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

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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Related Experiment Video

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Sample size and power for cost-effectiveness analysis (part 1).

Henry A Glick1

  • 1Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. hlthsvrs@mail.med.upenn.edu

Pharmacoeconomics
|February 12, 2011
PubMed
Summary
This summary is machine-generated.

This study reviews sample size and power calculations for cost-effectiveness analysis. It details common tables, parameter impacts, and data derivation methods for these crucial statistical estimates.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Sample size and power calculations are fundamental for designing statistically sound studies.
  • Cost-effectiveness analysis (CEA) requires specific methods for sample size and power estimation.
  • Existing literature provides basic formulae, but a comprehensive review is beneficial.

Purpose of the Study:

  • To review established sample size and power formulae for cost-effectiveness analysis.
  • To compare these methods with those used for other continuous variables.
  • To describe common calculation outputs and data derivation strategies.

Main Methods:

  • Literature review of existing sample size and power formulae for CEA.
  • Comparative analysis of CEA methods versus those for continuous variables (e.g., blood pressure, weight).
  • Discussion of sample size/power tables and sensitivity analyses.

Main Results:

  • Established formulae for CEA sample size and power are presented.
  • Similarities and differences between CEA and continuous variable analyses are highlighted.
  • The impact of parameter variations on sample size and power is discussed.

Conclusions:

  • Understanding sample size and power is critical for robust cost-effectiveness analyses.
  • The methods for CEA differ from those for simple continuous outcomes.
  • Guidance is provided on deriving data and interpreting results for sample size calculations in CEA.