Displaying 20 results from an estimated 10000 matches similar to: "missing values and survival analysis"
2003 Dec 08
1
Design functions after Multiple Imputation
I am a new user of R for Windows, enthusiast about the many functions
of the Design and Hmisc libraries.
I combined the results of a Cox regression model after multiple imputation
(of missing values in some covariates).
Now I got my vector of coefficients (and of standard errors).
My question is: How could I use directly that vector to run programs such
as 'nomogram', 'calibrate',
2009 Apr 24
1
Multiple Imputation in mice/norm
I'm trying to use either mice or norm to perform multiple imputation to fill
in some missing values in my data. The data has some missing values because
of a chemical detection limit (so they are left censored). I'd like to use
MI because I have several variables that are highly correlated. In SAS's
proc MI, there is an option with which you can limit the imputed values that
are
2011 Mar 31
2
fit.mult.impute() in Hmisc
I tried multiple imputation with aregImpute() and
fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1.
The warning message below suggests that summary(f) of
fit.mult.impute() would only use the last imputed data set.
Thus, the whole imputation process is ignored.
"Not using a Design fitting function; summary(fit)
will use standard errors, t, P from last imputation only.
Use
2006 May 24
1
multiple destinations in duration (survival) analysis
Hi,
I'm trying to estimate a (parametric) competing risks model in the context
of duration (or survival, if you wish) analysis. That is, instead of
studying the transition of subjects to "death", I wish to study the
transitions to multiple *destinations* (which is different from studying
multiple *durations*, or recurrent events). I am more interested in the
hazard function rather
2010 Apr 04
2
logistic regression in an incomplete dataset
Dear all,
I want to do a logistic regression.
So far I've only found out how, in a dataset of complete cases.
I'd like to do logistic regression via max likelihood, using all the study
cases (complete and incomplete). Can you help?
I'm using glm() with family=binomial(logit).
If any covariate in a study case is missing then the study case is
dropped, i.e. it is doing a complete case
2005 Jul 08
2
missing data imputation
Dear R-help,
I am trying to impute missing data for the first time using R. The norm
package seems to work for me, but the missing values that it returns seem
odd at times -- for example it returns negative values for a variable that
should only be positive. Does this matter in data analysis, and/or is
there a way to limit the imputed values to be within the minimum and
maximum of the actual
2012 Jul 13
2
Power analysis for Cox regression with a time-varying covariate
Hello All,
Does anyone know where I can find information about how to do a power analysis for Cox regression with a time-varying covariate using R or some other readily available software? I've done some searching online but haven't found anything.
Thanks,
Paul
2006 May 03
3
Giving Error
I tried your code, but it's giving the following
error..
Error in match.fun(FUN) : argument "FUN" is missing,
with no default
2003 Jun 12
3
Multiple imputation
Hi all,
I'm currently working with a dataset that has quite a few missing
values and after some investigation I figured that multiple imputation
is probably the best solution to handle the missing data in my case. I
found several references to functions in S-Plus that perform multiple
imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions?
I searched the archives but was not
2005 May 04
3
Imputation
I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods.
I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed
2008 Mar 05
1
rrp.impute: for what sizes does it work?
Hi,
I have a survey dataset of about 20000 observations
where for 2 factor variables I have about 200 missing
values each. I want to impute these using 10 possibly
explanatory variables which are a mixture of integers
and factors.
Since I was quite intrigued by the concept of rrp I
wanted to use it but it takes ages and terminates with
an error. First time it stopped complaining about too
little
2005 Jun 10
1
Estimate of baseline hazard in survival
Dear All,
I'm having just a little terminology problem, relating the language used in
the Hosmer and Lemeshow text on Applied Survival Analysis to that of the
help that comes with the survival package.
I am trying to back out the values for the baseline hazard, h_o(t_i), for
each event time or observation time.
Now survfit(fit)$surv gives me the value of the survival function,
S(t_i|X_i,B),
2005 Jan 19
1
Imputation missing observations
>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked.
But when I use the functions like aregImpute the package is displaying coundn't find the function
Where as in help.search it is giving that use of the function
>
2009 Feb 25
3
survival::predict.coxph
Hi,
if I got it right then the survival-time we expect for a subject is the
integral over the specific survival-function of the subject from 0 to t_max.
If I have a trained cox-model and want to make a prediction of the
survival-time for a new subject I could use
survfit(coxmodel, newdata=newSubject) to estimate a new
survival-function which I have to integrate thereafter.
Actually I thought
2004 Mar 15
2
imputation of sub-threshold values
Is there a good way in R to impute values which exist,
but are less than the detection level for an assay?
Thanks,
Jonathan Williams
OPTIMA
Radcliffe Infirmary
Woodstock Road
OXFORD OX2 6HE
Tel +1865 (2)24356
2010 Jun 30
3
Logistic regression with multiple imputation
Hi,
I am a long time SPSS user but new to R, so please bear with me if my
questions seem to be too basic for you guys.
I am trying to figure out how to analyze survey data using logistic
regression with multiple imputation.
I have a survey data of about 200,000 cases and I am trying to predict the
odds ratio of a dependent variable using 6 categorical independent variables
(dummy-coded).
2003 Jul 28
2
aregImpute: warning message re: acepack and mace
hi,
i'm trying to learn how to use aregImpute by doing the examples provided with
the package, and after installing Hmisc.1.6-1.zip (for Windows),
and running the very first example on R 1.7.1, i get an error message warning
me about "mace" (see below) and acepack.
i found the acepack package, but its filename ends in tar.gz
and i'm finding it difficult to open (because its
2013 Feb 14
2
Plotting survival curves after multiple imputation
I am working with some survival data with missing values.
I am using the mice package to do multiple imputation.
I have found code in this thread which handles pooling of the MI results:
https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html
Now I would like to plot a survival curve using the pooled results.
Here is a reproducible example:
require(survival)
require(mice)
set.seed(2)
dt
2011 Nov 12
2
Second-order effect in Parametric Survival Analysis
Hi experts,
http://r.789695.n4.nabble.com/file/n4034318/Parametric_survival_analysis_2nd-order_efffect.JPG
Parametric_survival_analysis_2nd-order_efffect.JPG
As we know a normal survival regression is the equation (1)
Well, I'ld like to modify it to be 2nd-order interaction model as shown in
equation(2)
Question:
Assume a and z is two covariates.
x = dummy variable (1 or 0)
z = factors
2005 Jun 09
2
Weibull survival modeling with covariate
I was wondering if someone familiar
with survival analysis can help me with
the following.
I would like to fit a Weibull curve,
that may be dependent on a covariate,
my dataframe "labdata" that has the
fields "cov", "time", and "censor". Do
I do the following?
wieb<-survreg(Surv(labdata$time,
labadata$censor)~labdata$cov,