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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Sample size determination for cost-effectiveness trials.

Andrew R Willan1

  • 1SickKids Research Institute and University of Toronto, Toronto, ON, Canada. andy@andywillan.com

Pharmacoeconomics
|October 13, 2011
PubMed
Summary
This summary is machine-generated.

Determining sample size for cost-effectiveness studies is crucial. This research reviews traditional methods and introduces a Bayesian approach using decision theory for optimal sample size determination, enhancing study design and decision-making.

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Area of Science:

  • Health Economics
  • Decision Science
  • Biostatistics

Background:

  • Cost-effectiveness studies (CES) are vital for healthcare decision-making.
  • Traditional sample size methods for CES often rely on arbitrary statistical thresholds.
  • There's a need for more robust methods that align with decision-making principles.

Purpose of the Study:

  • To review and illustrate methods for determining sample size in cost-effectiveness studies.
  • To present traditional hypothesis testing approaches for incremental cost-effectiveness ratio and incremental net benefit (INB).
  • To introduce a full Bayesian decision-theoretic approach for sample size determination based on the value of information.

Main Methods:

  • Review of traditional statistical methods for sample size calculation in CES.
  • Application of hypothesis testing and power calculations for incremental cost-effectiveness ratio and INB.
  • Development and illustration of a Bayesian decision-theoretic framework for optimal sample size determination for INB.

Main Results:

  • Traditional methods for sample size determination in CES are presented.
  • A novel Bayesian approach for sample size calculation in CES is detailed, focusing on INB.
  • The Bayesian approach offers a more consistent framework for decision-making by incorporating the value of information.

Conclusions:

  • The Bayesian approach provides a theoretically sounder method for sample size determination in CES.
  • This approach addresses limitations of traditional methods concerning error probabilities and clinical significance.
  • Decision theory-informed sample size calculations enhance the optimal use of information for healthcare intervention decisions.