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Confidence Intervals

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 Intervals

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 Interval for Estimating Population Mean

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...

Confidence Coefficient

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...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

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...

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Updated: Jul 10, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane

A Briggs¹, P Fenn

¹Health Economics Research Centre, Institute of Health Sciences, University of Oxford, Headington, UK. andrew.briggs@ihs.ox.ac.uk

Health Economics

|January 16, 1999

Summary

Cost-effectiveness analysis (CEA) is evolving with patient-level data. This study reviews methods for estimating confidence intervals for cost-effectiveness ratios and suggests confidence surfaces are better for decision-making.

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Area of Science:

Health Economics
Biostatistics
Clinical Trial Analysis

Background:

Cost-effectiveness analysis (CEA) is increasingly integrated with clinical trials.
Patient-level data enables statistical analysis of uncertainty in CEA, moving beyond traditional sensitivity analysis.
Ratio statistics in CEA present challenges for standard confidence interval estimation methods.

Purpose of the Study:

To review and provide guidance on appropriate methods for estimating confidence intervals for cost-effectiveness ratios.
To shift the focus from estimation problems to decision-making problems in CEA.
To advocate for methods better suited for decision-makers, such as one-sided hypothesis tests.

Main Methods:

Review of recent health economics literature on confidence interval estimation for cost-effectiveness ratios.
Discussion of the limitations of standard statistical methods for ratio statistics.
Introduction of confidence surfaces as a superior alternative to confidence intervals for decision-making.

Main Results:

Existing literature offers various methods for confidence interval estimation for cost-effectiveness ratios.
Confidence intervals primarily address estimation challenges, not decision-making needs.
Confidence surfaces are more appropriate for one-sided hypothesis testing, aligning with decision-maker interests.

Conclusions:

Decision-makers in health economics benefit more from one-sided hypothesis tests than traditional confidence intervals.
Confidence surfaces offer a more suitable approach for presenting uncertainty in cost-effectiveness analyses.
The cost-effectiveness acceptability curve (CEAC) framework is consistent with using confidence surfaces for decision-making under uncertainty.