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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...
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Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

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Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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Bayesian sample-size determination for two independent Poisson rates.

Austin L Hand1, James D Stamey, Dean M Young

  • 1Department of Statistics, Baylor University, Waco, TX, USA. Austin_Hand@baylor.edu

Computer Methods and Programs in Biomedicine
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces new Bayesian methods for determining sample sizes in clinical trials, focusing on comparing two Poisson rates. These flexible approaches offer accurate inferences and are implemented using R functions.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Clinical trials face significant cost and time constraints, necessitating efficient sample size determination.
  • Frequentist methods are common, but Bayesian approaches offer greater flexibility and interpretability for sample size calculations.
  • Previous Bayesian methods focused on single Poisson rates; this study addresses the comparison of two Poisson rates.

Purpose of the Study:

  • To extend Bayesian sample-size determination methods for comparing two Poisson rates in a clinical trial setting.
  • To develop novel Bayesian methods for hypothesis testing sample size determination.
  • To provide practical tools for implementing these Bayesian sample size methods.

Main Methods:

  • Development of Bayesian sample-size determination methods for comparing two Poisson rates.
  • Implementation of functions in R for determining conjugate gamma prior parameters.
  • Calculation of sample sizes using the average length criterion and average power methods within a Bayesian framework.

Main Results:

  • Successful extension of Bayesian sample-size determination to the comparison of two Poisson rates.
  • Creation of R functions to facilitate the application of these novel Bayesian methods.
  • Demonstration of the methods' utility through two examples using clinical data.

Conclusions:

  • Bayesian sample-size determination offers a flexible and interpretable alternative for clinical trial design.
  • The developed methods and R functions provide valuable tools for researchers comparing two Poisson rates.
  • These approaches enhance the accuracy and efficiency of sample size planning in clinical research.