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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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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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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.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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What are Estimates?01:06

What are Estimates?

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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...
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Cell Size01:22

Cell Size

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Cell sizes vary widely among and within organisms. Bacterial cells range between 1-10 micrometers (μm)and are considerably smaller than most eukaryotic cells. The smallest bacteria are 0.1 μm in diameter—about a thousand times smaller than eukaryotic cells, which typically range from 10-100 μm.
Surface Area
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Related Experiment Video

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Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
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Sample size estimation for case-crossover studies.

Sai Dharmarajan1, Joo-Yeon Lee1, Rima Izem1

  • 1Office of Biostatistics, Division of Biometrics VII, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland.

Statistics in Medicine
|November 7, 2018
PubMed
Summary
This summary is machine-generated.

Current sample size formulas for case-crossover studies, used for medical product safety, may underestimate needs. This research reveals their inadequacy and proposes improved methods for accurate sample size estimation in these observational studies.

Keywords:
case-crossovercorrelation in exposurematched case-controlsample size formula

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Case-crossover designs are valuable observational studies for assessing medical product safety post-approval.
  • Their self-controlled nature offers robust control for confounding factors.
  • However, intra-subject correlation between case and control periods presents unique challenges.

Purpose of the Study:

  • To evaluate the adequacy of existing sample size estimation methods for case-crossover studies.
  • To identify limitations in current formulas, such as Dupont's formula.
  • To propose novel, more accurate sample size calculation methods for this study design.

Main Methods:

  • The study involved simulations to determine true sample size requirements across various parameter spaces.
  • Mathematical derivations were used to analyze and explain the shortcomings of existing sample size formulas.
  • Two new sample size estimation methodologies were developed and presented.

Main Results:

  • Existing methods, including Dupont's formula, were found to frequently underestimate the required sample size for case-crossover studies.
  • Simulations confirmed the inadequacy of current approaches for accurate sample size determination.
  • The proposed new methods demonstrated improved accuracy in estimating the true sample size.

Conclusions:

  • Current sample size estimation methods for case-crossover studies are often inadequate and lead to underestimation.
  • Accurate sample size calculation is crucial for the statistical power and validity of post-market safety studies.
  • The newly proposed methods offer a more reliable approach to sample size estimation in case-crossover research.