Likelihood-Based Clustering of Meta-Analytic SROC Curves

Psychometrika

Volumn 77, Issue 1, 2012, Pages 106-126

  Holling, Heinz ^a   Böhning, Walailuck ^a   Böhning, Dankmar ^b  

^a UNIVERSITY OF MÜNSTER   (Germany)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

C.A.MAN; diagnostic testing; meta analysis; sensitivity; specificity; summary receiver operating characteristic (SROC); summary statistics approach

Indexed keywords

EID: 84155186408     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-011-9236-2     Document Type: Article

References (55)

1. Aergeerts, B., Buntinx, F., & Kester, A. (2004). The value of the CAGE in screening for alcohol abuse and alcohol dependence in general clinical populations: A diagnostic meta-analysis. Journal of Clinical Epidemiology, 57, 30-39.
2. Aitkin, M. (1999a). A general maximum likelihood analysis of variance components in generalized linear models. Biometrics, 55, 117-128.
3. Aitkin, M. (1999b). Meta-analysis by random effect modelling in generalized linear models. Statistics in Medicine, 18, 2343-2351.
4. Biggerstaff, B. J., & Tweedie, R. L. (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine, 16, 753-768.
5. Böhning, D., Dietz, E., & Schlattmann, P. (1998). Recent developments in computer-assisted analysis of mixtures (C. A. MAN). Biometrics, 54, 367-377.
6. Böhning, D. (2000). Computer-assisted analysis of mixtures and applications. Meta-analysis, disease mapping and others. Boca Raton: Chapman & Hall/CRC.
7. Böhning, D., Malzahn, U., Dietz, E., Schlattmann, P., Viwatwongkasem, C., & Biggeri, A. (2002). Some general points in estimating heterogeneity variance with the DerSimonian-Laird estimator. Biostatistics, 3, 445-457.
8. Böhning, D. (2003). The EM algorithm with gradient function update for discrete mixtures with known (fixed) number of components. Statistics and Computing, 13, 257-265.
9. Böhning, D., & Viwatwongkasem, C. (2005). Revisiting proportion estimators. Statistical Methods in Medical Research, 14, 1-23.
10. Böhning, D., Holling, H., & Böhning, W. (2008). Revisiting Youden's index as a useful measure of the misclassification error in meta-analysis of diagnostic studies. Statistical Methods in Medical Research, 17, 543-554.
11. Brockwell, S. E., & Gordon, I. R. (2001). A comparison of statistical methods for meta-analysis. Statistical Methods in Medical Research, 20, 825-840.
12. Clayton, D. G., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disese mapping. Biometrics, 43, 671-681.
13. Cooper, H., & Hedges, L. (1994). The handbook of research synthesis. New York: Russell Sage Foundation.
14. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society. Series B, 39, 1-38.
15. DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177-188.
16. Egger, M., Smith, G. D., & Altman, D. G. (2001). Systematic reviews in health care: Meta-analysis in context. London: BMJ Publishing Group.
17. Gatsonis, C., & Paliwal, P. (2006). Meta-analysis of diagnostic and screening test accuracy evaluations: Methodologic primer. American Journal of Roentgenology, 187, 271-281.
18. Harbord, R. M., Deeks, J. J., Egger, M., Whiting, P., & Sterne, J. A. C. (2007). A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics, 8, 239-251.
19. Hardy, R. J., & Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine, 15, 619-629.
20. Hardy, R. J., & Thompson, S. G. (1998). Detecting and describing heterogeneity in meta-analysis. Statistics in Medicine, 17, 841-856.
21. Hasselblad, V., & Hedges, L. V. (1995). Meta-analysis of screening and diagnostic tests. Psychological Bulletin, 117, 167-178.
22. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. New York: Academic Press.
23. Hunter, J. E., Schmidt, F. L., & Jackson, B. G. (1982). Meta-analysis: Cumulative research findings across studies. Beverly Hills: Sage Publications.
24. Irwig, L., Tosteson, A. N., Gatsonis, C., Lau, J., Colditz, G., Chalmers, T. C., & Mosteller, F. (1994). Guidelines for meta-analyses evaluating diagnostic tests. Annals of Internal Medicine, 120, 667-676.
25. Irwig, L., Macaskill, P., Glasziou, P., & Fahey, M. (1995). Meta-analytic methods for diagnostic test accuracy. Journal of Clinical Epidemiology, 48, 119-130.
26. Kiefer, J., & Wolfowitz, J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. Annals of Mathematical Statistics, 27, 886-906.
27. Kriston, L., Hölzel, L., Weiser, A., Berner, M. M., & Härter, M. (2008). Meta-analysis: Are 3 questions enough to detect unhealthy alcohol use? Annals of Internal Medicine, 149, 879-888.
28. Kuhnert, R., & Böhning, D. (2007). A comparison of three different models for estimating relative risk in meta-analysis. Statistics in Medicine, 28, 2277-2296.
29. Laird, N. M. (1978). Nonparametric maximum likelihood estimation of a mixing distribution. Journal of the American Statistical Association, 73, 805-811.
30. Le, C. T. (2006). A solution for the most basic optimization problem associated with an ROC curve. Statistical Methods in Medical Research, 15, 571-584.
31. Lindsay, B. G. (1983). The geometry of mixture likelihoods: A general theory. Annals of Statistics, 11, 86-94.
32. Lindsay, B. G. (1995). Mixture models: Theory, geometry, and applications. Hayward: Institute of Statistical Mathematics.
33. Macaskill, P., Glasziou, P., & Irwig, L. (2005). Meta-analysis of diagnostic tests. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics. doi: 10. 1002/0470011815. b2a04026.
34. Martin, C. S., Liepman, M. R., & Young, C. M. (1990). The Michigan alcoholism screening test: False positives in a college student sample. Alcoholism, Clinical and Experimental Research, 14, 853-855.
35. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman & Hall.
36. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.
37. Midgette, A. S., Stukel, T. A., & Littenberg, B. (1993). A meta-analytic method for summarizing diagnostic test performances: Receiver-operating-characteristic-summary point estimates. Medical Decision Making, 13, 253-257.
38. Mitchell, A. J. (2009). A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment. Journal of Psychiatric Research, 43, 411-431.
39. Moses, L. E., Littenberg, B., & Shapiro, D. (1993). Combining independent studies of a diagnostic test into a summary ROC curve: Data-analytical approaches and some additional considerations. Statistics in Medicine, 12, 1293-1316.
40. Ray, S., & Lindsay, B. G. (2008). Model selection in high dimensions: A quadratic-risk-based approach. Journal of the Royal Statistical Society. Series B, 70, 95-118.
41. Rabe-Hesketh, S., Pickles, A., & Skrondal, A. (2003). Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation. Statistical Modelling, 3, 215-232.
42. Reinert, D. F., & Allen, J. P. (2002). The alcohol use identification test (AUDIT): A review of recent research. Alcoholism, Clinical and Experimental Research, 26, 272-279.
43. Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative measures in diagnostic reviews. Journal of Clinical Epidemiology, 58, 982-990.
44. Ross, S. (1985). Introduction to probability models. Orlando: Academic Press.
45. Rutter, C. M., & Gatsonis, C. A. (2001). A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Statistics in Medicine, 20, 2865-2884.
46. Schulze, R., Holling, H., & Böhning, D. (Eds.) (2003). Meta-analysis. New developments and applications in medical and social sciences. Göttingen: Hogrefe & Huber.
47. Selzer, M. L. (1971). The Michigan alcoholism screening test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127, 1653-1658.
48. Sidik, K., & Jonkman, J. N. (2005). Simple heterogeneity variance estimation for meta-analysis. Journal of the Royal Statistical Society. Series C, 54, 367-384.
49. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: Chapman & Hall/CRC.
50. Storgaard, H., Nielsen, S. D., & Gluud, C. (1994). The validity of the Michigan alcoholism screening test (MAST). Alcohol and Alcoholism, 29, 493-502.
51. Sutton, A. J., Abrams, K. R., Jones, D. R., Sheldon, T. A., & Song, F. (2000). Methods for meta-analysis in medical research. New York: Wiley.
52. Sweeting, M. J., Sutton, A. J., & Lambert, P. C. (2004). What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Statistics in Medicine, 23, 1351-1375.
53. Swets, J. A. (2009). Signal detection theory and ROC analysis in psychology and diagnostics. New York: Psychology Press.
54. van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. Statistics in Medicine, 12, 2273-2284.
55. Whitehead, A. (2002). Meta-analysis of controlled clinical trials. New York: Wiley.