[R] what is the difference between survival analysis and (...)

[Christos Hatzis]

On the same point, transforming time-to-event data to binary outcomes so
that contingency-table analysis (odds ratios etc) or logistic regression can
be applied will result in loss of information that could lead to misleading
conclusions. 

For example, assuming that there is a good-prognosis group (low risk) and a
poor-prognosis group (high risk) that need to be compared.  By definition,
patients in the good prognosis group are those that have been followed up
for a longer time in the study, whereas patients with poor prognosis will
tend to die earlier.  Therefore censoring will occur later in the good
prognosis group and thus the two groups will not have a homogeneous
censorship structure. In this case, naïve analysis could be misleading.

For more details and a simulation example take a look at  

http://jnci.oxfordjournals.org/cgi/data/99/2/147/DC1/3

HTH

-Christos

Christos Hatzis, Ph.D.
Nuvera Biosciences, Inc.
400 West Cummings Park
Suite 5350
Woburn, MA 01801
Tel: 781-938-3830
www.nuverabio.com
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Lucke, Joseph F
> Sent: Wednesday, March 28, 2007 12:10 PM
> To: Eric Elguero; R-help at stat.math.ethz.ch
> Subject: Re: [R] what is the difference between survival 
> analysis and (...)
> 
> You can (and I have) fit survival data with logistic 
> regression. Agresti (1990, pp 189--196) has an introductory 
> discussion. 
> 
> The issue is whether the occurrence of the event is of 
> interest or whether the time-to-event is of interest. If the 
> study lasts 180 days (as in my case) logistic regression 
> treats an event at 1 day the same as an event at 179 days. 
> Similarly, non-occurrence censored at 5 days is treated the 
> same as non-occurrence censored at 180 days. These 
> assumptions only make sense if the hazard rate is constant 
> and (therefore) the time-to-failure distribution is exponential.
> 
> One can include exposure time as a offset (non-estimated 
> covariate) to handle non-constant hazard rates. One can also 
> model the hazard rate directly as a log-linear model.
> 
> Based on what he said (number events/sample size, using 
> cumulative times), the hostile medical epidemiologist was 
> implicitly assuming the survival time followed an exponential 
> distribution. This assumption is often incorrect.   His 
> arrogance was exceeded only by his ignorance.
> 
> Joe
> 
> @BOOK{Agresti1990,
>   author = {Agresti, Alan},
>   title = {Categorical data analysis},
>   year = {1990},
>   publisher = {John Wiley \& Sons},
>   address = {New York, NY},
>   series = {Wiley Series in Probability and Mathematical Statistics},
>   keywords = {loglinear; logistic}
> }
> 
> 
>  
> 
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Eric Elguero
> Sent: Wednesday, March 28, 2007 8:40 AM
> To: R-help at stat.math.ethz.ch
> Subject: Re: [R] what is the difference between survival 
> analysis and (...)
> 
> Hi everybody,
> 
> recently I had to teach a course on Cox model, of which I am 
> not a specialist, to an audience of medical epidemiologists.
> Not a good idea you might say.. anyway, someone in the 
> audience was very hostile. At some point, he sayed that Cox 
> model was useless, since all you have to do is count who dies 
> and who survives, divide by the sample sizes and compute a 
> relative risk, and if there was significant censoring, use 
> cumulated follow-up instead of sample sizes and that's it!
> I began arguing that in Cox model you could introduce several 
> variables, interactions, etc, then I remembered of logistic 
> models ;-) The only (and poor) argument I could think of was 
> that if mr Cox took pains to devise his model, there should 
> be some reason...
> 
> but the story doesn't end here. When I came back to my 
> office, I tried these two methods on a couple of data sets, 
> and true, crude RRs are very close to those coming from Cox model.
> 
> hence this question: could someone provide me with a dataset 
> (preferably real) where there is a striking difference 
> between estimated RRs and/or between P-values? and of course 
> I am interested in theoretical arguments and references.
> 
> sorry that this question has nothing to do with R and thank 
> you in advance for your leniency.
> 
> Eric Elguero
> GEMI-UMR 2724 IRD-CNRS,
> Équipe "Évolution des Systèmes Symbiotiques"
> 911 avenue Agropolis, BP 64501,
> 34394 Montpellier cedex 5 FRANCE
> 
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> ______________________________________________
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> PLEASE do read the posting guide 
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> and provide commented, minimal, self-contained, reproducible code.
> 
>