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Related Concept Videos

P-value01:10

P-value

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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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.
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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...
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Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Related Experiment Video

Updated: May 1, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Net risk reclassification p values: valid or misleading?

Margaret S Pepe1, Holly Janes, Christopher I Li

  • 1Affiliations of authors: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA (MSP, HJ, CIL); Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA (HJ).

Journal of the National Cancer Institute
|April 1, 2014
PubMed
Summary
This summary is machine-generated.

The Net Reclassification Index (NRI) frequently leads to false positive conclusions regarding biomarker performance. Researchers should be skeptical of NRI P values and consider alternative statistical methods for evaluating prediction models.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • The Net Reclassification Index (NRI) is widely used to assess biomarker improvements in risk prediction models.
  • Despite its popularity, concerns about the statistical validity of the NRI have emerged.
  • This study investigates the reliability of the NRI statistic.

Purpose of the Study:

  • To evaluate the statistical validity of the Net Reclassification Index (NRI).
  • To determine the rate of false-positive conclusions generated by the NRI.
  • To compare the performance of NRI with alternative statistical measures.

Main Methods:

  • Simulated studies using a population dataset (n=10000) with a 10.2% event rate.
  • Random selection of training (n=420) and test datasets (n=420 or 840).
  • Calculation of NRI, P values, change in area under the ROC curve, and likelihood ratio statistics.

Main Results:

  • Unacceptably high false-positive rates for NRI: 63.0% (training) and 18.8%-34.4% (test).
  • False-positive conclusions were rare for the change in area under the curve.
  • Likelihood ratio statistics yielded false-positive rates around the expected 5.0%.

Conclusions:

  • Conclusions on biomarker performance based solely on significant NRI P values warrant skepticism.
  • The use of NRI P values in scientific reporting should be discontinued.
  • Alternative statistical methods demonstrate greater reliability in assessing prediction model improvements.