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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
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...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

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An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A study on confidence intervals for incremental cost-effectiveness ratios.

Hongkun Wang1, Hongwei Zhao

  • 1Division of Biostatistics and Epidemiology, University of Virginia, P.O. Box 800717, Charlottesville, Virginia 22908, USA. hw3r@virginia.edu

Biometrical Journal. Biometrische Zeitschrift
|July 30, 2008
PubMed
Summary
This summary is machine-generated.

This study compares methods for calculating confidence intervals for the incremental cost-effectiveness ratio (ICER), crucial for health policy. A new bootstrapping approach is proposed for non-significant health effects, improving upon existing methods for cost-effectiveness analysis.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Health Policy

Background:

  • The incremental cost-effectiveness ratio (ICER) is vital for comparing therapy costs and health benefits in health policy and economics.
  • Skewed cost and ICER distributions necessitate robust methods for confidence interval estimation.
  • Existing parametric and nonparametric methods for ICER confidence intervals face challenges, especially with censored data.

Purpose of the Study:

  • To examine and compare the finite sample performance of various confidence interval methods for ICERs.
  • To propose a novel bootstrapping approach for situations with non-significant health effects.
  • To identify efficient methods for constructing confidence intervals and extend them to censored data.

Main Methods:

  • Simulation studies were conducted to evaluate the finite sample performance of different ICER confidence interval approaches.
  • A new bootstrapping method was developed to enhance the bootstrap percentile method for non-significant health effects.
  • The study extends efficient confidence interval construction techniques to handle censored data.

Main Results:

  • Simulation results will identify the most efficient methods for constructing ICER confidence intervals.
  • The proposed bootstrapping approach is expected to improve upon existing methods for non-significant health effects.
  • The study provides a framework for applying these methods to real-world data, including censored cases.

Conclusions:

  • The study offers a comprehensive comparison of ICER confidence interval methods, guiding best practices in health economic evaluations.
  • A novel bootstrapping technique is introduced, enhancing the analysis of therapies with non-significant health effects.
  • The findings are applicable to cardiovascular clinical trials and other health policy research involving cost-effectiveness analysis.