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Related Concept Videos

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...
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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

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Related Experiment Video

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Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Problems with interval estimates of the incremental cost-effectiveness ratio

D F Heitjan1, A J Moskowitz, W Whang

  • 1Division of Biostatistics, International Center for Health Outcomes and Innovation Research, Columbia University, New York, New York, USA. dfh5@columbia.edu

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|January 23, 1999
PubMed
Summary

New methods for estimating the incremental cost-effectiveness ratio (ICER) may fail to reliably cover true values. Simulations show issues, especially with large ICERs, but a modified bootstrap interval offers a partial solution for cost-effectiveness analysis.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Statistical Modeling

Background:

  • Confidence intervals are crucial for parameter estimation, ensuring a fixed probability of capturing true values.
  • Accurate estimation of the incremental cost-effectiveness ratio (ICER) is vital for healthcare decision-making.
  • Existing interval estimation methods for ICER may have limitations.

Purpose of the Study:

  • To evaluate recently proposed methods for ICER interval estimation.
  • To identify potential problems with these methods, particularly under specific conditions.
  • To propose a modified approach for more reliable ICER confidence intervals.

Main Methods:

  • Simulation studies were conducted to assess the performance of different ICER interval estimation methods.
  • The coverage probability of confidence intervals was examined across various scenarios.
  • A modified percentile bootstrap method was developed and tested.

Main Results:

  • Some recently proposed ICER interval estimation methods do not meet the definition of a confidence interval.
  • These methods exhibit significant problems when the ICER is large and the true effectiveness difference is small relative to its standard error.
  • The modified percentile bootstrap interval showed partial improvement in addressing these issues.

Conclusions:

  • Certain novel methods for ICER interval estimation are unreliable and may not guarantee coverage of the true parameter.
  • The performance issues are exacerbated under conditions of large ICER and small relative effectiveness differences.
  • A modified percentile bootstrap approach offers a potential improvement for constructing more robust confidence intervals in cost-effectiveness analyses.