Displaying 20 results from an estimated 10000 matches similar to: "Survival, should I use (start,stop) and how?"
2012 Apr 23
0
R-help Digest, Vol 110, Issue 23
Yes, the (start, stop] formalism is the easiest way to deal with time
dependent data.
Each individual only needs to have sufficient data to describe them, so
for if id number 4 is in house 1, their housemate #1 was eaten at time
2, and the were eaten at time 10, the following is sufficient data for
that subject:
id house time1 time2 status discovered
4 1 0 2
2011 Mar 10
2
Not sure how to handle hazard in my survival model
Hi R experts :)
I'm trying to carry out a survival model on my data, but I am unsure of
whether it's appropriate or if I should do something specific in regards to
hazard.
My data is time to death by predator where I have 8 prey and one predator in
the setting. This means that two prey can't possibly die at the same time
and I can't quite get my head around how to include this in
2012 Oct 30
0
Checking for different hazard distributions in interval censored data
Hi all!
I have two survival data sets looking at similar effects in different settings. One data set is only right censored, but the other is interval censored. In the right censored data set, there is an effect of one factor that causes very different shapes in survival curves (and non-proportional hazards) and I'd really like to say that the same factor has a similar effect in the other
2011 Feb 03
1
boostrap an nls regression
Hello there
I have the following model based on the hollings disc equation for the type
II functional response for 2 data sets:
nls(eaten~(a*suppl)/(1+a*h*suppl)
where eaten is the number of prey eaten by a predator and suppl is the
number of prey initially supplied to the same predator.
I have parameter estimates of 'a' and 'h' for the two populations studied
and would like
2005 Jul 19
1
ROC curve with survival data
Hi everyone,
I am doing 5 years mortality predictive index score with survival analysis using a Cox proportional hazard model where I have a continous predictive variable and a right censored response which is the mortality, and the individuals were followed a maximum of 7 years.
I'd like to asses the discrimination ability of survival analysis Cox model by computing a ROC curve and area
2012 Jun 07
3
- detecting outliers
Hello all,
I am estimating parameters for regression functions on experimental data.
Functional response of Rogers type II.
I would like to know which points of my dataset are outliers. What is the
best method to do this with R?
I found a method via R help, but would like to know if there are better
methods for my purpose.
Here is the script I us now:
library("mvoutlier")
dat
2000 Oct 26
1
competing risks survival analysis
I will have data in the following form:
Time resp type stim type
300 a A
200 b A
155 a B
250 b B
80 c A
1000 d B
...
c is left censored observation; d is right censored
This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of
Survival Data under the name
2010 Jul 07
1
Appropriateness of survdiff {survival} for non-censored data
I read through Harrington and Fleming (1982) but it is beyond my
statistical comprehension. I have survival data for insects that have
a very finite expiration date. I'm trying to test for differences in
survival distributions between different groups. I understand that
the medical field is most often dealing with censored data and that
survival analysis, at least in the package survival,
2010 Sep 16
1
Survival Analysis Daily Time-Varying Covariate but Event Time Unknown
Help!
I am unsure if I can analyze data from the following experiment.
Fish were placed in a tank at (t=0)
Measurements of Carbon Dioxide were taken each day for 120 days (t=0,...120)
A few fish were then randomly pulled out of the tank at different days,
killed and examined for the presence of a disease
T= time of examination in days from start (i.e. 85th day), E = 0/1 for
nonevent/event
My
2012 Nov 07
2
R: net reclassification index after Cox survival analysis
Dear all,
I am interested to evaluate reclassification using net
reclassification improvement and Integrated Discrimination Index IDI after
survival analysis (Cox proportional hazards using stcox). I search a R
package or a R code that specifically addresses the categorical NRI for
time-to-event data in the presence of censored observation and, if
possible, at different follow-up time points.
I
2012 May 30
0
Survival with different probabilities of censoring
Dear all
I have a fairly funky problem that I think demands some sort of survival
analysis. There are two Red List assessments for mammals: 1986 and 2008.
Some mammals changed their Red List status between those dates. Those
changes can be regarded as "events" and are "interval censored" in the sense
that we don't know at what point between 1986 and 2008 each species
2007 Jun 29
0
GAM for censored data? (survival analysis)
First let me admit that I am no statistician... rather, an ecologist with
just enough statistical knowledge to be dangerous.
I've got a dataset with percent ground cover values for species and other
entities. The data are left censored at zero, in that percent ground cover
cannot be negative. (My data rarely reach 100% cover so I haven't bothered
with adding a right censoring at 100).
2008 Mar 12
1
survival analysis and censoring
In your particular case I don't think that censoring is an issue, at least not
for the reason that you discuss. The basic censoring assumption in the Cox
model is that subjects who are censored have the same future risk as those who
were a. not censored and b. have the same covariates.
The real problem with informative censoring are the covaraites that are not
in the model; ones that
2011 Jun 07
0
WinBUGS on survival, simple but confusing question
Hi All,
I'm using WinBUGS on a very simple survival model (log-normal with one
covariate "Treat"), but I cannot understand the way it handles censored
data. I'm posting the R file which generates the data from pre-specified
parameters, as well as the .bug file.
The question is, if I use NA to denote the censored data (as suggested by
the example Mice in WinBUGS Example Vol.I),
2008 Mar 02
1
Problem plotting curve on survival curve (something silly?)
OK this is bound to be something silly as I'm completely new to R -
having started using it yesterday. However I am already warming to its
lack of 'proper' GUI... I like being able to rerun a command by editing
one parameter easily... try and do that in a Excel Chart Wizzard!
I eventually want to use it to analyse some chemotherapy response /
survival data. That data will not be
2002 Jul 30
2
Questions concerning survival analysis
Good morning everyone (or maybe good evening)
Is there a counterpart to the s-plus function "probplot" (which
provides a qq-plot for "survreg"-objects)? Or do exist other
(rather simple) possibilities to check the assumptions of the
distribution?
I have another question to the author(s) of summary.survreg:
Why does summary(...,times=sort(x)) not give the same result as
2009 May 29
1
final value of nnet with censored=TRUE for survival analysis
Hi there,
I´ve a question concerning the nnet package in the area of survival analysis: what is the final value, which is computed to fit the model with the following nnet-c
all:
net <- nnet(cat~x,
data=d,
size=2,
decay=0.1,
censored=TRUE,
maxit=20,
Wts=rep(0,22),
Hess=TRUE)
where cat is a matrix with a row for each record and
2005 Sep 07
1
Survival analysis with COXPH
Dear all,
I would have some questions on the coxph function for survival analysis,
which I use with frailty terms.
My model is:
mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'),
data)
I have a very large proportion of censored observations.
- If I understand correctly, the function mdcox$frail will return the random
effect estimated for each group on the
2012 May 11
2
survival analysis simulation question
Hi,
I am trying to simulate a regression on survival data under a few
conditions:
1. Under different error distributions
2. Have the error term be dependent on the covariates
But I'm not sure how to specify either conditions. I am using the Design
package to perform the survival analysis using the survreg, bj, coxph
functions. Any help is greatly appreciated.
This is what I have so far:
2012 Feb 07
2
Actual vs. predicted survival times
Dear R-help,
I am using R 2.14.1 on Windows 7.
I would like to produce a plot like the attached - although simplified to actual vs. Predicted survival time with distinguishing marks for censored and observed points. I have a dataset and have fitted a Cox model to it. In an attempt to visualise how accurate the model is it would be ideal if I could plot the actual survival times against the