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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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.
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...
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...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Sample size calculation for recurrent events data in one-arm studies.

Paola Rebora1, Stefania Galimberti

  • 1Center of Biostatistics for Clinical Epidemiology, Department of Clinical Medicine and Prevention, University of Milano-Bicocca, Monza, Italy. paola.rebora@unimib.it

Pharmaceutical Statistics
|October 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for one-arm trials with recurrent event endpoints, crucial for rare disease research. The formula, based on a mixed Poisson process, ensures reliable evidence for treatment efficacy and safety.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Rare Disease Research

Background:

  • Nonrandomized one-arm trials are essential for rare diseases where traditional trials are infeasible.
  • Accurate sample size determination is critical for the reliability of evidence from these studies.
  • Existing methods for sample size calculation do not address recurrent event endpoints in one-arm trials.

Purpose of the Study:

  • To propose a novel closed-form sample size formula for one-arm trials with recurrent event endpoints.
  • To provide a method for calculating sample sizes that ensures reliable efficacy and safety evidence.
  • To address the unique challenges of sample size calculation in rare disease clinical trials.

Main Methods:

  • Derivation of a closed sample size formula based on a mixed Poisson process.
  • Utilizing the asymptotic distribution of a one-sample robust nonparametric test for recurrent events data.
  • Validation through exhaustive simulation studies to assess robustness and performance.

Main Results:

  • The proposed formula effectively calculates sample sizes for one-arm trials with recurrent event endpoints.
  • Simulation studies confirmed the formula's validity in scenarios with event rate heterogeneity and time-varying treatment effects.
  • The method demonstrated robustness even with slight inaccuracies in the assumed event generation process.

Conclusions:

  • The developed sample size formula provides a sound methodological approach for one-arm trials with recurrent events.
  • This method is particularly valuable for rare diseases, such as ADA-SCID gene therapy trials, where such designs are common.
  • The formula supports the generation of reliable evidence on treatment efficacy and safety in challenging clinical contexts.