Biomarker Tools

This toolset estimates risk stratification from early biomarker data and provides strategies to advance biomarkers or other risk measures identified through case-control studies to clinical or public health applications. The toolset will show quantities for which people's intuition is poor, such as need for a single marker of a rare disease to improve management by some serious intervention. We hope these strategies will help researchers to identify and promote the most promising markers early on, illuminate any necessary improvements, and eliminate markers that are most likely to fail.

From Difference in Means to Risk Stratification: A Web Tool

Choose how to enter the data
User Defined Distribution (from file)

Normal Distribution
Enter Mean, Standard Error, and N for Cases and Controls
Cases Controls
Mean
Standard Error
N
Cases Controls Overall
Mean
Standard Error
N
Standard Deviation
Variance
Coefficient of Variation (CV)
Difference in Mean
Delta
AUC

Calculating

Biomarker Comparison

This tool plots contours of likelihood ratio positive (LR+) and likelihood ratio negative (LR-) for a reference test with indicated sensitivity and specificity pairs (see the example plot below). The likelihood ratio contours define four areas:

sample Likelyhood Ratio plot, Sensitivity versus Specificity with Likelyhood Ratio contours
  • Area A shows combinations of sensitivity and specificity with higher LR+ and LR- than the reference test. This corresponds to a higher positive predictive value (PPV) and lower complement of the negative predictive value (cNPV)
  • Area B shows combinations of sensitivity and specificity with higher LR+ and lower LR- than the reference test. This corresponds to a higher positive predictive value (PPV) and higher complement of the negative predictive value (cNPV).
  • Area C shows combinations of sensitivity and specificity with lower LR+ and higher LR- compared to the reference test. This corresponds to a lower positive predictive value (PPV) and lower complement of the negative predictive value (cNPV).
  • Area D shows combinations of sensitivity and specificity with lower LR+ and lower LR- compared to the reference test. This corresponds to a lower positive predictive value (PPV) and higher complement of the negative predictive value (cNPV)

LR+ and LR- measures provide test-specific characteristics of risk stratification that yield estimates of absolute risk (PPV and NPV) when multiplied with the specific disease prevalence. LR+ and LR- estimated in one population will be the same in another population, whenever sensitivities and specificities are the same in the 2 populations, even when disease prevalences are much different. To calculate PPV and cNPV, provide a prevalence value.

Input Information
Manually enter parameters OR import values from file

# Reference Sensitivity Specificity
this row is the reference row 0.8 0.7
click to set this row as the reference row 0.85 0.68
click to set this row as the reference row 0.9 0.5

Calculating

References:

Marina V. Kondratovich, (2007),Comparing Two Medical Tests When Results of Reference Standard Are Unavailable for Those Negative via Both Tests, Journal of Biopharmaceutical Statistics, 18:1, 145-166,DOI: 10.1080/10543400701668308

Risk Stratification Advanced Analysis

icon for the definition of the independent value type
icon for the definition of the contour value type
icon for the definition of the fixed value type
Example

Calculating

Mean Risk Stratification

Biomarker #1
Input: 2x2 Table
Marker Negative (M-) Marker Positive (M+)
Disease Positive (D+)
Disease Negative (D-)
Input: PPV, NPV, Sensitivity, Specificity, etc.
Parameter Proportion
pop up definition for parameter 1 dropdown value
pop up definition for parameter 2 dropdown value
pop up definition for parameter 3 dropdown value
Example 1Example 2

Calculating

Calculations (Biomarker Title Placeholder) (Biomarker Title Placeholder) (Biomarker Title Placeholder)
Mean Risk Stratification = 2(ad-bc)
Maximum possible MRS for a disease with this prevalence
Number Needed to Test (NNTest) =2/MRS
Parameters (Biomarker Title Placeholder) (Biomarker Title Placeholder) (Biomarker Title Placeholder)
True Positive Fraction = a
False Negative Fraction = b
False Positive Fraction = c
True Negative Fraction = d
Marker Positivity
Disease Prevalence
Positive Predictive Value (PPV)
Complement of Negative Predictive Value (cNPV)
Sensitivity
Specificity
Complement of Specificity (cSpec)
Relative Risk
Risk Difference (RD)
Youden's Index (J)
AUC = (1+J)/2

Help

Tools Help

Risk Stratification Advanced Analysis
Instructions for Input

This tool calculates values and creates graphs for valid combinations of given PPV, cNPV, delta, specificity, sensitivity, and prevalence values. Click on the 'Example' link for sample input combinations.

  1. Choose values for the independent variable, or for the x-axis of the output graph, from the drop-down menu next to 'Independent Variable'. Enter your values as decimals separated by commas.
  2. Choose values for the contours of the output graph from the drop-down menu next to 'Contour'. Enter your values as decimals separated by commas.
  3. Choose fixed values. Then enter your values as decimals separated by commas.
  4. Click 'Calculate'.
Input Validation Rules
  • Specificity, Sensitivity, PPV, cNPV, and Prevalence can only be 0 to 1
  • Delta can be 0 to 5
  • cNPV < Prevalence
  • For arrays: max(cNPV) < min(Prevalence)
  • Prevalence < PPV
  • For arrays: max(prev) < min(PPV)
  • Sensitivity+Specificity-1 > 0
  • PPV and cNPV; Sensitivity, Specificity, and Delta; PPV, Prevalence, and Delta; and cNPV, Prevalence, and Delta are invalid combinations of input

FAQ

Where do I go for technical support?
Please send an
e-mail to our technical support team.
What browsers do the web tools support?
The web tools have been designed and tested with Internet Explorer 10, Firefox and Chrome.
They do not support Internet Explorer 9 and below.

Glossary of Terms

NIH ... Turning Discovery Into Health®