I am a complete novice when it comes to survival analysis. I am working on a project that requires I use the coxph function in the "survival" package, but I am running into trouble because I do not understand what is required by the formula object.
Most descriptions I can find about the function are as follows:
"a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function. "
I know what needs to be on the left of the operator, the issue is what the function expects from the right-hand side.
Here is a link of what my data looks like (The actual data set is much larger, I'm only displaying the first 20 data points for brevity):
Short explanation of data:
-Row 1 is the header
-Each row after that is a separate patient
-The first column is the age of the patient at the time of the study
-columns 2 through 14 (headed by x2-x13), and 19 (x18) and 20 (x19) are covariates such as race, relationship status, medical conditions that take on either true (1) or false (0) values.
-columns 15 (x14) through 18 (x17) are covariates such as tumor size, which take on whole number values greater than 0.
-The second to last column "sur" is the number of months survived, and "index" is whether or not that is a right-censored time (1 for true, 0 for false).
Given this data I need to plot a Cox Proportional hazard curve, but I end up with an incorrect plot because the right hand side of the formula object is wrong.
Here is my code, "temp4" is the name I gave to the data table:
library("survival")
temp4 <- read.table("~/data.txt", header=TRUE)
seerCox <- coxph(Surv(sur, index)~ temp4$x1 + temp4$x2 + temp4$x3 + temp4$x4 + temp4$x5 + temp4$x6 + temp4$x7 + temp4$x8 + temp4$x9 + temp4$x10 + temp4$x11 + temp4$x12 + temp4$x13 + temp4$x14 + temp4$x15 + temp4$x16 + temp4$x17 + temp4$x18 + temp4$x19, data=temp4, singular.ok=TRUE)
plot(survfit(seerCox), main= "Cox Estimate", mark.time=FALSE, ylab="Probability", xlab="Survival Time in Months", col=c("blue", "red", "green"))
I should also note that I have tried replacing the right hand side that you're seeing with the number 1, a period, leaving it blank. These methods produce a kaplan-meier curve.
The following is the console output:
Each new line is an example of the error produced depending on how I filter the data. (ie if I only include patients with ages greater than 85, etc.)
If someone could explain how it works, it would be greatly appreciated.
PS- I have searched for over a week to my solution, and I am asking for help here as a last resort.
You should not be using the prefix temp$ if you are also using a data argument. The whole purpose of supplying a data argument is to allow dropping those in the formula.
seerCox <- coxph( Surv(sur, index) ~ . , data=temp4, singular.ok=TRUE)
The above would use all of the x-variables in your temp data.frame. This will use just the first 3:
seerCox <- coxph( Surv(sur, index) ~ x1+x2+x3 , data=temp4)
Exactly what the warnings signify depends on the data (as you have in one sense already exemplified by producing different sorts of collinearity with different subsets.) If you have collinear columns, then you get singularities in the inversion of the model matrix and the software will attempt to drop aliased columns with a warning. This is really telling you that you do not have enough data to build the large models you are attempting. Exploring that possibility with table calls is often informative.
Bottom line: This is not a problem with your formula construction, so much as it is a problem of not understanding the limitations of the chosen method with the dataset you have assembled. You need to be more careful about defining your goals. What is the highest priority in this research? Do you really need every variable? Is it possible to aggregate some of these anonymous variables into clinically meaningful categories such as diagnostic categories or comorbities?
Related
I am trying to run a nonproportional cox regression model featuring an interaction-with-time variable, as described in Chapter 15 (section 15.3) of Applied Longitudinal Data Analaysis by Singer and Willett. However I cannot seem to get answers that agree with the book.
The data used in this book and source code is supplied at this fantastic website. Unfortunarely no R code is supplied for the final chapter and the supplied dataset for R for the example discussed in-text is incomplete and provides incorrect answers for the simplest model (which I do know how to run). Instead, to obtain the complete dataset for this example, one must click the 'Download' link in the 'SAS' column (which has the correct dataset) and then, after installing the haven package (which allows one to read in foreign data formats), read in the dataset in question via:
haven::read_sas("alda/lengthofstay.sas7bdat")
This dataset indicates participants' (variable ID) length of stay (variable DAYS) in inpatient treatment in a hospital. The censoring variable is CENSOR. The researchers hypothesised that two different types of treatment (binary variable TREAT) would predict differential values of hazard of checking out of treatment. In addition they anticipated that the between-group difference in hazard would not be constant over time, therefore requiring the creation of an interaction term. I can get the simple main effect model to work, returning the same hazard coefficients reported in the book (which is how i eventually found out the .csv file supplied with the R code was incomplete).
summary(modA <- coxph(Surv(DAYS,1-CENSOR) ~ TREAT, data = los))
coef exp(coef) se(coef) z Pr(>|z|)
TREAT 0.1457 1.1568 0.1541 0.945 0.345
I tried to follow the procedure laid out here, and here, and the sources listed therein (e.g. Therneau vignette on time-varying covariates in the survival package), and, of course, when I am copy-pasting someone else's code and running that it all works fine. But I am trying to do this for myself from scratch with a dataset whose results I can compare against mine. And I just can't make it work.
first I created an EVENT variable
los$EVENT <- 1 - los$CENSOR
there is a duplicate id number in the dataset that causes issues. So we have to change it to a new ID number
los$ID[which(duplicated(los$ID))] <- 842
Now, based on what I read here and here the dataframe needs to be split so that, for every participant, there is one row indicating the EVENT status at every point prior to their event (or censorship) time when any other participant experienced an event. Therefore we need to create a vector of all the unique event times, then split the dataset on those event times
cutPoints <- sort(unique(los$DAYS[los$EVENT == 1]))
# now split the dataset
longLOS <- survSplit(Surv(DAYS,EVENT)~ ., data = los, cut = cutPoints)
# and (just because I'm anal) rename the interval upper bound column (formerly "DAYS")
names(longLOS)[5] <- "tstop"
When I looked at this dataset it appeared to be what I was after, with (1) as many rows for each participant as there are intervals prior to their event time when anyone else in the dataset experienced an event, (2) two columns indicating the lower and upper bounds of each interval, and (3) an event column with a 0 for all rows when the respondent did not experience the event, and a 1 in the final row when they either did experience the event or were censored.
Next I created the interaction-with-time variable, subtracting 1 from the 'interval upper bound' column so that main effect of TREAT represents the treatment effect on the first day of hospitalisation.
longLOS$TREATINT <- longLOS$EVENT*(longLOS$tstop - 1)
And ran the model
summary(modB <- coxph(Surv(tstart, tstop, EVENT) ~ TREAT + TREATINT, data = longLOS))
But it doesn't work! I got the (fairly unhelpful) error message
Error in fitter(X, Y, strats, offset, init, control, weights = weights, :
routine failed due to numeric overflow.This should never happen. Please contact the author.
What am I doing wrong? I have been slowly working through Singer and Willett for almost three years (I started while still a grad student), and now the final chapter is proving to be by far my greatest challenge. I have thirty pages to go; any help would be incredibly appreciated.
I figured out what I was doing wrong. A stupid error when I created the interaction variable TREATINT. instead of
longLOS$TREATINT <- longLOS$EVENT*(longLOS$tstop - 1)
it should have been
longLOS$TREATINT <- longLOS$TREAT*(longLOS$tstop - 1)
Now when you run the model
summary(modB <- coxph(Surv(tstart, tstop, EVENT) ~ TREAT + TREATINT, data = longLOS))
Not only does it work, it yields coefficients that match those reported in the Singer and Willett book.
coef exp(coef) se(coef) z Pr(>|z|)
TREAT 0.706411 2.026705 0.292404 2.416 0.0157
TREATINT -0.020833 0.979383 0.009207 -2.263 0.0237
Given how dumb my mistake was I was tempted to just delete this whole post but I think I'll leave it up for others like me who want to know how to do interaction with time Cox models in R.
I want to perform a multiple group CFA with lavaan in R.
I have several categorical variables and some variables contains 11 categories. So these variables will have 10 thresholds. In the results below you can see thatthe 10th threshold is smaller than the 9th, i.e., it is not in the creasing order.
Several variables with 11 categories have the same problem.
Question:
Why are the thresholds distorted?
R-code:
model2<-'range = ~ NA*gvjbevn + gvhlthc + gvslvol + gvslvue + gvcldcr + gvpdlwk
goals = ~ NA*sbprvpv + sbeqsoc + sbcwkfm
range~~1*range
goals~~1*goals
gvhlthc ~~ gvslvol
gvcldcr ~~ gvpdlwk
'
cfa.model2<-cfa(model2, ordered=varcat, estimator="WLSMV",data=sub)
summary(cfa.model2,fit.measures=TRUE,standardized=TRUE, modindices=TRUE)
Label assignation of the thresholds was sorted alphabetically, aka c('t1','t10','t2','t3'....) but summary() sorts it ""properly"".
You can try to add additional factors to check if your scale corresponds to:
c('t1','t10','t11','t12',...,'t2','t3'....)
Not much you can do on your side, except understand which row is each of your factors.
Well, it seems like I cannot add a comment due to not having enough reputation, so I can only reply with an answer, although this is not a proper answer (it will definitely not solve your issue, though I hope it points in the right direction).
For your example to be reproducible, you should provide the community with the data to fit the model.
On the other side, I guess your problem must have to do with the nature of the category: it's possible that your 11th category does not mean "the most level of agreement" with the item, or that the response categories are not ordered from 1 to 11, or something similar. Given that the rest of the thresholds seem to accurately represent a continuous, monotonically increasing scale, and that this same problem precisely happens in the same category in different variables (at least the two that you are showing), there must be something with the response scale in those items.
In summary, it seems to be more of a problem of interpretation of the parameters of the model rather than a statistical issue.
Until recently I used SPSS for my statistics, but since I am not in University any more, I am changing to R. Things are going well, but I can't seem to replicate the results I obtained for repeated effect LMM in SPSS. I did find some treads here which seemed relevant, but those didn't solve my issues.
This is the SPSS script I am trying to replicate in R
MIXED TriDen_L BY Campaign Watering Heating
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0,
ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED=Campaign Watering Heating Campaign*Watering Campaign*Heating
Watering*Heating Campaign*Watering*Heating | SSTYPE(3)
/METHOD=REML
/PRINT=TESTCOV
/RANDOM=Genotype | SUBJECT(Plant_id) COVTYPE(AD1)
/REPEATED=Week | SUBJECT(Plant_id) COVTYPE(AD1)
/SAVE=PRED RESID
Using the lme4 package in R I have tried:
lmm <- lmer(lnTriNU ~ Campaign + Watering + Heating + Campaign*Watering
+ Campaign*Heating + Watering*Heating + Campaign*Watering*Heating
+ (1|Genotype) + (1|Week:Plant_id), pg)
But this -and the other options I have tried for the random part- keep producing an error:
Error: number of levels of each grouping factor must be < number of observations
Obviously in SPSS everything is fine. I am suspecting I am not correctly modelling the repeated effect? Also saving predicted and residual values is not yet straightforward for me...
I hope anyone can point me in the right direction.
You probably need to take out either Week or Plant_id, as I think you have as many values for either variable as you have cases. You can nest observations within a larger unit if you add a variable to model time. I am not familiar with SPSS, but if your time variable is Week (i.e., if week has a value of 1 for the first observation, 2 for the second etc.), then it should not be a grouping factor but a random effect in the model. Something like <snip> week + (1 + week|Plant_id).
k.
Is Plant_id nested within Genotype, and Week indicate different measure points? If so, I assume that following formula leads to the required result:
lmm <- lmer(lnTriNU ~ Campaign + Watering + Heating + Campaign*Watering
+ Campaign*Heating + Watering*Heating + Campaign*Watering*Heating
+ (1+Week|Genotype/Plant_id), pg)
Also saving predicted and residual values is not yet straightforward for me...
Do you mean "computing" by "saving"? In R, all relevant information are in the returned object, and accessible through functions like residuals() or predict() etc., called on the saved object (in your case, residuals(lmm)).
Note that, by default, lmer does not use AD1-covtype.
step() and stepAIC() produce a "remove missing values error" when running the code on data with missing values.
Error in step(mod1, direction = "backward") :
number of rows in use has changed: remove missing values?
According to ?step:
The model fitting must apply the models to the same dataset. This may be
a problem if there are missing values and R's default of na.action = na.omit
is used. We suggest you remove the missing values first.
I have a data frame with one variable which has four na values. However, when I run step on the lm object, I don't get the "missing values" error even though it has missing values. Can anyone tell me what could be going on?
> d1$Impressions
[1] NA NA NA 6924180 9313226 27888455
18213812 54557205 13495553
...
This does not produce an error message:
mod1 = lm(Leads ~ G + Con + GOO + DAY + Res + SD + ED +
ME + Impressions + Inc + Sea, data=d1)
step(mod1, direction="backward")
stepAIC(mod1)
Even with a variable which has missing values, it's not generating an error message. Any ideas on what's going on?
One reason for the stated behaviour is this. step() fits the full model and hence drops 3 (as stated) observations due to presence of NAs. As long as the variables for which there are NAs remain in the model, the lm() function will remove those observations at each step. If stepping stops before it removes a variable that would result in one of the previously removed observations remaining in the model, then no error will be raised, because the numbers of rows in the model matrix will not have changed.
As an aside, stepwise selection like this is considered to be of somewhat dubious validity. Not least, in using it you a making a fairly bold statement that the effects of the eliminated variables are exactly equal to zero. This also has the effect of biasing the effect (estimated coefficients) of the variables retained in the model to have larger (absolute) value.
Alternatives to this stepwise selection include shrinkage methods such as the Lasso and the Elastic Net.
I have data of various companies' financial information organized by company ticker. I'd like to regress one of the columns' values against the others while keeping the company constant. Is there an easy way to write this out in lm() notation?
I've tried using:
reg <- lmList(lead2.dDA ~ paudit1 + abs.d.GINDEX + logcapx + logmkvalt +
logmkvalt2|pp, data=reg.df)
where pp is a vector of company names, but this returns coefficients as though I regressed all the data at once (and did not separate by company name).
A convenient and apparently little-known syntax for estimating separate regression coefficients by group in lm() involves using the nesting operator, /. In this case it would look like:
reg <- lm(lead2.dDA ~ 0 + pp/(paudit1 + abs.d.GINDEX + logcapx +
logmkvalt + logmkvalt2), data=reg.df)
Make sure that pp is a factor and not a numeric. Also notice that the overall intercept must be suppressed for this to work; in the new formulation, we have a different "intercept" for each group.
A couple comments:
Although the regression coefficients obtained this way will match those given by lmList(), it should be noted that with lm() we estimate only a single residual variance across all the groups, whereas lmList() would estimate separate residual variances for each group.
Like I mentioned in my earlier comment, the lmList() syntax that you gave looks like it should have worked. Since you say it didn't, this leads me to expect that really the problem is something else (although it's hard to tell what without a reproducible example), and so it seems likely that the solution I posted will fail for you as well, for the same unknown reasons. If you want more detailed guidance, please provide more information; help us help you.