Dealing with excess of zeros in the statistical analysis of magnetic resonance imaging lesion count in multiple sclerosis

Pharmaceutical Statistics

Volumn 11, Issue 5, 2012, Pages 417-424

  Francois, Mercier ^a   Peter, Chin ^b   Gordon, Francis ^b  

^a Pharsight Consulting Services   (United States)

^b NOVARTIS PHARMA AG   (Switzerland)

Author keywords

brain lesion; count; fingolimod; generalized linear mixed effect model; multiple sclerosis; negative binomial; zero inflation

Indexed keywords

FINGOLIMOD;

ARTICLE; BRAIN DAMAGE; DRUG EFFICACY; DRUG SAFETY; HUMAN; MATHEMATICAL PARAMETERS; MULTIPLE SCLEROSIS; NEGATIVE BINOMIAL; NUCLEAR MAGNETIC RESONANCE IMAGING; POISSON DISTRIBUTION; PROPORTIONAL HAZARDS MODEL; RECURRENT DISEASE; STATISTICAL ANALYSIS; ZERO INFLATED NEGATIVE BINOMIAL; ZERO INFLATED POISSON;

ADOLESCENT; ADULT; BRAIN; DATA INTERPRETATION, STATISTICAL; DOSE-RESPONSE RELATIONSHIP, DRUG; DOUBLE-BLIND METHOD; ENDPOINT DETERMINATION; FEMALE; HUMANS; IMMUNOSUPPRESSIVE AGENTS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MODELS, STATISTICAL; MULTIPLE SCLEROSIS, RELAPSING-REMITTING; POISSON DISTRIBUTION; PROPYLENE GLYCOLS; REGRESSION ANALYSIS; SPHINGOSINE; YOUNG ADULT;

EID: 84866352725     PISSN: 15391604     EISSN: 15391612     Source Type: Journal    
DOI: 10.1002/pst.1529     Document Type: Article

References (42)

1. nd edn). Cambridge University Press: Cambridge, UK, 2011.
2. Deb P, Trivedi P,. Demand for medical care by the elderly: a finite mixture approach. Journal of Applied Econometrics 1997; 12: 313-336.
3. Slymen DJ, Ayala GX, Arredondo EM, Elder J,. A demonstration of modeling count data with an application to physical activity. Epidemiologic Perspectives & Innovations 2006; 3: 1-9. DOI: 10.1186/1742-5573-3-3.
4. Yau KK, Lee AH,. Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme. Statistics in Medicine 2001; 20: 2907-2920. (Pubitemid 32923215)
5. Yau KK, Wang K, Lee AH,. Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biometrical Journal 2003; 45: 437-452. DOI: 10.1002/bimj.200390024. (Pubitemid 36802755)
6. Sheu ML, Hu TW, Keeler TE, Ong M, Sung HY,. The effect of a major cigarette price change on smoking behavior in California: a zero-inflated negative binomial model. Health Economics 2004; 13: 781-791. (Pubitemid 39136903)
7. Wang H, Heitjan DF,. Modeling heaping in self-reported cigarette counts. Statistics in Medicine 2008; 27: 3789-3804. DOI: 10.1002/sim.3281.
8. Min Y, Agresti A,. Random effect models for repeated measures of zero-inflated count data. Statistical Modelling 2005; 5: 1-19. DOI: 10.1191/1471082X05st084oa. (Pubitemid 40549467)
9. Rose CE, Martin SW, Wannemuehler KA, Plikaytis DB,. On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of Biopharmaceutical Statistics 2006; 16: 463-481. DOI: 0.1080/10543400600719384. (Pubitemid 43995556)
10. Hsu C-H,. A weighted zero-inflated Poisson model for estimation of recurrence of adenomas. Statistical Methods in Medical Research 2007; 16: 155-166. DOI: 10.1177/0962280206071929. (Pubitemid 46656630)
11. Hasan TM, Sneddon G, Ma R,. Pattern-mixture zero-inflated mixed models for longitudinal unbalanced count data with excessive zeros. Biometrical Journal 2009; 51: 946-960.
12. Mwalili S, Lesaffre E, Declerck D,. The zero-inflated negative regression model with correction for misclassification: an example in caries research. Statistical Methods in Medical Research 2008; 17: 123-139. DOI: 10.1177/0962280206071840. (Pubitemid 351699483)
13. Gilthorpe MS, Fryden M, Cheng Y, Baelum V,. Modelling count data with excessive zeros: the need for class prediction in zero-inflated models and the issue of data generation in choosing between zero-inflated and generic mixtures for dental caries data. Statistics in Medicine 2009; 28: 3539-3553. DOI: 10.1002/sim.3699.
14. Bermel RA, Fisher E, Cohen JA,. The use of MR imaging as an outcome measure in multiple sclerosis clinical trials. Neuroimaging Clinics of North America 2008; 18: 687-701.
15. Sormani MP, Bruzzi P, Miller DH, Gasperini C, Barkhof F, Filippi M,. Modelling MRI enhancing lesion counts in multiple sclerosis using a negative binomial model: implications for clinical trials. Journal of Neurological Sciences 1999; 163: 74-80. (Pubitemid 29155420)
16. Sormani M, Bruzzi P, Beckmann K, Kappos L, Miller D, Polman C, Pozzili C, Thompson AK, Wagner K, Flippi M,. The distribution of magnetic resonance imaging response to interferon beta-1b in multiple sclerosis. Journal of Neurology 2005; 252: 1455-1458. DOI: 10.1007/s00415-005-0885-x. (Pubitemid 41803833)
17. Sormani MP, Bruzzi P, Comi G, Filippi M,. The distribution of the magnetic resonance imaging response to glatiramer acetate in multiple sclerosis. Multiple Sclerosis 2005; 11: 447-449. DOI: 10.1191/1352458505ms1178oa. (Pubitemid 40984240)
18. Zhao Y, Traboulsee A, Petkau AJ, Li D,. Regression of new gadolinium enhancing lesion activity in relapsing-remitting multiple sclerosis. Neurology 2008; 70: 1092-1097. (Pubitemid 351464765)
19. Van den Elskamp IJ, Knol DL, Uitdehaag BMJ, Barkhof F,. The distribution of new enhancing lesion counts in multiple sclerosis: further explorations. Multiple Sclerosis 2009; 15: 42-49. DOI: 10.1177/1352458508096683.
20. Brinkman V,. FTY720 (fingolimod) in multiple sclerosis: therapeutic effects in the immune and the central nervous system. British Journal of Pharmacology 2009; 158: 1173-1182. DOI: 10.1111/j.1476-5381.2009.00451.x.
21. Kappos L, Radue E-W, O'Connor P, Hohlfeld R, Calabresi P, Selmaj K, Agoropoulou C, Leyk M, Zhang-Auberson L, Burtin P,. A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. New England Journal of Medicine 2010; 362: 387-401.
22. McFarland HF, Frank JA, Albert PS, Smith ME, Martin R, Harris JO, Patronas N, Maloni H, McFarlin DE,. Using gadolinium-enhanced magnetic resonance imaging lesions to monitor disease activity in multiple sclerosis. Annals of Neurology 1992; 32: 758-766.
23. Katz D, Taubenberger JK, Cannella B, McFarlin DE, Raine CS, McFarland HF,. Correlation between magnetic resonance imaging findings and lesion development in chronic, active multiple sclerosis. Annals of Neurology 1993; 34: 661-669. (Pubitemid 23333237)
24. Morgan CJ, Aban IB, Katholi CR, Cutter GR,. Modeling lesion counts in multiple sclerosis when patients have been selected for baseline activity. Multiple Sclerosis 2010; 16: 926-934. DOI: 10.1177/1352458510373110.
25. Kurtzke JF,. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 1983; 33: 1444-1452. (Pubitemid 14237132)
26. Barkhof F, Held U, Simon JH, Daumer M, Fazekas F, Filippi M, Frank JA, Kappos L, Li D, Menzler S, Miller DH, Petkau J, Wolinsky J,. Predicting gadolinium enhancement status in MS patients eligible for randomized clinical trials. Neurology 2005; 65: 1447-1454. (Pubitemid 41552778)
27. Tortorella C, Bellacosa A, Paolicelli D, Fuiani A, Di Monte E, Simone IL, Giaguinto P, Livrea P, Trojano M,. Age-related gadolinium-enhancement of MRI brain lesions in multiple sclerosis. Journal of Neurological Sciences 2005; 239: 95-99. (Pubitemid 41617420)
28. Li DKB, Held U, Petkau J, Daumer M, Barkhof F, Fazekas F, Frank JA, Kappos L, Miller DH, Simon JH, Wolinsky JS, Filippi M,. MRI T2 lesion burden in multiple sclerosis. Neurology 2006; 66: 1384-1389. DOI: 10.1212/01.wnl. 0000210506.00078.5c.
29. Cook R, Lawless JF,. The Statistical Analysis of Recurrent Events. Springer: New York, NY, 2010.
30. Cheung YB,. Zero-inflated models for regression analysis of count data: a study of growth and development. Statistics in Medicine 2002; 21: 1461-1469. (Pubitemid 34495611)
31. Lambert D,. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 1992; 34: 1-14.
32. Booth JG, Casella G, Friedl H, Hobert JP,. Negative binomial log linear mixed models. Statistical Modelling 2003; 3: 179-191.
33. Pinheiro J, Bates D,. Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics 1995; 4: 12-35.
34. Vuong QH,. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 1989; 57: 307-333.
35. Greene WH,. Accounting for excess zeros and sample selection in poisson and negative binomial regression models. NYU Working Paper No. EC-94-10, 1994. Available at SSRN: http://ssrn.com/abstract=1293115 [Accessed on Jan 02, 2010].
36. Deng D, Paul R,. Score tests for zero-inflation and over-dispersion in generalized linear models. Statistica Sinica 2005; 15: 257-276. (Pubitemid 40531556)
37. Yang Z, Hardin JW, Addy CL,. Score tests for zero-inflation in overdispersed count data. Communications in Statistics-Theory Methods 2010; 39: 2008-2030. DOI: 10.1080/03610920902948228.
38. nd edn). Springer-Verlag: New York, NY, 2002.
39. Mullahy J,. Specification and testing of some modified count data models. Journal of Econometrics 1986; 33: 341-365.
40. Molas M, Lesaffre E,. Hurdle models for multilevel zero-inflated data via h-likelihood. Statistics in Medicine 2010; 29: 3294-3310. DOI: 10.1002/sim.3852.
41. Li X,. Stochastic models for MRI lesion count sequences from patients with relapsing remitting multiple sclerosis. Unpublished Ph.D. Thesis, The Ohio State University Department of Statistics, 2006.
42. Altman RM, Petkau AJ, Vrecko D, Smith A,. A longitudinal model for magnetic resonance imaging lesion count data in multiple sclerosis patients. Statistics in Medicine 2012; 31: 449-469. DOI: 10.1002/sim.4394.