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Multi-State Models for Panel Data: The msm Package for R
Open accessAbstract
Panel data are observations of a continuous-time process at arbitrary times, for example, visits to a hospital to diagnose disease status. Multi-state models for such data are generally based on the Markov assumption. This article reviews the range of Markov models and their extensions which can be fitted to panel-observed data, and their implementation in the msm package for R. Transition intensities may vary between individuals, or with piecewise-constant time-dependent covariates, giving an inhomogeneous Markov model. Hidden Markov models can be used for multi-state processes which are misclassified or observed only through a noisy marker. The package is intended to be straightforward to use, flexible and comprehensively documented. Worked examples are given of the use of msm to model chronic disease progression and screening. Assessment of model fit, and potential future developments of the software, are also discussed.
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Cites
The Theory of Stochastic Processes
Cox, D. R., Miller, H. D., Weiss, G., & Shuler, K. (1966) · Physics Today · doi:10.1063/1.3047872 · Cited by 3,324
Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
Moler, C., & Van Loan, C. (2003) · SIAM Review · doi:10.1137/s00361445024180 · Cited by 2,357
Dementia and survival in Parkinson disease: A 12-year population study
Buter, T. C., van den Hout, A., Matthews, F. E., Larsen, J. P., Brayne, C., & Aarsland, D. (2008) · Neurology · doi:10.1212/01.wnl.0000306632.43729.24 · Cited by 451
Cited by
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761
Vermunt, L., Sikkes, S. A. M., van den Hout, A., Handels, R., Bos, I., van der Flier, W. M., Kern, S., Ousset, P., Maruff, P., Skoog, I., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.04.001 · Cited by 570
“3, (estimating life-expectancies for interval censored data) were used to estimate the duration estimates and CIs [36,37].”
MethodInfluentialflexsurv : A Platform for Parametric Survival Modeling in R
Jackson, C. (2016) · Journal of Statistical Software · doi:10.18637/jss.v070.i08 · Cited by 558
Self-citationIncidence and Long-Term Outcomes of Hypertensive Disorders of Pregnancy
Garovic, V. D., White, W. M., Vaughan, L., Saiki, M., Parashuram, S., Garcia-Valencia, O., Weissgerber, T. L., Milic, N., Weaver, A., & Mielke, M. M. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.03.028 · Cited by 426
Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection
Brotman, R. M., Shardell, M. D., Gajer, P., Tracy, J. K., Zenilman, J. M., Ravel, J., & Gravitt, P. E. (2014) · Journal of Infectious Diseases · doi:10.1093/infdis/jiu330 · Cited by 411
Veloso, A., Kirkconnell, K. S., Magnuson, B., Biewen, B., Paulsen, M. T., Wilson, T. E., & Ljungman, M. (2014) · Genome Research · doi:10.1101/gr.171405.113 · Cited by 281
Iorfino, F., Scott, E. M., Carpenter, J. S., Cross, S. P., Hermens, D. F., Killedar, M., Nichles, A., Zmicerevska, N., White, D., Guastella, A. J., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.2360 · Cited by 188
Suresh, K., Psoter, K. J., Voong, K. R., Shankar, B., Forde, P. M., Ettinger, D. S., Marrone, K. A., Kelly, R. J., Hann, C. L., Levy, B., et al. (2019) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2018.11.016 · Cited by 186
Discrete Time Markov Chains with R
Spedicato, G. (2017) · The R Journal · doi:10.32614/rj-2017-036 · Cited by 148
Frequency of evidence-based screening for retinopathy in type 1 diabetes
The DCCT/EDIC Research Group (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1612836 · Cited by 141
Jack, C. R., Therneau, T. M., Wiste, H. J., Weigand, S. D., Knopman, D. S., Lowe, V. J., Mielke, M. M., Vemuri, P., Roberts, R. O., Machulda, M. M., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00323-3 · Cited by 129
Efficacy of inactivated poliovirus vaccine in India
Jafari, H., Deshpande, J. M., Sutter, R. W., Bahl, S., Verma, H., Ahmad, M., Kunwar, A., Vishwakarma, R., Agarwal, A., Jain, S., et al. (2014) · Science · doi:10.1126/science.1255006 · Cited by 112
Cognitive lifestyle jointly predicts longitudinal cognitive decline and mortality risk
Marioni, R. E., Proust-Lima, C., Amieva, H., Brayne, C., Matthews, F. E., Dartigues, J.-F., & Jacqmin-Gadda, H. (2014) · European Journal of Epidemiology · doi:10.1007/s10654-014-9881-8 · Cited by 99
Chen, Y., Bandosz, P., Stoye, G., Liu, Y., Wu, Y., Lobanov-Rostovsky, S., French, E., Kivimaki, M., Livingston, G., Liao, J., et al. (2023) · The Lancet Public Health · doi:10.1016/s2468-2667(23)00214-1 · Cited by 96
on behalf of the Scottish Diabetes Research Network (SDRN) Epidemiology Group and the Scottish Diabetic Retinopathy Collaborative, Looker, H. C., Nyangoma, S. O., Cromie, D. T., Olson, J. A., Leese, G. P., Philip, S., Black, M. W., Doig, J., Lee, N., et al. (2013) · Diabetologia · doi:10.1007/s00125-013-2928-7 · Cited by 80
Robitaille, A., van den Hout, A., Machado, R. J. M., Bennett, D. A., Čukić, I., Deary, I. J., Hofer, S. M., Hoogendijk, E. O., Huisman, M., Johansson, B., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.10.003 · Cited by 77
Chan, M. S., van den Hout, A., Pujades-Rodriguez, M., Jones, M. M., Matthews, F. E., Jagger, C., Raine, R., & Bajekal, M. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz052 · Cited by 57
Laiolo, P., Seoane, J., Obeso, J. R., & Illera, J. C. (2017) · Global Ecology and Biogeography · doi:10.1111/geb.12565 · Cited by 46
Comparing evolutionary rates between trees, clades and traits
Revell, L. J., González‐Valenzuela, L. E., Alfonso, A., Castellanos‐García, L. A., Guarnizo, C. E., & Crawford, A. J. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12977 · Cited by 38
Finger, R. P., Puth, M.-T., Schmid, M., Barthelmes, D., Guymer, R. H., & Gillies, M. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2020.3989 · Cited by 34
Saqr, M., López-Pernas, S., & Vogelsmeier, L. V. D. E. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104934 · Cited by 26
Cuthbertson, C. C., Tan, X., Heiss, G., Kucharska‐Newton, A., Nichols, H. B., Kubota, Y., & Evenson, K. R. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012657 · Cited by 26
Coley, N., Gallini, A., Garès, V., Gardette, V., Andrieu, S., Vellas, B., Reynish, E., Ousset, P. J., Andrieu, S., Burns, A., et al. (2015) · Journal of the American Medical Directors Association · doi:10.1016/j.jamda.2015.09.010 · Cited by 20
Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data
Rodriguez, P. J., Ward, Z. J., Long, M. W., Austin, S. B., & Wright, D. R. (2021) · Medical Decision Making · doi:10.1177/0272989x20985752 · Cited by 10
Deciphering the serological response to syphilis treatment in men living with HIV
Ren, M., Szadkowski, L., & Walmsley, S. L. (2020) · AIDS · doi:10.1097/qad.0000000000002656 · Cited by 10
Metadata
- Authors
- Jackson, C. H.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2011
- Volume
- 38
- Issue
- 8
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W1919480245
- Cited by
- 1,297
- References
- 3
- FWCI
- 42.01
- Type
- journal article
- Language
- en
- Open access
- diamond
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