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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Introduction To Survival Analysis01:18

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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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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Sample Size Calculations for Time-Averaged Difference of Longitudinal Binary Outcomes.

Ying Lou1, Jing Cao1, Song Zhang2

  • 1Department of Statistical Science, Southern Methodist University, Dallas, TX.

Communications in Statistics: Theory and Methods
|June 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for clinical trials with repeated binary outcomes. It accounts for complex missing data patterns and correlation structures, improving accuracy for time-averaged difference analysis.

Keywords:
GEEbinarymixture of missing patternsrepeated measurementssample sizetime-averaged differences

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Repeated measurements in clinical trials require specific statistical approaches.
  • Comparing treatment effects often involves analyzing the rate of change or time-averaged difference (TAD).
  • Existing sample size calculations for TAD are limited, especially with complex data structures.

Purpose of the Study:

  • To develop and investigate a sample size calculation method for comparing time-averaged responses in longitudinal binary outcome studies.
  • To address limitations in existing methods by incorporating heterogeneous correlation structures and missing data.
  • To provide a flexible formula applicable to real-world clinical trial scenarios.

Main Methods:

  • Utilized the Generalized Estimating Equations (GEE) approach to derive a closed-form sample size formula.
  • The formula is designed to accommodate arbitrary missing data patterns and correlation structures.
  • Investigated the handling of a mixture of missing patterns, common in clinical trials.

Main Results:

  • The derived GEE-based sample size formula is flexible for various missing data patterns and correlations.
  • Demonstrated the formula's ability to handle a mixture of missing patterns.
  • Simulation studies confirmed that the nominal power and type I error rates are well-maintained across diverse design parameters.

Conclusions:

  • This study presents the first sample size calculation method for time-averaged difference in longitudinal binary outcomes that explicitly considers a mixture of missing data patterns.
  • The proposed method offers a practical and robust solution for sample size estimation in complex clinical trial settings.
  • The findings enhance the statistical rigor for designing clinical trials with longitudinal binary data and missing observations.