

Therapeutic Innovation & Regulatory Science
|April 6, 2021
PubMed
Summary
This summary is machine-generated.

Clinical trial sample size calculations must align with the study

Keywords:
Clinical trialsEstimandIntercurrent eventsMissing dataMissing not at randomSample sizeSensitivity analysis

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Pharmaceutical research

Background:

  • Missing data in clinical trials are consequences of intercurrent events, not events themselves.
  • Conventional sample size calculation methods often fail to account for missing data and are not estimand-oriented.
  • Alignment with estimands is crucial for robust clinical trial design.

Purpose of the Study:

  • To develop estimand-aligned methods for sample size calculation in clinical trials.
  • To address the challenge of intercurrent events and missing data in trial planning.
  • To provide a framework for sample size adjustments that reflect chosen strategies for handling intercurrent events.

Main Methods:

  • Review and extend five strategies for handling intercurrent events as outlined by ICH E9(R1).
  • Develop five basic approaches for sample size calculation, each aligned with a specific strategy for intercurrent events.
  • Extend these approaches to accommodate combinations of multiple strategies for dealing with intercurrent events.

Main Results:

  • Proposed methods ensure sample size calculations are aligned with the chosen estimand and strategy for intercurrent events.
  • The approaches provide a structured way to adjust sample size based on expected missing data rates and handling strategies.
  • Extended methods accommodate complex scenarios involving multiple strategies for intercurrent events.

Conclusions:

  • The presented methods facilitate accurate sample size calculations in clinical trials with intercurrent events.
  • Alignment with estimands and ICH E9(R1) strategies enhances the reliability of clinical trial planning.
  • These estimand-oriented approaches improve the statistical rigor of sample size determination.