I am trying to model data in which subjects are followed through time to determine if they fall, or do not fall. Some of the subjects fall once, some fall several times. Follow-up time varies from subject to subject. I know how to model time to the first fall (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model the data if I include the data for those subjects who fall more than once. I would appreciate suggestions about a models that I could use, how I would quantify the follow-up time, how I account for the imbalance in the data (some subjects would contribute one outcome measure, others multiple measures), etc. Many thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC and University of Maryland School of Medicine Claude Pepper OAIC University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 410-605-7119 - NOTE NEW EMAIL ADDRESS: jsorkin@grecc.umaryland.edu [[alternative HTML version deleted]]
Kjetil Brinchmann Halvorsen
2005-Jul-29 13:24 UTC
[R] Binary outcome with non-absorbing outcome state
John Sorkin wrote:>I am trying to model data in which subjects are followed through time to >determine if they fall, or do not fall. Some of the subjects fall once, >some fall several times. Follow-up time varies from subject to subject. >I know how to model time to the first fall (e.g. Cox Proportional >Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model >the data if I include the data for those subjects who fall more than >once. I would appreciate suggestions about a models that I could use, >how I would quantify the follow-up time, how I account for the imbalance >in the data (some subjects would contribute one outcome measure, others >multiple measures), etc. > >Many thanks, >John > >John Sorkin M.D., Ph.D. >Chief, Biostatistics and Informatics >Baltimore VA Medical Center GRECC and >University of Maryland School of Medicine Claude Pepper OAIC > >University of Maryland School of Medicine >Division of Gerontology >Baltimore VA Medical Center >10 North Greene Street >GRECC (BT/18/GR) >Baltimore, MD 21201-1524 > >410-605-7119 >-- NOTE NEW EMAIL ADDRESS: >jsorkin at grecc.umaryland.edu > > [[alternative HTML version deleted]] > >______________________________________________ >R-help at stat.math.ethz.ch mailing list >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > > > > >help.search("recurrent") leads to CRAN pakage survrec. You couls also have a look at CRAN package eha and at Lindsey's package event -- Kjetil Halvorsen. Peace is the most effective weapon of mass construction. -- Mahdi Elmandjra -- No virus found in this outgoing message. Checked by AVG Anti-Virus.
Frank E Harrell Jr
2005-Jul-29 13:25 UTC
[R] Binary outcome with non-absorbing outcome state
John Sorkin wrote:> I am trying to model data in which subjects are followed through time to > determine if they fall, or do not fall. Some of the subjects fall once, > some fall several times. Follow-up time varies from subject to subject. > I know how to model time to the first fall (e.g. Cox Proportional > Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model > the data if I include the data for those subjects who fall more than > once. I would appreciate suggestions about a models that I could use, > how I would quantify the follow-up time, how I account for the imbalance > in the data (some subjects would contribute one outcome measure, others > multiple measures), etc. > > Many thanks, > JohnA great reference for this is @Book{the00mod, author = {Therneau, Terry and Grambsch, Patricia}, title = {Modeling Survival Data: Extending the Cox Model}, publisher = {Springer-Verlag}, year = 2000, address = {New York} } Frank> > John Sorkin M.D., Ph.D. > Chief, Biostatistics and Informatics > Baltimore VA Medical Center GRECC and > University of Maryland School of Medicine Claude Pepper OAIC > > University of Maryland School of Medicine > Division of Gerontology > Baltimore VA Medical Center > 10 North Greene Street > GRECC (BT/18/GR) > Baltimore, MD 21201-1524 > > 410-605-7119 > -- NOTE NEW EMAIL ADDRESS: > jsorkin at grecc.umaryland.edu > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University
Frank Funderburk
2005-Jul-29 13:43 UTC
[R] Binary outcome with non-absorbing outcome state
Singer & Willett (2003) also cover this ground. Singer, JD & Willett, JB (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New Yok: Oxford University Press. -----Original Message----- From: Frank E Harrell Jr <f.harrell at vanderbilt.edu> Sent: Jul 29, 2005 9:25 AM To: John Sorkin <jsorkin at grecc.umaryland.edu> Cc: R-help at stat.math.ethz.ch Subject: Re: [R] Binary outcome with non-absorbing outcome state John Sorkin wrote:> I am trying to model data in which subjects are followed through time to > determine if they fall, or do not fall. Some of the subjects fall once, > some fall several times. Follow-up time varies from subject to subject. > I know how to model time to the first fall (e.g. Cox Proportional > Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model > the data if I include the data for those subjects who fall more than > once. I would appreciate suggestions about a models that I could use, > how I would quantify the follow-up time, how I account for the imbalance > in the data (some subjects would contribute one outcome measure, others > multiple measures), etc. > > Many thanks, > JohnA great reference for this is @Book{the00mod, author = {Therneau, Terry and Grambsch, Patricia}, title = {Modeling Survival Data: Extending the Cox Model}, publisher = {Springer-Verlag}, year = 2000, address = {New York} } Frank> > John Sorkin M.D., Ph.D. > Chief, Biostatistics and Informatics > Baltimore VA Medical Center GRECC and > University of Maryland School of Medicine Claude Pepper OAIC > > University of Maryland School of Medicine > Division of Gerontology > Baltimore VA Medical Center > 10 North Greene Street > GRECC (BT/18/GR) > Baltimore, MD 21201-1524 > > 410-605-7119 > --- NOTE NEW EMAIL ADDRESS: > jsorkin at grecc.umaryland.edu > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help at stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Frank Funderburk Converting Data to ... Information for Action .........................Through Understanding frankfunder at earthlink.net voice mail: 888-431-7594
John Sorkin <jsorkin <at> grecc.umaryland.edu> writes:> > I am trying to model data in which subjects are followed through time to > determine if they fall, or do not fall. Some of the subjects fall once, > some fall several times. Follow-up time varies from subject to subject.Chapter 4.3 in http://www.mayo.edu/hsr/people/therneau/survival.ps might also help. Dieter
On Fri, 29 Jul 2005, John Sorkin wrote:> I am trying to model data in which subjects are followed through time to > determine if they fall, or do not fall. Some of the subjects fall once, > some fall several times. Follow-up time varies from subject to subject. > I know how to model time to the first fall (e.g. Cox Proportional > Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model > the data if I include the data for those subjects who fall more than > once.Various people have already given references that deal with marginal Cox models. I'd second Frank Harrell's recommendation of Therneau & Grambsch. Computationally this is very straightforward: each person has multiple records corresponding to the times between events, and in a Cox model you add +cluster(id) to the model formula to get the right standard errors, where id is unique identifier for individuals. The difficult part is deciding which person-time to compare: eg should someone who has recently had a second event at time 500 be compared to other people who have recently had a second event, other people who have recently had any sort of event, other people at time 500, etc. Another possibility is frailty models, the analogue of generalized linear mixed models. As with GLMMs, even fitting these is tricky and statistical theory isn't that well-developed. The survival package does have an implementation, though. -thomas
Frank Funderburk a ??crit :> Singer & Willett (2003) also cover this ground. > > Singer, JD & Willett, JB (2003). Applied longitudinal data analysis: > Modeling change and event occurrence. New Yok: Oxford University > Press. > > -----Original Message----- From: Frank E Harrell Jr > <f.harrell at vanderbilt.edu> Sent: Jul 29, 2005 9:25 AM To: John Sorkin > <jsorkin at grecc.umaryland.edu> Cc: R-help at stat.math.ethz.ch Subject: > Re: [R] Binary outcome with non-absorbing outcome state > > John Sorkin wrote: > >> I am trying to model data in which subjects are followed through >> time to determine if they fall, or do not fall. Some of the >> subjects fall once, some fall several times. Follow-up time varies >> from subject to subject. I know how to model time to the first fall >> (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I >> am not sure how I can model the data if I include the data for >> those subjects who fall more than once. I would appreciate >> suggestions about a models that I could use, how I would quantify >> the follow-up time, how I account for the imbalance in the data >> (some subjects would contribute one outcome measure, others >> multiple measures), etc. >> >> Many thanks, John > > > A great reference for this is > > @Book{the00mod, author = {Therneau, Terry and Grambsch, > Patricia}, title = {Modeling Survival Data: Extending > the Cox Model}, publisher = {Springer-Verlag}, year > 2000, address = {New York} } > > Frank > > >> >> John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics >> Baltimore VA Medical Center GRECC and University of Maryland School >> of Medicine Claude Pepper OAIC >> >> University of Maryland School of Medicine Division of Gerontology >> Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) >> Baltimore, MD 21201-1524 >> >> 410-605-7119 ---- NOTE NEW EMAIL ADDRESS: >> jsorkin at grecc.umaryland.eduAnother possibility is to discretize the time, group the observations by covariate pattern and use a beta-binomial model (accounting for possible overdispersion caused by the within-subject repeated events) with a cloglog link. When time interval is short (e.g., 1 day), this is equivalent to a Cox prpoprtional hazards model. See: Prentice, R.L., Gloeckler, L.A., 1978. Regression analysis of grouped survival data with application to breast cancer data. Biometrics, 34: 57-67. and Prentice, R.L., 1986. Binary regression using an extended beta-binomial distribution, with discussion of correlation induced by covariate measurement errors. J.A.S.A. 81, 321-327. and subsequent papers. Function betabin in package aod (among others) allows to fit such models. Best, Renaud -- Dr Renaud Lancelot, v??t??rinaire Projet FSP r??gional ??pid??miologie v??t??rinaire C/0 Ambassade de France - SCAC BP 834 Antananarivo 101 - Madagascar e-mail: renaud.lancelot at cirad.fr tel.: +261 32 40 165 53 (cell) +261 20 22 665 36 ext. 225 (work) +261 20 22 494 37 (home)