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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.
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One-Way ANOVA: Equal Sample Sizes01:15

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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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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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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Comparing the Survival Analysis of Two or More Groups01:20

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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...
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Study Design in Statistics01:15

Study Design in Statistics

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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Sample size calculation for before-after experiments with partially overlapping cohorts.

Song Zhang1, Jing Cao2, Chul Ahn3

  • 1Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States.

Contemporary Clinical Trials
|September 30, 2015
PubMed
Summary
This summary is machine-generated.

This study provides a new sample size formula for before-after studies with binary outcomes and mixed data. The formula accounts for missing data, improving sample size estimation for clinical trials.

Keywords:
Before–after studyBinary outcomeClinical trialExperimental designSample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Before-after studies are common in clinical research.
  • Handling incomplete data in longitudinal studies presents challenges.
  • Accurate sample size calculation is crucial for study validity.

Purpose of the Study:

  • To develop a sample size formula for binary outcomes in before-after studies with incomplete data.
  • To provide a method that accounts for missing data using a generalized estimating equation (GEE) approach.
  • To offer a practical tool for researchers designing clinical trials with mixed data types.

Main Methods:

  • Utilized Generalized Estimating Equations (GEE) to derive a closed-form sample size formula.
  • Treated incomplete observations as missing data within a generalized linear model framework.
  • Incorporated key design factors: intervention effect, baseline rate, within-subject correlation, and missing data patterns.

Main Results:

  • A novel sample size formula was derived for before-after studies with binary outcomes and mixed data.
  • The formula effectively incorporates the impact of incomplete observations and various design parameters.
  • Simulation studies confirmed the formula's ability to maintain nominal power and Type I error rates.

Conclusions:

  • The proposed sample size formula offers a robust solution for studies with incomplete longitudinal binary data.
  • This method enhances the accuracy of sample size estimation in before-after clinical trials.
  • The findings support more efficient and reliable study designs in the presence of missing data.