Abstract
Motivated by the goal of evaluating a biomarker for acute kidney injury, we consider the problem of assessing operating characteristics for a new biomarker when a true gold standard for disease status is unavailable. In this case, the biomarker is typically compared to another imperfect reference test, and this comparison is used to estimate the performance of the new biomarker. However, errors made by the reference test can bias assessment of the new test. Analysis methods like latent class analysis have been proposed to address this issue, generally employing some strong and unverifiable assumptions regarding the relationship between the new biomarker and the reference test. We investigate the conditional independence assumption that is present in many such approaches and show that for a given set of observed data, conditional independence is only possible for a restricted range of disease prevalence values. We explore the information content of the comparison between the new biomarker and the reference test, and give bounds for the true sensitivity and specificity of the new test when operating characteristics for the reference test are known. We demonstrate that in some cases these bounds may be tight enough to provide useful information, but in other cases these bounds may be quite wide.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2933-2945 |
| Number of pages | 13 |
| Journal | Statistical Methods in Medical Research |
| Volume | 27 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1 2018 |
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Keywords
- Biomarkers
- conditional independence
- diagnostic tests
- imperfect reference
- sensitivity
- specificity
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
Cite this
Biomarker validation with an imperfect reference : Issues and bounds. / Emerson, Sarah C.; Waikar, Sushrut S.; Fuentes, Claudio; Bonventre, Joseph V.; Betensky, Rebecca.
In: Statistical Methods in Medical Research, Vol. 27, No. 10, 01.10.2018, p. 2933-2945.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Biomarker validation with an imperfect reference
T2 - Issues and bounds
AU - Emerson, Sarah C.
AU - Waikar, Sushrut S.
AU - Fuentes, Claudio
AU - Bonventre, Joseph V.
AU - Betensky, Rebecca
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Motivated by the goal of evaluating a biomarker for acute kidney injury, we consider the problem of assessing operating characteristics for a new biomarker when a true gold standard for disease status is unavailable. In this case, the biomarker is typically compared to another imperfect reference test, and this comparison is used to estimate the performance of the new biomarker. However, errors made by the reference test can bias assessment of the new test. Analysis methods like latent class analysis have been proposed to address this issue, generally employing some strong and unverifiable assumptions regarding the relationship between the new biomarker and the reference test. We investigate the conditional independence assumption that is present in many such approaches and show that for a given set of observed data, conditional independence is only possible for a restricted range of disease prevalence values. We explore the information content of the comparison between the new biomarker and the reference test, and give bounds for the true sensitivity and specificity of the new test when operating characteristics for the reference test are known. We demonstrate that in some cases these bounds may be tight enough to provide useful information, but in other cases these bounds may be quite wide.
AB - Motivated by the goal of evaluating a biomarker for acute kidney injury, we consider the problem of assessing operating characteristics for a new biomarker when a true gold standard for disease status is unavailable. In this case, the biomarker is typically compared to another imperfect reference test, and this comparison is used to estimate the performance of the new biomarker. However, errors made by the reference test can bias assessment of the new test. Analysis methods like latent class analysis have been proposed to address this issue, generally employing some strong and unverifiable assumptions regarding the relationship between the new biomarker and the reference test. We investigate the conditional independence assumption that is present in many such approaches and show that for a given set of observed data, conditional independence is only possible for a restricted range of disease prevalence values. We explore the information content of the comparison between the new biomarker and the reference test, and give bounds for the true sensitivity and specificity of the new test when operating characteristics for the reference test are known. We demonstrate that in some cases these bounds may be tight enough to provide useful information, but in other cases these bounds may be quite wide.
KW - Biomarkers
KW - conditional independence
KW - diagnostic tests
KW - imperfect reference
KW - sensitivity
KW - specificity
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UR - http://www.scopus.com/inward/citedby.url?scp=85043471004&partnerID=8YFLogxK
U2 - 10.1177/0962280216689806
DO - 10.1177/0962280216689806
M3 - Article
VL - 27
SP - 2933
EP - 2945
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 10
ER -