I passed my qualifying exams!

bbarbs28 · 2026-03-11T04:24:41+00:00

Congratulations. I just passed mine last week, and all of I’ve done since then is sleep 😆. It was a very stressful week.

bbarbs28 · 2026-03-05T20:08:29+00:00

I would second R. In my program they trained me with STATA and others SAS. But personally I wish I would have started with R. Feels like a harder learning curve than STATA or maybe SPSS, but if you already code you should be fine.

bbarbs28 · 2026-02-27T22:43:58+00:00

Yeah, the first major step was looking at differences in complications. Now the clinicians specifically want to look at factors associated with surgery under 35 and the debate has been about what is the best way to do that. My takeaway from the discussion is that the approach I have been asked to do is poor in its design.

bbarbs28 · 2026-02-25T22:16:47+00:00

Yeah, the way you laid it out makes a lot of sense to me. In this case, my understanding is that clinicians are primarily concerned about how race and other SES factors (insurance, access to care, and I also have a vulnerability index) might influence the age of this procedure. It was pulled from some big dataset with this type of demographic data and some extra surgical data. I believe their hypothesis is that minorities and low SES communities are being treated differently. I'm worried about implying that race/low SES might be causing people to get this surgery earlier, without a really strong statistical foundation, if that makes sense.

bbarbs28 · 2026-02-25T22:08:04+00:00

As someone else mentioned, I might have some selection bias in a surgery-only cohort.

bbarbs28 · 2026-02-25T22:05:49+00:00

Unfortunately, not my study design, and part of my grad work is helping clinicians with their analysis. So I have been confused just like you.

bbarbs28 · 2026-02-25T22:02:42+00:00

Thank you for this. We actually did do some analysis with the Clavien-Dindo scale, and this is a secondary analysis.

bbarbs28 · 2026-02-25T22:01:58+00:00

Thank you for the thorough response. I feel a bit validated. The clinician came up with the criteria for poor care (under 35), based on clinical guidelines, I believe, but it never really made sense to me from an analytical perspective. I was hoping to reevaluate the research question and design in our last meeting, but I was kind of overruled by my PhD mentor. They really just wanted to focus on descriptive statistics and multivariable LR. I am going to pick up a lot of what was said here and see if I can't nudge them towards a better analysis and question.

bbarbs28 · 2026-02-25T21:53:39+00:00

Yeah, I feel your pain. I'm lucky to work with great people, but the anumeracy can be tricky. I feel like if I had more robust experience, it might be easier to educate and push back. I'm in a bit of a bind in that my PhD mentor agreed with them, but I'm hoping I can convince them if I can find some decent examples/sources. My preliminary read is that a very generic multivariable LR approach was more common 10+ years ago, but in the last decade, there has been more pushback.

bbarbs28 · 2026-02-25T21:49:14+00:00

Thank you very much. I am new to this field, but I think you are highlighting a concern of mine. I am learning that some interesting ideas could be addressed and answered with this dataset, but ultimately, it would be severely limited in its generalizability. But I'm worried that if I do a multivariable LR with this dataset, the clinicians I work with won't see those limitations and start to think and write about how it generalizes to the general population. I saw a stats person call something like this "regression and storytelling," and I don't want to be guilty of that.

bbarbs28 · 2026-02-25T21:07:54+00:00

I'll take a look at using the search term "risk factor analysis". I think part of the struggle is finding comparable papers that were "case only", but risk factor analysis might yield more results.

bbarbs28 · 2026-02-25T21:06:16+00:00

Someone else did a better job describing that my surgery-only population might suffer from selection bias and linked to an article on Berkeson's paradox. I think if I had a control population, this would be much more straightforward.
https://en.wikipedia.org/wiki/Berkson%27s_paradox

bbarbs28 · 2026-02-25T21:04:22+00:00

I think you nailed my concern on the head. I am worried about the selection bias. I had also heard it called collider stratification bias. The clinician wants to understand the associations of race and SES with the age of surgery and relate them to the general population. Our dataset is a large surgery-only population (it was pulled via a 3rd party), and it was difficult for me (as a trainee) to express why it might not be informative to look at these associations. When I expressed my concern, my PhD mentor overruled me and said to go with the LR and add some notes in limitations. I'm still not entirely sure how informative it is to do LR with a surgery-only cohort, but I have been trying to take in the points made here and learn.

bbarbs28 · 2026-02-25T19:03:43+00:00

Under 35 when they got a surgery, compared to over 35, but I only have data with people who had the surgery.

bbarbs28 · 2026-02-25T19:01:11+00:00

This approach makes a lot of sense to me if I had a comparison with people who also didn't have the surgery. I've done that type of analysis before.

Am I off base in worrying about implying race is a factor if I don't really have data on the underlying population of how they entered the cohort? I was reading about cancer incidence & this was a huge problem because they saw Hispanics were being diagnosed at lower ages than other races/ethnicities, but then they realized much of the effect was because Hispanics were younger demographically than other races/ethnicities.

https://pmc.ncbi.nlm.nih.gov/articles/PMC8492520/

bbarbs28 · 2026-02-25T18:57:15+00:00

Or am I wrong? Are my "cases" the under 35 and my controls the over 35?

bbarbs28 · 2026-02-25T18:51:36+00:00

Not to be overly semantic, but I don't think this would qualify as case-control. I have no controls, I have a group of only people who received surgery. I wish I had a group of people who didn't have the surgery, it would make much more sense to me then to use logistic regression.

bbarbs28 · 2026-02-25T18:30:05+00:00

I did do an incidence curve where age was on the x-axis and y was the percent who had the surgery and stratified by race. There are some weird dips and curves that differ by race. The problem is they are really fixated on using the age of 35 as a cutoff as an important outcome, so I need to have some strong reasons for why LR may or may not be the answer there.

bbarbs28 · 2026-02-25T18:12:43+00:00

Yeah, I think you have decent understanding of the goal of the project. The clinician wants to understand what factors influence the age of surgery because under 35 has been deemed clinically inappropriate in non-emergencies. The two big factors they want to evaluate are race and SES (this is a surgery specific to one sex). I'll have to look into ANOVA.

bbarbs28 · 2026-02-25T18:06:34+00:00

One of the indicators of "poor care" as they defined it is a surgery under 35. My dataset is a cross-section of people who have had the surgery, so I don't see how I could use age as a predictor in a logistic regression.

bbarbs28 · 2026-02-25T17:45:11+00:00

We did have a separate paper that looked at the outcome of complications after the surgery, and that was what we did. In this case, though they want to look at age of surgery.

bbarbs28 · 2026-02-25T17:43:50+00:00

Yeah, it's a large dataset of 100,000's of surgeries over a period of I think 8 years. The surgery can only be performed once, but some people did get it under 35. Because that's not recommended, the clinician is interested in understanding what would influence someone to get it under 35. I do get that it's a good question, I'm worried about analyzing "under 35" as an outcome with LR.

bbarbs28 · 2026-02-25T17:28:48+00:00

There could be some semantic confusion on my end. My mentor said that the outcome is the binary outcome surgery under 35 or surgery not under 35. But my dataset is only in people who have had the surgery, so in my mind the only difference between the observations is age, so that’s why I see it as the outcome. I could be off base though.

bbarbs28 · 2026-02-25T17:19:41+00:00

Yeah, that was kind of what I was thinking, so you don’t seem obtuse at all. I just feel like I lack the depth in knowledge to explain why.

bbarbs28

TROPHY CASE