Solved – Can Cox regression be used to assess the difference between two treatments

multiple regressionspsssurvival

In this project our objective was to compare the revision rates between two surgical techniques. We have time to revision (time between two surgeries), demographics, and other variables we are comparing among the two techniques. Reviewers of the article wrote that we need to use Cox regression. When using Cox regression all of the cases will have the event (revision surgery) and our covariates will be entered. Can I still look at the differences between the two techniques like I would in a KM survival by the log rank? How would I set this up in SPSS?

Additional information. The percentage of pts needing revison is extremely low, only 1.2%, out of 7400. In answer to one of the post, there are pts who may still need a revision, the last operations were in December of 2011. However, the majority will not. I am learning SAS but I haven't done any survival analysis as of yet.

Thank you for all the post. I just want to make sure I'm on the right track. We only have data for the patients that have had a revision. All pts will have the event in the cox regression. The majority of pts will not have the revision surgery, however, we don't have access to any of the data from these patients. Does this change any of your suggestions?

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I have no idea how one would set this up in SPSS, it's not my software of choice.

To answer your question however, yes, you can use Cox regression to look at the relative difference in time-until-revision for the two techniques. There are other techniques you might try, but Cox proportional hazards models are by far the most commonly used I see in clinical literature.

Having all events isn't a problem (indeed, it lets you get around censoring, which is nice), and will let you control for your covariates, which is somewhat harder to do when using a KM-based approach. My one recommendation however is, if you're not comfortable with what you're doing, to go find someone in your department, university, etc. who is comfortable with survival analysis, and talk to them.

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Solved – perform Cox regression on left truncated records

First a disclaimer: I've never had to use the time start/end variable in this way and although I'm familiar with mixed effects models I have never really had to use them IRL. Feel free to correct me if I've made a mistake

The problem consists out of two things as I see it:

One person can occur multiple times. This puts the observations independence into question.
A person may enter and exit the cohort at risk at different times throughout the study, i.e. this is an open (dynamic) cohort.

For the first point I think using a mixed effects model is a must. I use R and there is a coxme package recently developed by prof. Therneau. The vignette documentation is excellent and it seems easy to deploy.

For the second point you just need to add the start and end point to the survival object. This is fairly easy in R although I have never had to use it myself. Below is an example that should work:

# Set the event yes/no (1/0)
df$event <- !is.na(df$IpAdmit)
# Those that lack a date should have one
df$discharged <- !is.na(df$start)
df$start[is.na(df$start)] <- as.Date("2010-01-01")
df$end[is.na(df$end)] <- as.Date("2011-12-31")

# Can be merged into one step without the sv variable
sv <- Surv(time=df$start, time2=df$end, event=df$event)
# A model where the medication possession ratio (compliance) interacts with
# the fact that a patient has been discharged
coxme(sv ~ discharged*MPR + age + sex + (1|MemberID), data=df)

You might want to consider what you want to achieve with the cox regression model in this case. I am not sure that hazards make sense in this setting, although this is very difficult to know without going through the full study protocol. Make sure that others have used cox regressions in similar settings prior to this analysis. It seems to me that a good alternative would be a mixed effect logistic regression where you simply use odds for admission and add the number of days at risk as a predictor, preferably as a natural spline or something that allows a non-linear relationship.

Minor update from the discussion

When it comes to time-dependant covariates I have found this to be a little tricky when trying to deploy. I had a CV-question a while ago on this subject that you may want to look into. As I wrote in the comments, in the end the time dependence was a little more than I could conveniently display and explain to my colleagues. Furthermore the model was not strongly affected by this effect so I dropped it and switched to an early and late dataset. I recommend you consider who your audience is and if the time-varying coefficients will add that much to the model.

You have a potentially very serious problem where some patients start their period discharged from the hospital while some are untainted. I think you need to think about possible effect modification between these two groups - do they belong to the same population or not? It is easy to make a case that medication-compliance has a much bigger admission-avoidance impact in the discharged population. I think you at least should have a variable indicating if the patient has started a period straight after hospitalization or not (I've added an example in the code).

I have recently done a medication adherence study, if you haven't read this article I strongly recommend it. In my study I was also able to deduce from the prescription text 94 % of the cases using Python's very powerful regular expressions. I'm planning on doing a post on my blog once the article gets published, the text interpretation is in Swedish but you can very easily use the structure as most prescriptions follow a similar pattern (let me know if this would be useful and I can write up the post a little earlier). The advantage is that you want to identify exactly when a patient is expected to be without medication because you will probably have a very close relationship between that and readmission.

Solved – Comparing the difference in change in repeated measures ordinal variables between two groups

If you are familiar with ordinal logistic regression models and mixed-effects models with lmer in the lme4 package in R, you may want to check out the clmm (cumulative link mixed models) function in the ordinal package. This function, built on lmer, may prove especially useful if you have covariates to control for.

I suppose this answer may be more relevant to Brian's problem than that of @user45094; I am not sure if SPSS has something like cumulative link mixed models.

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Solved – perform Cox regression on left truncated records

Minor update from the discussion

Solved – Comparing the difference in change in repeated measures ordinal variables between two groups

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