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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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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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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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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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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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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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Prior Effective Sample Size When Borrowing on the Treatment Effect Scale.

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This summary is machine-generated.

Prior effective sample size (ESS) is crucial for Bayesian external data borrowing. This study extends the expected local information ratio (ELIR) ESS definition to the treatment effect scale, addressing a key methodological gap for improved trial design.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Bayesian Inference

Background:

  • Bayesian external data borrowing is increasingly used in clinical trials.
  • Accurate prior effective sample size (ESS) is critical for controlling borrowed information.
  • Existing ESS methods primarily focus on borrowing control, not treatment effect scales.

Purpose of the Study:

  • To extend the expected local information ratio (ELIR) ESS definition to the treatment effect scale.
  • To provide a general framework and derive ESS for diverse endpoints and treatment effect measures.
  • To evaluate the predictive consistency property of the proposed ELIR ESS.

Main Methods:

  • Extension of the expected local information ratio (ELIR) ESS definition.
  • Derivation of ESS for various endpoint types and treatment effect measures.
  • Assessment of predictive consistency for different endpoint and treatment effect combinations.

Main Results:

  • The ELIR ESS definition was successfully extended to the treatment effect scale.
  • Formulas for prior ESS were derived for multiple endpoint and treatment effect types.
  • Predictive consistency was preserved only for the difference between two normal endpoints.

Conclusions:

  • The developed methods address the gap in computing prior ESS on the treatment effect scale.
  • The findings highlight the importance of considering endpoint and treatment effect types when applying ELIR ESS.
  • R implementations are available to facilitate the application of these novel ESS methods in practice.