Friday, February 24, 2006
RE: st: informative censoring
Thank you very much!
At 01:15 PM 2/9/2006, you wrote: >Michael > >If you are willing to assume a parametric model, say F(t) = P(T < t) >where T = time to event, you could impose another model for informative >censoring conditional on the (unobserved) value of T: P(censored|T = t) >= g(t) where g(t) and F(t) are known up to a set of parameters and also >could depend on covariates. Then you could attempt to estimate the >parameters by maximimum likehood. You would have to write your own >ml-program to do this. I have done something similar to this for >informative dropout (not a time-to-event model). See > >Feiveson, A. H., Metter, E. J., and Paloski, W. H. (2002) "A Statistical >Model for Interpreting Computerized Dynamic Posturography Data", IEEE >Transactions in Biomedical Engineering 49, 300-309. > > >Al Feiveson > > >-----Original Message----- >From: email@example.com >[mailto:firstname.lastname@example.org] On Behalf Of Maarten buis >Sent: Thursday, February 09, 2006 1:21 PM >To: email@example.com >Subject: Re: st: informative censoring > >Michael: >Informative censoring means that the fact that you are censored tells >you something about the hazard rate that you will experience the event >you study. For instance if you study how long elderly people live in a >panel, and respondents choose not to participate in the panel ones they >get gravely ill, than the fact that they are censored tells you >something about the hazard of experience the event (die). If on the >other hand people are censored because you decided to stop collecting >data after five years in the field, than the fact that they are censored >is likely to be unrelated to the probability of experiencing the event. > >So deciding that censoring is informative or not is usually done using >information outside the data. It is very hard to draw such conclusions >form the data itself, since in order to know whether the censoring tells >you something about the hazard you need to have data about your >respondents after they were censored, and if you had that than they >wouldn't be censored... > >HTH, >Maarten > >--- Michael McCulloch <firstname.lastname@example.org> wrote: > > Are there methods in stata for assessing informative censoring, i.e. > > whether it is present, and how to estimate the survival function if > > informative censoring is present. > > >----------------------------------------- >between 1/2/2006 and 31/3/2006 I will be visiting the UCLA, during this >time the best way to reach me is by email > >Maarten L. Buis >Department of Social Research Methodology Vrije Universiteit Amsterdam >Boelelaan 1081 >1081 HV Amsterdam >The Netherlands > >visiting adress: >Buitenveldertselaan 3 (Metropolitan), room Z214 > >+31 20 5986715 > >http://home.fsw.vu.nl/m.buis/ >----------------------------------------- > > > >___________________________________________________________ >To help you stay safe and secure online, we've developed the all new >Yahoo! Security Centre. http://uk.security.yahoo.com >* >* For searches and help try: >* http://www.stata.com/support/faqs/res/findit.html >* http://www.stata.com/support/statalist/faq >* http://www.ats.ucla.edu/stat/stata/ > >* >* For searches and help try: >* http://www.stata.com/support/faqs/res/findit.html >* http://www.stata.com/support/statalist/faq >* http://www.ats.ucla.edu/stat/stata/
Best wishes, Michael
Michael McCulloch Pine Street Clinic 124 Pine Street, San Anselmo, CA 94960-2674 tel 415.407.1357 fax 415.485.1065 email: email@example.com web: www.pinest.org www.pinestreetfoundation.org
* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/
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