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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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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. 
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Confidence Intervals01:21

Confidence Intervals

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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...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Implementation of a Reference Interferometer for Nanodetection
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refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data.

Tatjana Ammer1,2, André Schützenmeister3, Hans-Ulrich Prokosch4

  • 1Chair of Medical Informatics, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany. tatjana.ammer@roche.com.

Scientific Reports
|August 7, 2021
PubMed
Summary
This summary is machine-generated.

The novel refineR algorithm accurately estimates medical reference intervals from real-world data, offering a valuable alternative to traditional methods requiring healthy samples. This approach enhances laboratory test interpretation.

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Area of Science:

  • Clinical Chemistry
  • Biostatistics
  • Medical Informatics

Background:

  • Reference intervals are crucial for interpreting laboratory test results in clinical medicine.
  • Direct methods for determining reference intervals necessitate samples from healthy individuals, which can be challenging to obtain.
  • Existing indirect methods may not always provide sufficient accuracy.

Purpose of the Study:

  • To introduce and evaluate the refineR algorithm, a novel indirect method for estimating reference intervals from real-world data.
  • To compare the performance of refineR against direct methods and an alternative indirect method (kosmic).
  • To assess the utility of refineR for analyzing pediatric laboratory data.

Main Methods:

  • The refineR algorithm employs an inverse approach to separate non-pathological and pathological distributions in observed test results.
  • Simulations were conducted using six common laboratory analytes with varying pathological data fractions.
  • Performance was evaluated by comparing estimated reference intervals against ground truth, kosmic, and direct methods (N=120 and N=400 samples).

Main Results:

  • refineR achieved the lowest mean percentage error (2.77%) among all evaluated methods.
  • refineR's accuracy in estimating reference intervals was comparable to the direct method with a larger sample size (N=400) and superior to the direct method with a smaller sample size (N=120) and kosmic.
  • Estimates from pediatric data using refineR were consistent with findings from published direct method studies.

Conclusions:

  • The refineR algorithm provides a precise and viable method for estimating reference intervals using readily available real-world data.
  • refineR serves as a valuable complement to traditional direct methods, particularly when healthy control samples are scarce.
  • This approach has the potential to improve the interpretation of laboratory diagnostics across various patient populations.