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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Survival analysis gurus |

Date |
Wed, 27 Feb 2008 12:16:09 +0000 (GMT) |

question 1: Cox regression is pretty typical in these kinds of studies. In Stata this is implemented as -stcox-. question 2: If you continue to follow respondents that move than that is not a problem but actually a bonus, especially if they move from institions that are a control to an institution that are a treatment or vice versa. This allows you to add your treatment dummy as a so called time varying covariate. If you do not follow them than that is a problem. Survival analysis is designed for dealing with respondents that leave the study without experiencing the event (called right censoring), but it assumes that this the probability of being right censored is unrelated to experiencing the event. I have known a PhD student studying the effect of friendship networks on health events in elderly people who was forced to abondon that research because of the same problem you just described (they did not follow the respondents when they moved). question 3: I would add age as a time varying control variable. One question you have not asked is how to deal with the fact that your respondents are nested within institutions. -stcox- allows for this using the -shared- option. When you are doing such an analysis you will want to have a good book on your desk. A book I have found very useful is "An introduction to survival analysis using Stata" by Mario Cleves, William Gould, and Roberto Gutierrez: http://www.stata-press.com/books/saus.html Hope this helps, Maarten --- Richard Gibson <Richard.Gibson@newcastle.edu.au> wrote: > Survival analysis is not my usual territory and so I would appreciate > some advice. > > I have data on around 5,700 residents of aged care facilities > enrolled into a study on the effect of an intervention delivered to > the facilities for the benefit of residents. There were around 90 > facilities randomised to control or intervention. We wish to assess > the effect of the intervention in preventing certain events. The > analysis plan included a survival analysis. We can resolve time to > days (or hours/minutes with some assumptions) over a period of 18 > months of observation. > > Part of my problem is that about 35% (I am guessing - the data are at > work - but it was a lot) of the residents either changed facility > (often moving to higher care) or had time out from the facility (eg > hospitalisation) or both. The other part is that those residents who > changed facility, especially from low to high care were much more > likely to experience the events of interest; presumably they were > moved having been assessed as being at higher risk. If I were to say > that those who changed facility were no longer under observation then > I loose about 60% (again a guestimate - but reasonable) of events! > > Complicating matters is that residents who moved may have moved from > an intervention facility to a control facility or vice versa. Those > moving from intervention to control could carry some of the impact of > the intervention to the new facility - eg fitness or medication > regimen, while those moving from a control facility to an > intervention facility may obtain new benefit from the change (caveat: > providing the intervention is working). Residents could move in and > out of the same facility several times or change locations completely > several times. There is also a wide age range to account for with > older residents being at greater risk of experiencing any event. > > My questions are: > > 1. What would be the best way to estimate the impact or not of the > intervention? > > 2. How do/can I treat resident movements in a survival analysis? > > 3. As age is a risk factor, should age be the measure of time? > > With respect to question 1, I am thinking that a logistic regression > with the outcome being the event of interest and having several > covariates describing movements could be one appropriate analysis - > but I am not sure that would be best. ... > > I am not sure how to answer question 2. I have done some reading > around but nothing has lept out at me yet. One idea is to model > final facility adjusting for origin (intervention or control and > relative time), number of movements in and out of hospital and say > number of facility changes. Another idea is to model initial > facility, time in a control facility, time in an intervention > facility, number of hospitalisations (ignoring time out). So many > options, but they may be naive. > > Advice and suggestions will be greatly appreciated. > > Richard > > > > * > * 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/ > ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- ___________________________________________________________ Rise to the challenge for Sport Relief with Yahoo! For Good http://uk.promotions.yahoo.com/forgood/ * * 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/

**Follow-Ups**:**Re: st: Survival analysis gurus***From:*Richard Gibson <Richard.Gibson@newcastle.edu.au>

**References**:**st: Survival analysis gurus***From:*Richard Gibson <Richard.Gibson@newcastle.edu.au>

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