Diagnostic accuracy and screening statistics

Overview & Definition

A baby's newborn bloodspot screen comes back abnormal, and the parents ask, through tears, whether their child has the disease. The technician on the phone has quoted a test sensitivity and specificity, but those numbers are not the answer the family needs. They need a probability: given this positive result, how likely is it that their baby is truly affected? That single question — the move from how good the test is to how likely the disease is in this child — is the whole point of diagnostic accuracy statistics. [1] [4]

A diagnostic accuracy study compares a test (the index test) against a reference standard — the best available method for establishing whether the disease is truly present — in the same group of patients, and reports how closely the two agree. The agreement is summarised in a 2×2 table that sorts every patient into one of four boxes: true positive, false positive, false negative, or true negative. From those four boxes come every measure you will meet in this topic: sensitivity, specificity, predictive values, and likelihood ratios. [2] [3]

There is a second, larger question that shares the same machinery. A screening test is offered to people who look and feel well, in order to find unsuspected disease whose early treatment changes the outcome — newborn bloodspot screening for phenylketonuria, automated auditory brainstem response screening for permanent childhood hearing impairment, the M-CHAT-R for autism. Screening sits inside the same 2×2 table, but it carries an extra burden of proof: because serious disease is rare in well children, the same test that performs beautifully in a clinic can flood a population with false positives, and because screening finds disease earlier and slower, it can manufacture the illusion of benefit even when no child is saved. [13]

So the work on this page has two halves. First, the measures of test performance — how to build the 2×2 table, derive sensitivity and specificity, watch predictive values bend with prevalence, and turn the result into a probability shift with likelihood ratios and a receiver operating characteristic curve. Second, the logic and the biases of screening — the Wilson and Jungner principles, the four biases that fake a survival benefit, and the QUADAS-2 and STARD tools that keep diagnostic studies honest. Cross-link the dedicated evidence-based medicine leaf for the broader appraisal engine; this page owns only the diagnostic-test statistics. [4] [9]

References14Show ledgerHide ledger

1. [1]Griner PF, Mayewski RJ, Mushlin AI, Greenland P Selection and interpretation of diagnostic tests and procedures. Principles and applications. Annals of internal medicine, 1981.PMID 6452080
2. [2]Jaeschke R, Guyatt G, Sackett DL Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? Evidence-Based Medicine Working Group. JAMA, 1994.PMID 8283589
3. [3]Jaeschke R, Guyatt GH, Sackett DL Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA, 1994.PMID 8309035
4. [4]Akobeng AK Understanding diagnostic tests 1: sensitivity, specificity and predictive values. Acta paediatrica, 2007.PMID 17407452
5. [5]Akobeng AK Understanding diagnostic tests 2: likelihood ratios, pre- and post-test probabilities and their use in clinical practice. Acta paediatrica, 2007.PMID 17306009
6. [6]Akobeng AK Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta paediatrica, 2007.PMID 17376185
7. [7]Deeks JJ, Altman DG Diagnostic tests 4: likelihood ratios. BMJ, 2004.PMID 15258077
8. [8]Whiting P, Rutjes AW, Reitsma JB, et al. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy. BMC medical research methodology, 2003.PMID 14606960
9. [9]Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine, 2011.PMID 22007046
10. [10]Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA, 1999.PMID 10493205
11. [11]Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, Lijmer JG, Moher D, Rennie D, de Vet HC Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Standards for Reporting of Diagnostic Accuracy. Croatian medical journal, 2003.PMID 14515428
12. [12]Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015.PMID 26511519
13. [13]Esserman LJ, Thompson IM, Reid B, et al. Addressing overdiagnosis and overtreatment in cancer: a prescription for change. Lancet oncology, 2014.PMID 24807866
14. [14]Egger M, Davey Smith G, Schneider M, Minder C Bias in meta-analysis detected by a simple, graphical test. BMJ, 1997.PMID 9310563