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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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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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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...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Sample size recalculation based on the prevalence in a randomized test-treatment study.

Amra Hot1, Norbert Benda2, Patrick M Bossuyt3

  • 1Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Christoph-Probst Weg 1, 20246, Hamburg, Germany. a.hot@uke.de.

BMC Medical Research Methodology
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Adaptive designs with blinded sample size recalculation improve randomized test-treatment studies. This method ensures desired statistical power and controls error rates, unlike fixed designs that risk incorrect sample sizes due to prevalence uncertainties.

Keywords:
Adaptive designPrevalenceSample size recalculationSensitivitySpecificity

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Area of Science:

  • Clinical trial methodology
  • Biostatistics
  • Diagnostic test evaluation

Background:

  • Randomized test-treatment studies assess diagnostic test clinical utility.
  • Sample size calculations are complex, influenced by disease prevalence and other factors.
  • Uncertainties in sample size planning necessitate adaptive adjustments.

Purpose of the Study:

  • To propose and evaluate an adaptive design for randomized test-treatment studies.
  • To compare the performance of an adaptive design with a fixed design.
  • To investigate the impact of prevalence estimation on sample size and study power.

Main Methods:

  • A simulation study was conducted to evaluate the proposed adaptive design.
  • The adaptive design incorporates blinded sample size recalculation based on disease prevalence.
  • Performance metrics include statistical power and type I error rate.

Main Results:

  • The adaptive design achieved the desired statistical power when other parameters were correctly specified.
  • Fixed designs are susceptible to over- or underpowering if prevalence assumptions are incorrect.
  • Both adaptive and fixed designs adequately controlled the empirical type I error rate.

Conclusions:

  • Adaptive designs with blinded sample size recalculation enhance the success probability and efficiency of studies.
  • Consideration of adaptive design is advisable during study planning.
  • Limitations include feasibility, required sample sizes, and prerequisite fulfillment.