Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2014

Open Access 01-12-2014 | Research article

A trivariate meta-analysis of diagnostic studies accounting for prevalence and non-evaluable subjects: re-evaluation of the meta-analysis of coronary CT angiography studies

Authors: Xiaoye Ma, Muhammad Fareed K Suri, Haitao Chu

Published in: BMC Medical Research Methodology | Issue 1/2014

Login to get access

Abstract

Background

A recent paper proposed an intent-to-diagnose approach to handle non-evaluable index test results and discussed several alternative approaches, with an application to the meta-analysis of coronary CT angiography diagnostic accuracy studies. However, no simulation studies have been conducted to test the performance of the methods.

Methods

We propose an extended trivariate generalized linear mixed model (TGLMM) to handle non-evaluable index test results. The performance of the intent-to-diagnose approach, the alternative approaches and the extended TGLMM approach is examined by extensive simulation studies. The meta-analysis of coronary CT angiography diagnostic accuracy studies is re-evaluated by the extended TGLMM.

Results

Simulation studies showed that the intent-to-diagnose approach under-estimate sensitivity and specificity. Under the missing at random (MAR) assumption, the TGLMM gives nearly unbiased estimates of test accuracy indices and disease prevalence. After applying the TGLMM approach to re-evaluate the coronary CT angiography meta-analysis, overall median sensitivity is 0.98 (0.967, 0.993), specificity is 0.875 (0.827, 0.923) and disease prevalence is 0.478 (0.379, 0.577).

Conclusions

Under MAR assumption, the intent-to-diagnose approach under-estimate both sensitivity and specificity, while the extended TGLMM gives nearly unbiased estimates of sensitivity, specificity and prevalence. We recommend the extended TGLMM to handle non-evaluable index test subjects.
Appendix
Available only for authorised users
Literature
1.
Begg CB, Greenes RA: Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics. 1983, 39 (1): 207-215. 10.2307/2530820.CrossRefPubMed
2.
de Groot JA, Dendukuri N, Janssen KJ, Reitsma JB, Brophy J, Joseph L, Bossuyt PM, Moons KG: Adjusting for partial verification or workup bias in meta-analyses of diagnostic accuracy studies. Am J Epidemiol. 2012, 175 (8): 847-853. 10.1093/aje/kwr383.CrossRefPubMed
3.
Harel O, Zhou X: Multiple imputation for correcting verification bias. Stat Med. 2006, 25 (22): 3769-3786. 10.1002/sim.2494.CrossRefPubMed
4.
Ransohoff DF, Feinstein AR: Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med. 1978, 299 (17): 926-930. 10.1056/NEJM197810262991705.CrossRefPubMed
5.
Begg CB, Greenes RA, Iglewicz B: The influence of uninterpretability on the assessment of diagnostic tests. J Chronic Dis. 1986, 39 (8): 575-584. 10.1016/0021-9681(86)90182-7.CrossRefPubMed
6.
Schuetz GM, Schlattmann P: Use of 3×2 tables with an intention to diagnose approach to assess clinical performance of diagnostic tests: meta-analytical evaluation of coronary ct angiography studies. BMJ. 2012, 345 (2): 6717-6717.CrossRef
7.
Simel DL, Feussner JR, Delong ER, Matchar DB: Intermediate, indeterminate, and uninterpretable diagnostic test results. Med Decis Making. 1987, 7 (2): 107-114. 10.1177/0272989X8700700208.CrossRefPubMed
8.
Rutter CM, Gatsonis CA: A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001, 20 (19): 2865-2884. 10.1002/sim.942.CrossRefPubMed
9.
Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005, 58 (10): 982-990. 10.1016/j.jclinepi.2005.02.022.CrossRefPubMed
10.
Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JA: A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics. 2007, 8 (2): 239-251. 10.1093/biostatistics/kxl004.CrossRefPubMed
11.
Ma X, Nie L, Cole SR, Chu H: Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial. Stat Methods Med Res. 2013, in press,
12.
Van Houwelingen HC, Arends LR, Stijnen T: Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med. 2002, 21 (4): 589-624. 10.1002/sim.1040.CrossRefPubMed
13.
Zwinderman AH, Bossuyt PM: We should not pool diagnostic likelihood ratios in systematic reviews. Stat Med. 2008, 27 (5): 687-697. 10.1002/sim.2992.CrossRefPubMed
14.
Chu H, Cole SR: Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol. 2006, 59 (12): 1331-1332. 10.1016/j.jclinepi.2006.06.011.CrossRefPubMed
15.
Hamza TH, Reitsma JB, Stijnen T: Meta-analysis of diagnostic studies: a comparison of random intercept, normal-normal, and binomial-normal bivariate summary roc approaches. Med Decis Making. 2008, 28 (5): 639-649. 10.1177/0272989X08323917.CrossRefPubMed
16.
Chu H, Guo H, Zhou Y: Bivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models. Med Decis Making. 2010, 30 (4): 499-508. 10.1177/0272989X09353452.CrossRefPubMed
17.
Chu H, Nie L, Cole SR, Poole C: Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: Alternative parameterizations and model selection. Stat Med. 2009, 28 (18): 2384-2399. 10.1002/sim.3627.CrossRefPubMed
18.
Little RJ, Rubin D: Statistical Analysis with Missing Data, 2nd Edn. 2002, New Jersey: John Wiley & Sons
19.
Pepe MS: The Statistical Evaluation of Medical Tests for Classification and Prediction. 2003, Oxford: Oxford University Press
20.
Brenner H, Gefeller O: Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence. Stat Med. 1997, 16 (9): 981-991. 10.1002/(SICI)1097-0258(19970515)16:9<981::AID-SIM510>3.0.CO;2-N.CrossRefPubMed
21.
Choi BC: Causal modeling to estimate sensitivity and specificity of a test when prevalence changes. Epidemiology. 1997, 1: 80-86.CrossRef
22.
Leeflang MM, Bossuyt PM, Irwig L: Diagnostic test accuracy may vary with prevalence: implications for evidence-based diagnosis. J Clin Epidemiol. 2009, 62 (1): 5-12. 10.1016/j.jclinepi.2008.04.007.CrossRefPubMed
23.
Little RJ: Modeling the drop-out mechanism in repeated-measures studies. J Am Stat Assoc. 1995, 90 (431): 1112-1121. 10.1080/01621459.1995.10476615.CrossRef
24.
Scharfstein DO, Rotnitzky A, Robins JM: Adjusting for nonignorable drop-out using semiparametric nonresponse models. J Am Stat Assoc. 1999, 94 (448): 1096-1120. 10.1080/01621459.1999.10473862.CrossRef
25.
Shinkins B, Thompson M, Mallett S, Perera R: Diagnostic accuracy studies: how to report and analyse inconclusive test results. BMJ: Br Med J. 2013, 346: 2778-10.1136/bmj.f2778.CrossRef
26.
Blick CG, Nazir SA, Mallett S, Turney BW, Onwu NN, Roberts IS, Crew JP, Cowan NC: Evaluation of diagnostic strategies for bladder cancer using computed tomography (ct) urography, flexible cystoscopy and voided urine cytology: results for 778 patients from a hospital haematuria clinic. BJU Int. 2012, 110 (1): 84-94. 10.1111/j.1464-410X.2011.10664.x.CrossRefPubMed
Metadata
Title
A trivariate meta-analysis of diagnostic studies accounting for prevalence and non-evaluable subjects: re-evaluation of the meta-analysis of coronary CT angiography studies
Authors
Xiaoye Ma
Muhammad Fareed K Suri
Haitao Chu
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2014
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-14-128

Other articles of this Issue 1/2014

BMC Medical Research Methodology 1/2014 Go to the issue

Research article

A critical analysis of test-retest reliability in instrument validation studies of cancer patients under palliative care: a systematic review

Research article

Model-based estimation of measures of association for time-to-event outcomes

Technical advance

Developing a weighting strategy to include mobile phone numbers into an ongoing population health survey using an overlapping dual-frame design with limited benchmark information

Research article

Easier said than done!: methodological challenges with conducting maternal death review research in Malawi

Research article

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design

Research article

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm