Estimating time to event from longitudinal categorical data: An analysis of multiple sclerosis progression

Micha Mandel; Susan A. Gauthier; Charles R.G. Guttmann; Howard L. Weiner; Rebecca A. Betensky

doi:10.1198/016214507000000059

Estimating time to event from longitudinal categorical data: An analysis of multiple sclerosis progression

Micha Mandel^*, Susan A. Gauthier, Charles R.G. Guttmann, Howard L. Weiner, Rebecca A. Betensky

^*Corresponding author for this work

Department of Statistics

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing EDSS by one point (relative progression). Survival methods for time to progression are not adequate for such data because they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large-sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase EDSS by at least one point, and time to two consecutive visits with EDSS greater than 3 are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners Multiple Sclerosis Center in Boston. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than 3 and calculate crude (without covariates) and covariate-specific curves.

Original language	English
Pages (from-to)	1254-1266
Number of pages	13
Journal	Journal of the American Statistical Association
Volume	102
Issue number	480
DOIs	https://doi.org/10.1198/016214507000000059
State	Published - Dec 2007

Bibliographical note

Funding Information:
Micha Mandel is Lecturer, Department of Statistics, Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel (E-mail: [email protected]). Susan A. Gauthier is Associate Neurologist, Partners Multiple Sclerosis Center, Brigham and Women’s Hospital, and Instructor of Neurology, Harvard Medical School, Boston, MA 02115. Charles R. G. Guttmann is Director of the Center for Neurological Imaging, Brigham and Women’s Hospital, and Assistant Professor in Radiology, Harvard Medical School, Boston, MA 02115. Howard L. Weiner is Director of the Partners Multiple Sclerosis Center and a co-director of the Center for Neurological Diseases, Brigham and Women’s Hospital, and Robert L. Kroc Professor of Neurology, Harvard Medical School, Boston, MA 02115. Rebecca A. Betensky is Professor, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115. This research was supported in part by NIH CA075971, the Harvard Center for Neurodegeneration and Repair (HCNR), the Nancy Davis Center Without Walls, and the Partners Multiple Sclerosis Center. The authors thank Brent Coull, the editor, the associate editor, and three anonymous referees for helpful comments and suggestions.

Keywords

Markov model
Multistate model
Ordinal response
Pointwise confidence interval
Survival curve
Time series
Transition model

Access to Document

10.1198/016214507000000059

Cite this

@article{fa22eb820b294bd59131c3859b40b7b9,

title = "Estimating time to event from longitudinal categorical data: An analysis of multiple sclerosis progression",

abstract = "The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing EDSS by one point (relative progression). Survival methods for time to progression are not adequate for such data because they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large-sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase EDSS by at least one point, and time to two consecutive visits with EDSS greater than 3 are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners Multiple Sclerosis Center in Boston. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than 3 and calculate crude (without covariates) and covariate-specific curves.",

keywords = "Markov model, Multistate model, Ordinal response, Pointwise confidence interval, Survival curve, Time series, Transition model",

author = "Micha Mandel and Gauthier, {Susan A.} and Guttmann, {Charles R.G.} and Weiner, {Howard L.} and Betensky, {Rebecca A.}",

note = "Funding Information: Micha Mandel is Lecturer, Department of Statistics, Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel (E-mail: [email protected]). Susan A. Gauthier is Associate Neurologist, Partners Multiple Sclerosis Center, Brigham and Women{\textquoteright}s Hospital, and Instructor of Neurology, Harvard Medical School, Boston, MA 02115. Charles R. G. Guttmann is Director of the Center for Neurological Imaging, Brigham and Women{\textquoteright}s Hospital, and Assistant Professor in Radiology, Harvard Medical School, Boston, MA 02115. Howard L. Weiner is Director of the Partners Multiple Sclerosis Center and a co-director of the Center for Neurological Diseases, Brigham and Women{\textquoteright}s Hospital, and Robert L. Kroc Professor of Neurology, Harvard Medical School, Boston, MA 02115. Rebecca A. Betensky is Professor, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115. This research was supported in part by NIH CA075971, the Harvard Center for Neurodegeneration and Repair (HCNR), the Nancy Davis Center Without Walls, and the Partners Multiple Sclerosis Center. The authors thank Brent Coull, the editor, the associate editor, and three anonymous referees for helpful comments and suggestions.",

year = "2007",

month = dec,

doi = "10.1198/016214507000000059",

language = "אנגלית",

volume = "102",

pages = "1254--1266",

journal = "Journal of the American Statistical Association",

issn = "0162-1459",

publisher = "Taylor and Francis Ltd.",

number = "480",

}

TY - JOUR

T1 - Estimating time to event from longitudinal categorical data

T2 - An analysis of multiple sclerosis progression

AU - Mandel, Micha

AU - Gauthier, Susan A.

AU - Guttmann, Charles R.G.

AU - Weiner, Howard L.

AU - Betensky, Rebecca A.

N1 - Funding Information: Micha Mandel is Lecturer, Department of Statistics, Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel (E-mail: [email protected]). Susan A. Gauthier is Associate Neurologist, Partners Multiple Sclerosis Center, Brigham and Women’s Hospital, and Instructor of Neurology, Harvard Medical School, Boston, MA 02115. Charles R. G. Guttmann is Director of the Center for Neurological Imaging, Brigham and Women’s Hospital, and Assistant Professor in Radiology, Harvard Medical School, Boston, MA 02115. Howard L. Weiner is Director of the Partners Multiple Sclerosis Center and a co-director of the Center for Neurological Diseases, Brigham and Women’s Hospital, and Robert L. Kroc Professor of Neurology, Harvard Medical School, Boston, MA 02115. Rebecca A. Betensky is Professor, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115. This research was supported in part by NIH CA075971, the Harvard Center for Neurodegeneration and Repair (HCNR), the Nancy Davis Center Without Walls, and the Partners Multiple Sclerosis Center. The authors thank Brent Coull, the editor, the associate editor, and three anonymous referees for helpful comments and suggestions.

PY - 2007/12

Y1 - 2007/12

N2 - The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing EDSS by one point (relative progression). Survival methods for time to progression are not adequate for such data because they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large-sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase EDSS by at least one point, and time to two consecutive visits with EDSS greater than 3 are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners Multiple Sclerosis Center in Boston. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than 3 and calculate crude (without covariates) and covariate-specific curves.

AB - The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing EDSS by one point (relative progression). Survival methods for time to progression are not adequate for such data because they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large-sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase EDSS by at least one point, and time to two consecutive visits with EDSS greater than 3 are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners Multiple Sclerosis Center in Boston. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than 3 and calculate crude (without covariates) and covariate-specific curves.

KW - Markov model

KW - Multistate model

KW - Ordinal response

KW - Pointwise confidence interval

KW - Survival curve

KW - Time series

KW - Transition model

UR - http://www.scopus.com/inward/record.url?scp=34250304796&partnerID=8YFLogxK

U2 - 10.1198/016214507000000059

DO - 10.1198/016214507000000059

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:34250304796

SN - 0162-1459

VL - 102

SP - 1254

EP - 1266

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 480

ER -

Estimating time to event from longitudinal categorical data: An analysis of multiple sclerosis progression

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this