The analysis of multivariate longitudinal data: A review
Open accessAbstract
Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
As noted in the review by Verbeke et al. (2014), copula models have been utilized in the multivariate longitudinal data literature although it is not widely used in biomedical applications.
“As noted in the review by Verbeke et al. (2014), copula models have been utilized in the multivariate longitudinal data literature although it is not widely used in biomedical applications; early contributions include Frees and Wang (2005), Lambert (1996), Lambert and Vandenhende (2002), and Meester and Mackay (1994).”
From Dependence modeling of multivariate longitudinal hybrid insurance data with dropout (2021) · cites this paper as Verbeke et al. (2014)
Section 2.1 (Marginal models; page unavailable) identifies a copula-based analysis of multivariate longitudinal outcomes and says that very limited applications of copulas in that setting had been reported. That supports the claim about use in the multivariate longitudinal literature, but the review does not establish how widely copulas are used across biomedical applications generally. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550 · see citing works
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923 · see citing works
Nelsen, R. B. (1999) · Lecture notes in statistics · doi:10.1007/978-1-4757-3076-0 · Cited by 7,885 · see citing works
Everitt, B. S. (2001) · Psychology Press eBooks · doi:10.4324/9781410605542-11 · Cited by 6,877 · see citing works
Goldstein, H. (2006) · Technometrics · doi:10.1198/tech.2006.s417 · Cited by 4,960 · see citing works
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,595 · see citing works
Fonctions de répartition à N dimensions et leurs marges
Sklar, M. (1959) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 4,271 · see citing works
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506 · see citing works
Self-citationDiggle, P. J., Heagerty, P. J., Liang, K., & Zeger, S. L. (2002) · doi:10.1093/oso/9780198524847.001.0001 · Cited by 3,388 · see citing works
Courgeau, D., & Goldstein, H. (1997) · Population · doi:10.2307/1534624 · Cited by 2,590 · see citing works
Analyzing developmental trajectories: A semiparametric, group-based approach.
Nagin, D. S. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.2.139 · Cited by 2,346 · see citing works
Analyzing developmental trajectories of distinct but related behaviors: A group-based method.
Nagin, D. S., & Tremblay, R. E. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.1.18 · Cited by 1,283 · see citing works
Nonlinear Models for Repeated Measurement Data
Young, D. A., Davidian, M., & Giltinan, D. M. (1997) · Journal of the American Statistical Association · doi:10.2307/2965730 · Cited by 1,226 · see citing works
Self-citationNagin, D. S., & Land, K. C. (1993) · Criminology · doi:10.1111/j.1745-9125.1993.tb01133.x · Cited by 1,136 · see citing works
Models for Discrete Longitudinal Data
Molenberghs, G., & Verbeke, G. N. (2005) · Springer eBooks · doi:10.1007/0-387-28980-1 · Cited by 1,092 · see citing works
Self-citationBeyond SEM: General latent variable modeling
Muthén, B. O. (2002) · Behaviormetrika · doi:10.2333/bhmk.29.81 · Cited by 1,074 · see citing works
Correlated Binary Regression with Covariates Specific to Each Binary Observation
Prentice, R. L. (1988) · Biometrics · doi:10.2307/2531733 · Cited by 861 · see citing works
Nonlinear Models for Repeated Measurement Data
Timm, N. H., Davidian, M., & Giltinan, D. M. (1996) · Technometrics · doi:10.2307/1270420 · Cited by 819 · see citing works
Self-citationNormal Human Aging: The Baltimore Longitudinal Study on Aging
Shock, N. W., Greulich, R. C., Costa, P. T., Andres, R., Lakatta, E. G., Arenberg, D., & Tobin, J. D. (1984) · University Libraries (University of Maryland) · doi:10.13016/sclw-iccq · Cited by 757 · see citing works
Dynamic but Structural Equation Modeling of Repeated Measures Data
Mcardle, J. (1988) · Springer eBooks · doi:10.1007/978-1-4613-0893-5_17 · Cited by 670 · see citing works
New methods for the analysis of change
Collins, L. M., & Sayer, A. (2001) · American Psychological Association eBooks · doi:10.1037/10409-000 · Cited by 578 · see citing works
Lindsay, B. G. (1988) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/080/999014 · Cited by 555 · see citing works
Modelling multivariate binary data with alternating logistic regressions
CAREY, V., ZEGER, S. L., & DIGGLE, P. (1993) · Biometrika · doi:10.1093/biomet/80.3.517 · Cited by 514 · see citing works
A Class of Bivariate Distributions
Plackett, R. L. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480807 · Cited by 492 · see citing works
A Dynamic Factor Model for the Analysis of Multivariate Time Series
Molenaar, P. C. M. (1985) · Psychometrika · doi:10.1007/bf02294246 · Cited by 480 · see citing works
Correlated binary regression using a quadratic exponential model
Zhao, L. P., & Prentice, R. L. (1990) · Biometrika · doi:10.1093/biomet/77.3.642 · Cited by 480 · see citing works
Chan, D. K. (1998) · Organizational Research Methods · doi:10.1177/109442819814004 · Cited by 454 · see citing works
Joe, H., Reid, N. M., Xuekun, P., & Firth, D. (2012) · Cited by 434 · see citing works
Gender differences in a longitudinal study of age-associated hearing loss
Pearson, J. D., Morrell, C. H., Gordon-Salant, S. M., Brant, L. J., Metter, E. J., Klein, L. L., & Fozard, J. L. (1995) · The Journal of the Acoustical Society of America · doi:10.1121/1.412231 · Cited by 416 · see citing works
Studying Multivariate Change Using Multilevel Models and Latent Curve Models
MacCallum, R. C., Kim, C., Malarkey, W. B., & Kiecolt‐Glaser, J. K. (1997) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3203_1 · Cited by 367 · see citing works
Longford, N. T. (1987) · Biometrika · doi:10.1093/biomet/74.4.817 · Cited by 359 · see citing works
Families of Bivariate Distributions
Huzurbazar, V. S., & Mardia, K. V. (1973) · International Statistical Review · doi:10.2307/1402797 · Cited by 354 · see citing works
Duchateau, L., & Janssen, P. (2007) · Cited by 347 · see citing works
Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution
Molenberghs, G., & Lesaffre, E. M. E. H. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476788 · Cited by 282 · see citing works
Self-citationA Composite Likelihood Approach to Binary Spatial Data
Heagerty, P. J., & Lele, S. R. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473771 · Cited by 265 · see citing works
Alternative Structural Models for Multivariate Longitudinal Data Analysis
Ferrer, E., & McArdle, J. (2003) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem1004_1 · Cited by 239 · see citing works
Age changes in pure-tone hearing thresholds in a longitudinal study of normal human aging
Brant, L. J., & Fozard, J. L. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.399731 · Cited by 231 · see citing works
Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles
Fieuws, S., & Verbeke, G. N. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00507.x · Cited by 231 · see citing works
Self-citationGałecki, A. T. (1994) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929408831436 · Cited by 210 · see citing works
Stoolmiller, M. (1994) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2903_4 · Cited by 210 · see citing works
An Illustration of Second-Order Latent Growth Models
Hancock, G. R., Kuo, W.-L., & Lawrence, F. (2001) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0803_7 · Cited by 175 · see citing works
Random Effects Models for Longitudinal Data
Verbeke, G. N., Molenberghs, G., & Rizopoulos, D. (2010) · Springer eBooks · doi:10.1007/978-3-642-11760-2_2 · Cited by 163 · see citing works
Self-citationA Random-Effects Model for Multiple Characteristics with Possibly Missing Data
Shah, A., Laird, N. M., & Schoenfeld, D. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474030 · Cited by 161 · see citing works
A Family of Generalized Linear Models for Repeated Measures with Normal and Conjugate Random Effects
Molenberghs, G., Verbeke, G., Demétrio, C. G. B., & Vieira, A. M. C. (2010) · Statistical Science · doi:10.1214/10-sts328 · Cited by 145 · see citing works
Self-citationSecond-order latent growth models.
Sayer, A. G., & Cumsille, P. E. (2001) · American Psychological Association eBooks · doi:10.1037/10409-006 · Cited by 133 · see citing works
Timmerman, M. E., & Kiers, H. A. L. (2003) · Psychometrika · doi:10.1007/bf02296656 · Cited by 132 · see citing works
A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects
Molenberghs, G., Verbeke, G. N., Demétrio, C. G. B., & Vieira, A. M. C. (2009) · Cited by 127 · see citing works
Self-citationJoint modelling of multivariate longitudinal profiles: pitfalls of the random‐effects approach
Fieuws, S., & Verbeke, G. N. (2004) · Statistics in Medicine · doi:10.1002/sim.1885 · Cited by 120 · see citing works
Self-citationBivariate linear mixed models using SAS proc MIXED
Thiébaut, R., Jacqmin‐Gadda, H., Chêne, G., Leport, C., & Commenges, D. (2002) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(02)00017-2 · Cited by 117 · see citing works
Estimation and Prediction in a Multivariate Random Effects Generalized Linear Model
Reinsel, G. C. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478064 · Cited by 116 · see citing works
Pseudolikelihood Modeling of Multivariate Outcomes in Developmental Toxicology
Geys, H., Molenberghs, G., & RYAN, L. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474176 · Cited by 115 · see citing works
Self-citationLatent Variable Models for Longitudinal Data with Multiple Continuous Outcomes
Roy, J. A., & Lin, X. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.01047.x · Cited by 114 · see citing works
Random-effects models for multivariate repeated measures
Fieuws, S., Verbeke, G. N., & Molenberghs, G. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206075305 · Cited by 102 · see citing works
Self-citationA multivariate latent growth curve analysis of adolescent substance use
Duncan, S. C., & Duncan, T. E. (1996) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519609540050 · Cited by 101 · see citing works
Joint modelling of mixed outcome types using latent variables
McCulloch, C. E. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280207081240 · Cited by 100 · see citing works
Spiegelman, D. L., Carroll, R. J., & Kipnis, V. (2000) · Statistics in Medicine · doi:10.1002/1097-0258(20010115)20:1 · Cited by 96 · see citing works
Kiers, H. A. L., & ten Berge, J. M. F. (1994) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1994.tb01027.x · Cited by 95 · see citing works
A Mixed‐Effects Regression Model for Longitudinal Multivariate Ordinal Data
Liu, L. C., & Hedeker, D. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00408.x · Cited by 88 · see citing works
Lambert, P., & Vandenhende, F. (2002) · Statistics in Medicine · doi:10.1002/sim.1249 · Cited by 86 · see citing works
Pseudo-degrees of freedom for complex predictive models: the example of partial least squares
van der Voet, H. (1999) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199905/08)13:3/4 · Cited by 86 · see citing works
Structured Latent Curve Models for the Study of Change in Multivariate Repeated Measures.
Blozis, S. A. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.3.334 · Cited by 83 · see citing works
Nelson, K. P., Lipsitz, S. R., Fitzmaurice, G. M., Ibrahim, J. G., Parzen, M. I., & Strawderman, R. L. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x96854 · Cited by 81 · see citing works
Hierarchical Linear Models for Multivariate Outcomes
Thum, Y. M. (1997) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986022001077 · Cited by 77 · see citing works
A Class of Logistic Regression Models for Multivariate Binary Time Series
Liang, K., & Zeger, S. L. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478789 · Cited by 77 · see citing works
A Composite Likelihood Approach to Semivariogram Estimation
Curriero, F. C., & Lele, S. R. (1999) · Journal of Agricultural Biological and Environmental Statistics · doi:10.2307/1400419 · Cited by 76 · see citing works
A likelihood reformulation method in non‐normal random effects models
Liu, L., & Yu, Z. (2007) · Statistics in Medicine · doi:10.1002/sim.3153 · Cited by 75 · see citing works
Latent Transition Modeling of Progression of Health-Risk Behavior
Reboussin, B. A., Reboussin, D. M., Liang, K.-Y., & Anthony, J. C. (1998) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3304_2 · Cited by 71 · see citing works
Predicting renal graft failure using multivariate longitudinal profiles
Fieuws, S., Verbeke, G. N., Maes, B., & Vanrenterghem, Y. F. C. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm041 · Cited by 70 · see citing works
Self-citationA review of multivariate longitudinal data analysis
Bandyopadhyay, S., Ganguli, B., & Chatterjee, A. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280209340191 · Cited by 69 · see citing works
Analyzing Bivariate Repeated Measures for Discrete and Continuous Outcome Variables
Rochon, J. (1996) · Biometrics · doi:10.2307/2532914 · Cited by 68 · see citing works
A Stochastic Model for the Analysis of Bivariate Longitudinal AIDS Data
Sy, J. P., Taylor, J. M. G., & Cumberland, W. G. (1997) · Biometrics · doi:10.2307/2533956 · Cited by 65 · see citing works
Have, T. R. T., & Morabia, A. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00085.x · Cited by 56 · see citing works
State‐space models for multivariate longitudinal data of mixed types
Jørgensen, B., Lundbye‐Christensen, S., Song, P. X. ‐., & Sun, L. (1996) · Canadian Journal of Statistics · doi:10.2307/3315747 · Cited by 56 · see citing works
Multivariate longitudinal models for complex change processes
Beckett, L. A., Tancredi, D. J., & Wilson, R. S. (2004) · Statistics in Medicine · doi:10.1002/sim.1712 · Cited by 55 · see citing works
Three‐mode models for multivariate longitudinal data
Oort, F. J. (2001) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711001159429 · Cited by 50 · see citing works
Analysis of Longitudinal Multiple-Source Binary Data Using Generalized Estimating Equations
O’Brien, L. M., & Fitzmaurice, G. M. (2004) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1046/j.0035-9254.2003.05296.x · Cited by 46 · see citing works
Chakraborty, H., Helms, R. W., Sen, P. K., & Cohen, M. S. (2003) · Statistics in Medicine · doi:10.1002/sim.1505 · Cited by 46 · see citing works
Using Covariance Structure Analysis to Model Change over Time
Willett, J. B., & Keiley, M. K. (2000) · Elsevier eBooks · doi:10.1016/b978-012691360-6/50024-0 · Cited by 43 · see citing works
High Dimensional Multivariate Mixed Models for Binary Questionnaire Data
Fieuws, S., Verbeke, G. N., Boen, F., & Delecluse, C. (2006) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2006.00546.x · Cited by 43 · see citing works
Self-citationPseudo-likelihood inference for clustered binary data
Geys, H., Molenberghs, G., & RYAN, L. M. (1997) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929708832075 · Cited by 41 · see citing works
Self-citationOn Fitting Nonlinear Latent Curve Models to Multiple Variables Measured Longitudinally
Blozis, S. A. (2007) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705510709336743 · Cited by 40 · see citing works
A Model for Repeated Measurements of a Multivariate Binary Response
Agresti, A. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473629 · Cited by 38 · see citing works
Estimating a Treatment Effect from Multidimensional Longitudinal Data
Gray, S. M., & Brookmeyer, R. (1998) · Biometrics · doi:10.2307/2533850 · Cited by 37 · see citing works
Three-way component analysis with smoothness constraints
Timmerman, M. E., & Kiers, H. A. L. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(02)00059-2 · Cited by 36 · see citing works
Gray, S. M., & Brookmeyer, R. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474209 · Cited by 32 · see citing works
Longitudinal Data Analysis. Handbooks of Modern Statistical Methods
Molenberghs, G. (2009) · Cited by 31 · see citing works
Self-citationModelling hearing thresholds in the elderly
Morrell, C. H., & Brant, L. J. (1991) · Statistics in Medicine · doi:10.1002/sim.4780100912 · Cited by 28 · see citing works
The analysis of repeated multivariate binary quality of life data: a hierarchical model approach
Ribaudo, H. J., & Thompson, S. G. (2002) · Statistical Methods in Medical Research · doi:10.1191/0962280202sm272ra · Cited by 25 · see citing works
Marginal correlation from an extended random-effects model for repeated and overdispersed counts
Vangeneugden, T., Molenberghs, G., Verbeke, G. N., & Demétrio, C. G. B. (2010) · Journal of Applied Statistics · doi:10.1080/02664760903406405 · Cited by 25 · see citing works
Self-citationA Nonlinear Mixed Effects Model for Latent Variables
Harring, J. R. (2009) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998609332750 · Cited by 24 · see citing works
Transition Models for Multivariate Longitudinal Binary Data
Zeng, L., & Cook, R. J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000000889 · Cited by 24 · see citing works
Joint models for longitudinal data: Introduction and overview
(2008) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420011579-24 · Cited by 22 · see citing works
Multiple Indicator Stationary Time Series Models
Sivo, S. A. (2001) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0804_05 · Cited by 21 · see citing works
A flexible joint modeling framework for longitudinal and time-to-event data with overdispersion
Njagi, E. N., Molenberghs, G., Rizopoulos, D., Verbeke, G. N., Kenward, M. G., Dendale, P., & Willekens, K. (2013) · Statistical Methods in Medical Research · doi:10.1177/0962280213495994 · Cited by 20 · see citing works
Self-citationMarginalized transition random effect models for multivariate longitudinal binary data
İlk, Ö., & Daniels, M. J. (2007) · Canadian Journal of Statistics · doi:10.1002/cjs.5550350110 · Cited by 19 · see citing works
Stochastic three‐mode models for mean and covariance structures
Oort, F. J. (1999) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711099159099 · Cited by 17 · see citing works
Zhang, M., Tsiatis, A. A., Davidian, M., Pieper, K., & Mahaffey, K. W. (2010) · Biostatistics · doi:10.1093/biostatistics/kxq054 · Cited by 17 · see citing works
Self-citationRegression Analysis of Multiple-Source Longitudinal Outcomes: A "Stirling County" Depression Study
Daskalakis, C. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.1.88 · Cited by 16 · see citing works
Joint models for high-dimensional longitudinal data
Fieuws, S., & Verbeke, G. N. (2008) · Chapman & Hall/CRC interdisciplinary statistics series/Interdisciplinary statistics · doi:10.1201/9781420011579.ch16 · Cited by 7 · see citing works
Self-citationFacilitated analysis of data on drug regimen compliance
de Klerk, E., van der Linden, S., van der Heijde, D. M. F. M., & Urquhart, J. (1997) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19970730)16:14 · Cited by 2 · see citing works
Tibshirani (1997) · doi:10.1002/(sici)1097-0258(19970228)16:4
Flagged first, then by citations
1 of the 102 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [70]Arnold B Strauss D Pseudo-likelihood estimation: some examples Sankhya: The Indian Journal of Statistics - Series B 1991 53 233 243
- [83]Kiers H ten Berge J Bro R PARAFAC2-Part 1: A direct fitting algorithm for the PARAFAC2 model Journal of Chemometrics 1999 13 275 294
Cited by
Sorted by citations
Quantification of biological aging in young adults
Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506264112 · Cited by 1,012 · see citing works
Group-based multi-trajectory modeling
Nagin, D., Jones, B., Passos, V. L., & Tremblay, R. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280216673085 · Cited by 824 · see citing works
On the unnecessary ubiquity of hierarchical linear modeling.
McNeish, D., Stapleton, L. M., & Silverman, R. D. (2016) · Psychological Methods · doi:10.1037/met0000078 · Cited by 746 · see citing works
Faria, R., Gomes, M., Epstein, D., & White, I. R. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0193-3 · Cited by 673 · see citing works
Measurement error in geometric morphometrics
Fruciano, C. (2016) · Development Genes and Evolution · doi:10.1007/s00427-016-0537-4 · Cited by 305 · see citing works
van der Nest, G., Passos, V. L., Candel, M. J. J. M., & van Breukelen, G. (2020) · Advances in Life Course Research · doi:10.1016/j.alcr.2019.100323 · Cited by 292 · see citing works
Hickey, G. L., Philipson, P., Jorgensen, A., & Kolamunnage‐Dona, R. (2016) · BMC Medical Research Methodology · doi:10.1186/s12874-016-0212-5 · Cited by 200 · see citing works
Bryan, S., Goldsmith, L. J., Davis, J. C., Hejazi, S., MacDonald, V., McAllister, P., Randall, E., Suryaprakash, N., Wu, A. D., & Sawatzky, R. G. (2018) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-018-2340-z · Cited by 103 · see citing works
A multiphase non-linear mixed effects model: An application to spirometry after lung transplantation
Rajeswaran, J., & Blackstone, E. H. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280214537255 · Cited by 79 · see citing works
Multivariate Covariance Generalized Linear Models
Bonat, W. H., & Jørgensen, B. (2016) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12145 · Cited by 75 · see citing works
Missing data in trial‐based cost‐effectiveness analysis: An incomplete journey
Leurent, B., Gomes, M., & Carpenter, J. R. (2018) · Health Economics · doi:10.1002/hec.3654 · Cited by 59 · see citing works
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
Anderlucci, L., & Viroli, C. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas816 · Cited by 59 · see citing works
van den Beld, A. W., Carlson, O. D., Doyle, M. E., Rizopoulos, D., Ferrucci, L., van der Lely, A. J., & Egan, J. M. (2018) · European Journal of Endocrinology · doi:10.1530/eje-18-0422 · Cited by 51 · see citing works
Montero-Marin, J., Taylor, L., Crane, C., Greenberg, M. T., Ford, T. J., Williams, J. M. G., García-Campayo, J., Sonley, A., Lord, L., Dalgleish, T., et al. (2021) · Journal of Educational Psychology · doi:10.1037/edu0000542 · Cited by 49 · see citing works
Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review
Hickey, G. L., Philipson, P., Jorgensen, A., & Kolamunnage‐Dona, R. (2018) · The International Journal of Biostatistics · doi:10.1515/ijb-2017-0047 · Cited by 47 · see citing works
Functional Models for Time-Varying Random Objects
Dubey, P., & Müller, H. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12337 · Cited by 46 · see citing works
Li, C., Xiao, L., & Luo, S. (2021) · Biometrics · doi:10.1111/biom.13427 · Cited by 45 · see citing works
Patterson, R. E., Marinac, C. R., Natarajan, L., Hartman, S. J., Cadmus‐Bertram, L., Flatt, S. W., Li, H., Parker, B. A., Oratowski-Coleman, J., Villaseñor, A., et al. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.12.009 · Cited by 44 · see citing works
Habyarimana, F. (2016) · African Population Studies · doi:10.11564/30-2-836 · Cited by 43 · see citing works
Hansrivijit, P., Yarlagadda, K., Cheungpasitporn, W., Thongprayoon, C., & Ghahramani, N. (2020) · Journal of Critical Care · doi:10.1016/j.jcrc.2020.10.013 · Cited by 42 · see citing works
Cheney, C. M., Stephens, D. M., Mo, X. M., Rafiq, S., Butchar, J. P., Flynn, J. M., Jones, J. A., Maddocks, K. J., O’Reilly, A., Ramachandran, A., et al. (2014) · mAbs · doi:10.4161/mabs.28282 · Cited by 41 · see citing works
Introduction to the special issue on joint modelling techniques
Rizopoulos, D., & Lesaffre, E. M. E. H. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280212445800 · Cited by 40 · see citing works
Wang, J., Luo, S., & Li, L. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1059 · Cited by 38 · see citing works
Dampier, W., Nonnemacher, M. R., Mell, J. C., Earl, J. P., Ehrlich, G. D., Pirrone, V., Aiamkitsumrit, B., Zhong, W., Kercher, K., Passic, S. R., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155382 · Cited by 36 · see citing works
Estimation Methods for Mixed Logistic Models with Few Clusters
McNeish, D. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1236237 · Cited by 37 · see citing works
Petrovski, B. É., Vetvik, K. G., Lundqvist, C., & Eberhard‐Gran, M. (2018) · The Journal of Headache and Pain · doi:10.1186/s10194-018-0853-3 · Cited by 37 · see citing works
Learning Bayesian Networks from Correlated Data
Bae, H. T., Monti, S., Montano, M. A., Steinberg, M. H., Perls, T. T., & Sebastiani, P. (2016) · Scientific Reports · doi:10.1038/srep25156 · Cited by 35 · see citing works
Accessing the Influence of Perceived Value on Social Attachment: Developing Country Perspective
Wang, Q., Yang, M., & Zhang, W. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.760774 · Cited by 40 · see citing works
Robust identification of temporal biomarkers in longitudinal omics studies
Metwally, A. A., Zhang, T., Wu, S., Kellogg, R., Zhou, W., Contrepois, K., Tang, H., & Snyder, M. P. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac403 · Cited by 35 · see citing works
Martí‐Juan, G., Lorenzi, M., & Piella, G. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119892 · Cited by 35 · see citing works
Multiple Response Variables Regression Models in R: The mcglm Package
Bonat, W. H. (2018) · Journal of Statistical Software · doi:10.18637/jss.v084.i04 · Cited by 35 · see citing works
Arabidopsis phenotyping through geometric morphometrics
Manacorda, C. A., & Asurmendi, S. (2018) · GigaScience · doi:10.1093/gigascience/giy073 · Cited by 33 · see citing works
Albrecht, É., Bayon, V., Hirotsu, C., Jabari, A. A., & Heinzer, R. C. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.07.052 · Cited by 32 · see citing works
Volumetric growth tracking of patient-derived cancer organoids using optical coherence tomography
Gil, D. A., Deming, D. A., & Skala, M. C. (2021) · Biomedical Optics Express · doi:10.1364/boe.428197 · Cited by 33 · see citing works
McClurg, D., Harris, F., Goodman, K., Doran, S., Hagen, S., Treweek, S. P., Norton, C. S., Coggrave, M., Norrie, J. D., Rauchhaus, P., et al. (2018) · Health Technology Assessment · doi:10.3310/hta22580 · Cited by 30 · see citing works
Functional regulation of an outer retina hyporeflective band on optical coherence tomography images
Gao, S., Li, Y., Bissig, D., Cohen, E. D., Podolsky, R. H., Childers, K. L., Vernon, G., Chen, S., Berkowitz, B. A., & Qian, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-89599-1 · Cited by 28 · see citing works
Guaranteed Functional Tensor Singular Value Decomposition
Han, R., Shi, P., & Zhang, A. R. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2153689 · Cited by 27 · see citing works
PrEP Use Among Female Sex Workers: No Evidence for Risk Compensation
Giguère, K., Béhanzin, L., Guédou, F. A., Talbot, D. C., Leblond, F. A., Goma‐Matsétsé, E., Zannou, D. M., Affolabi, D., Kêkê, R. K., Gangbo, F. A., et al. (2019) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000002134 · Cited by 27 · see citing works
Jones, M., Mishra, G. D., & Dobson, A. J. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.03.011 · Cited by 26 · see citing works
Gil, D. A., Deming, D. A., & Skala, M. C. (2021) · Journal of Biomedical Optics · doi:10.1117/1.jbo.26.3.036005 · Cited by 24 · see citing works
Multilevel Multivariate Modelling of Legislative Count Data, with a Hidden Markov Chain
Lagona, F., Maruotti, A., & Padovano, F. (2014) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12089 · Cited by 24 · see citing works
Tackling Longitudinal Round-Robin Data: A Social Relations Growth Model
Nestler, S., Geukes, K., Hutteman, R., & Back, M. D. (2016) · Psychometrika · doi:10.1007/s11336-016-9546-5 · Cited by 24 · see citing works
Early detection and risk assessment for chronic disease with irregular longitudinal data analysis
He, K., Huang, S., & Qian, X. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103231 · Cited by 23 · see citing works
Patson, N., Mukaka, M., Otwombe, K. N., Kazembe, L. N., Mathanga, D. P., Mwapasa, V., Kabaghe, A. N., Eijkemans, M. J. C., Laufer, M. K., & Chirwa, T. F. (2020) · Malaria Journal · doi:10.1186/s12936-020-03190-z · Cited by 22 · see citing works
Modeling Time-Varying Random Objects and Dynamic Networks
Dubey, P., & Müller, H. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1917416 · Cited by 22 · see citing works
Schmutte, T. J., Davidson, L., & O’Connell, M. J. (2017) · Psychiatric Quarterly · doi:10.1007/s11126-017-9516-9 · Cited by 22 · see citing works
The analysis of multivariate longitudinal data using multivariate marginal models
Cho, H. (2015) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2015.10.012 · Cited by 21 · see citing works
Simultaneous inference for multiple marginal generalized estimating equation models
Ristl, R., Hothorn, L. A., Ritz, C., & Posch, M. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280219873005 · Cited by 21 · see citing works
Monitoring therapeutic processes using risk‐adjusted multivariate Tukey's CUSUM control chart
Kazemi, S., Noorossana, R., Rasouli, M., Nayebpour, M. R., & Heidari, K. (2021) · Quality and Reliability Engineering International · doi:10.1002/qre.2891 · Cited by 20 · see citing works
A pairwise interaction model for multivariate functional and longitudinal data
Chiou, J., & Müller, H. (2016) · Biometrika · doi:10.1093/biomet/asw007 · Cited by 20 · see citing works
Early changes in depression predict outcomes of inpatient adolescent anorexia nervosa
Kahn, M., Klomek, A. B., Hadas, A., Snir, A., & Fennig, S. (2019) · Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · doi:10.1007/s40519-019-00686-9 · Cited by 20 · see citing works
Pfledderer, C. D., Bai, Y., Brusseau, T. A., Burns, R. D., & Jensen, J. K. (2022) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2022.101743 · Cited by 19 · see citing works
Multivariate longitudinal data analysis with mixed effects hidden Markov models
Raffa, J. D., & Dubin, J. A. (2015) · Biometrics · doi:10.1111/biom.12296 · Cited by 19 · see citing works
Parsimonious hidden Markov models for matrix-variate longitudinal data
Tomarchio, S. D., Punzo, A., & Maruotti, A. (2022) · Statistics and Computing · doi:10.1007/s11222-022-10107-0 · Cited by 18 · see citing works
Lee, K., Cho, H., Kwak, M., & Jang, E. J. (2019) · Biometrics · doi:10.1111/biom.13113 · Cited by 18 · see citing works
A bivariate logistic regression model based on latent variables
Kristensen, S. B., & Bibby, B. M. (2020) · Statistics in Medicine · doi:10.1002/sim.8587 · Cited by 18 · see citing works
Edwards, L. J., & Simpson, S. L. (2014) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000039 · Cited by 18 · see citing works
Estimating correlation between multivariate longitudinal data in the presence of heterogeneity
Gao, F., Miller, J. P., Xiong, C., Luo, J. R., Beiser, J. A., Chen, L., & Gordon, M. O. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0398-1 · Cited by 17 · see citing works
Rasouli, M., Noorossana, R., & Samimi, Y. (2022) · Quality and Reliability Engineering International · doi:10.1002/qre.3085 · Cited by 17 · see citing works
Rombach, I., Jenkinson, C., Gray, A. M., Murray, D. W., & Rivero‐Arias, O. (2018) · Patient Related Outcome Measures · doi:10.2147/prom.s147790 · Cited by 17 · see citing works
Multidimensional performance assessment of public sector organisations using dominance criteria
Gutacker, N., & Street, A. (2017) · Health Economics · doi:10.1002/hec.3554 · Cited by 17 · see citing works
Longitudinal omics data analysis: approaches and applications
Taheriyoun, A., Ross, A., Safikhani, A., Soudbakhsh, D., & Rahnavard, A. (2026) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2026.01.001 · Cited by 17 · see citing works
Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging
Özalay, Ö., Mediavilla, T., Giacobbo, B. L., Pedersen, R., Marcellino, D. J., Orädd, G., Rieckmann, A., & Sultan, F. R. (2024) · Communications Biology · doi:10.1038/s42003-024-05873-8 · Cited by 17 · see citing works
Pickering, G. P., Morel, V., Joly, D., Villatte, C., Roux, D., Dubray, C., & Pereira, B. (2014) · Trials · doi:10.1186/1745-6215-15-331 · Cited by 16 · see citing works
Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data
Kohli, P., Garcia, T. P., & Pourahmadi, M. (2015) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2015.11.014 · Cited by 15 · see citing works
for the IPPCollapse-II study group, Díaz‐Cambronero, Ó., Mazzinari, G., Errando, C. L., Schultz, M. J., Lorente, B. F., García-Gregorio, N., Montañes, M. V., Robles, D., Arnal, L. E. O., et al. (2019) · Trials · doi:10.1186/s13063-019-3255-1 · Cited by 16 · see citing works
Maruotti, A. (2016) · Environmental and Ecological Statistics · doi:10.1007/s10651-015-0338-3 · Cited by 15 · see citing works
Norman, E. R. V., Maki, K. E., Burns, M. K., McComas, J. J., & Helman, L. A. (2018) · Journal of School Psychology · doi:10.1016/j.jsp.2018.02.002 · Cited by 15 · see citing works
Fischer, H. J., Weiss, R. E., Friedman, A. N., Imam, T. H., & Coleman, K. J. (2020) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2020.11.010 · Cited by 16 · see citing works
Giguère, K., Béhanzin, L., Guédou, F. A., Leblond, F. A., Goma‐Matsétsé, E., Zannou, D. M., Affolabi, D., Kêkê, R. K., Gangbo, F. A., Bachabi, M., et al. (2019) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofz010 · Cited by 15 · see citing works
Penalized Variable Selection for Lipid–Environment Interactions in a Longitudinal Lipidomics Study
Zhou, F., Ren, J., Li, G., Jiang, Y., Li, X., Wang, W., & Wu, C. (2019) · Genes · doi:10.3390/genes10121002 · Cited by 15 · see citing works
Ma, J., Chan, W., & Tilley, B. C. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216639859 · Cited by 15 · see citing works
Sparse Pairwise Likelihood Estimation for Multivariate Longitudinal Mixed Models
Hui, F. K. C., Müller, S., & Welsh, A. H. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1371026 · Cited by 14 · see citing works
Alfò, M., Marino, M. F., Ranalli, M. G., Salvati, N., & Tzavidis, N. (2020) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12452 · Cited by 14 · see citing works
A hidden Markov model for continuous longitudinal data with missing responses and dropout
Pandolfi, S., Bartolucci, F., & Pennoni, F. (2023) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.202200016 · Cited by 14 · see citing works
Pickering, G. P., Pereira, B., Morel, V., Tiberghien-Chatelain, F., Martin, E., Marcaillou, F., Picard, P., Delage, N., de Montgazon, G. B., Sorel, M., et al. (2014) · Contemporary Clinical Trials · doi:10.1016/j.cct.2014.06.004 · Cited by 13 · see citing works
Joint Modeling of Multivariate Longitudinal Data and Competing Risks Using Multiphase Sub-models
Rajeswaran, J., Blackstone, E. H., & Barnard, J. (2018) · Statistics in Biosciences · doi:10.1007/s12561-018-9223-6 · Cited by 13 · see citing works
Martin, E., Morel, V., Joly, D., Villatte, C., Delage, N., Dubray, C., Pereira, B., & Pickering, G. P. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.01.012 · Cited by 12 · see citing works
Modeling Through Latent Variables
Verbeke, G. N., & Molenberghs, G. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054017 · Cited by 12 · see citing works
Self-citationLi, H., Zhang, Y., Carroll, R. J., Keadle, S. K., Sampson, J. N., & Matthews, C. E. (2017) · Statistics in Medicine · doi:10.1002/sim.7401 · Cited by 12 · see citing works
Albrecht, É., Bayon, V., Hirotsu, C., & Heinzer, R. C. (2020) · Anaesthesia · doi:10.1111/anae.15236 · Cited by 12 · see citing works
Repeated measures discriminant analysis using multivariate generalized estimation equations
Brobbey, A., Wiebe, S., Nettel‐Aguirre, A., Josephson, C. B., Williamson, T. S., Lix, L. M., & Sajobi, T. T. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211032705 · Cited by 11 · see citing works
Cao, Y., Allore, H. G., Wyk, B. V., & Gutman, R. (2022) · Statistics in Medicine · doi:10.1002/sim.9592 · Cited by 11 · see citing works
Multivariate $t$ Degradation Processes for Dependent Multivariate Degradation Data
Liu, Q., Jin, L., Ng, H. K. T., Hu, Q., & Yu, D. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3398652 · Cited by 11 · see citing works
A latent variable model for analyzing mixed longitudinal (k,l)-inflated count and ordinal responses
Razie, F., Samani, E. B., & Ganjali, M. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2015.1134448 · Cited by 11 · see citing works
Morel, V., Joly, D., Villatte, C., Pereira, B., & Pickering, G. P. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-017986 · Cited by 11 · see citing works
Multivariate quasi-beta regression models for continuous bounded data
Petterle, R. R., Bonat, W. H., Scarpin, C. T., Jonasson, T. H., & Borba, V. Z. C. (2020) · The International Journal of Biostatistics · doi:10.1515/ijb-2019-0163 · Cited by 11 · see citing works
Maruotti, A., Raponi, V., & Lagona, F. (2016) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.201400121 · Cited by 14 · see citing works
Employing a latent variable framework to improve efficiency in composite endpoint analysis
McMenamin, M. E., Barrett, J., Berglind, A., & Wason, J. M. S. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220970986 · Cited by 11 · see citing works
Dependence modeling of multivariate longitudinal hybrid insurance data with dropout
Frees, E. W., Bolancé, C., Guillen, M., & Valdez, E. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115552 · Cited by 10 · see citing works
“As emphasized by Verbeke et al. (2014), a multivariate modeling strategy is more complex than analyzing univariate outcomes but also provides important insights not available with univariate analysis.”
BackgroundMethodShi, P., & Zhao, Z. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105676 · Cited by 10 · see citing works
Taming the shrewdness of neural function: methodological challenges in computational psychiatry
Dayan, P., Dolan, R. J., Friston, K. J., & Montague, P. R. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.09.009 · Cited by 9 · see citing works
Dawber, J. P., Salvati, N., Fabrizi, E., & Tzavidis, N. (2022) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12953 · Cited by 10 · see citing works
Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data
Petterle, R. R., Bonat, W. H., & Scarpin, C. T. (2019) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-019-00360-8 · Cited by 10 · see citing works
Sparse group variable selection for gene–environment interactions in the longitudinal study
Zhou, F., Xi, L., Ren, J., Fan, K., Ma, S., & Wu, C. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22461 · Cited by 9 · see citing works
Building integral projection models with nonindependent vital rates
Fung, Y. L., Newman, K. B., King, R., & de Valpine, P. (2022) · Ecology and Evolution · doi:10.1002/ece3.8682 · Cited by 10 · see citing works
Multivariate Longitudinal Analysis with Bivariate Correlation Test
Adjakossa, E., Sadissou, I. A., Hounkonnou, M. N., & Nuel, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159649 · Cited by 10 · see citing works
Functional Principal Component Analysis for Extrapolating Multistream Longitudinal Data
Chung, S., & Kontar, R. A. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2020.3035084 · Cited by 3 · see citing works
Ma, X., Li, J., Guo, F., Cui, C., Chen, T., Xv, F., & Wang, W. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.999229 · Cited by 10 · see citing works
Joint models: when are treatment estimates improved?
Hatfield, L. A., Hodges, J. S., & Carlin, B. P. (2014) · Statistics and Its Interface · doi:10.4310/sii.2014.v7.n4.a2 · Cited by 9 · see citing works
- Authors
- Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M.
- Affiliations
- Hasselt University (BE)
- KU Leuven (BE)
- North Carolina State University (US)
- Corresponding author
- Verbeke, Geert
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2012-04-20
- Volume
- 23
- Issue
- 1
- Pages
- 42-59
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- PMC
- PMC3404254
- OpenAlex
- W2004277545
- Cited by
- 290
- References
- 102
- Citations by year
- 2026: 8 · 2025: 25 · 2024: 17 · 2023: 19 · 2022: 30 · 2021: 43 · 2020: 34 · 2019: 26 · 2018: 19 · 2017: 19 · 2016: 20 · 2015: 14 · 2014: 13 · 2013: 3
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Document Server@UHasselt (UHasselt) (submittedVersion, other-oa)
- Lirias (submittedVersion)
- Funders
- National Cancer Institute (R01CA085848, P01CA142538)
- National Institute of Allergy and Infectious Diseases (R37AI031789)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- multivariate longitudinal data, joint modeling, multiple outcomes, repeated measures, longitudinal experiment, model comparison
- MeSH terms
- Hearing, Humans, Longitudinal Studies, Models, Statistical, Multivariate Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
