u/regretting_biostats

Isaias stirred up every allergen in the 20 miles between my house and the coastline and funneled the pollen directly into my sinuses

update: it’s a cold/strep

1. Is biostatistics one of those fields where you reach out to faculty about their research before applying for a PhD? I just really didn't get far enough in my master's degree to remark on advanced methods beyond "it sounds cool." I did an MSPH in state because it was cheap, but I learned it wasn't a very serious curriculum, with 9-hour semesters, most exams open-book, and apparently the CDC doesn't like hiring us.

2. Would I be okay just taking more math classes as preparation for a PhD? I took courses with Johns Hopkins online before the application cycle and did pretty well.

3. Are there any programs that I should avoid? I have no intention of applying to my previous school. I've also heard OSU has had problems with PhDs not getting jobs.

4. Do I need a PhD to get a job? I wanted to take a year off school to work. It's not unusual for a new job posting to amass 100 applicants within 24 hours, half of them master's, maybe 1 in 5 have PhDs, fighting for relevant positions that supposedly require a bachelor's. I thought the Bureau of Labor Statistics projected something like 30% job growth.

I have an advisor who often answers questions without looking at the data or reading anything about the metrics we're using. The set is sourced from a neurologist, who supplied 493 patient charts, 490 complete case. We are using the Charlson Comorbidity Index (CCI) and the Expanded Disability Status Scale (EDSS).

Her initial suggestion was to use ordinal logistic regression for all 20 increments of the EDSS. It was found to be estimated retrospectively into 3 categories. She then suggested ambulatory status, which had some outcome categories lower than 40.

She also does not seem to believe me that the CCI is often treated as a continuous variable. We have the CCI and an interaction term with a dichotomous variable. With a range of 14 in our set, this would mean a minimum of 28 predictors against each of the outcome levels. We have an additional 6. The mid-range EDSS category has a sample size of 92, and the severe category has a size of 143.

I tend to be treated like a perfectionist for protesting against this. She just seemed to shrug at algorithm convergence failures, as though this were a skill issue. I'm just doing my own thing at this point but I'm still just a master's student, and she's suggesting impossible statistical analysis plans after earning her PhD something like 10 years ago.

The program director thinks my advisor can do no wrong and that I'm whiny.

In collinearity assessments, is it typical for class variables to have high (> 0.5) correlations between levels?

In collinearity assessments, is it typical for class variables to have high (> 0.5) correlations between levels?

I know it's breaking rule #1 but this is actually my final project and am supposed to present it to the department. I'm finishing up an MSPH program and my project mentor has always been inattentive: not responding to emails for weeks, missing deadlines or cutting them really close, nearly cost me my scholarship. I think she forgot how to use SAS (she uses JMP) and she was really dismissive about diagnostics ("data is easy") for my set, which uses EHRs and has some data collection problems. She also has some bad OCD or something because she has this fixation on formatting tables and figures and will just not leave constructive feedback where reading text is involved. Other faculty have helped me more with statistics than she did but they're busy people, too, and I stopped asking questions because it felt like I was "cheating on" my mentor, and she said it was supposed to be an independent project so I wasn't sure how academic integrity played into it (although this was just an excuse in retrospect - she seems comfortable with taking credit for her colleagues' work).

Anyway the project has limited scope and things like GEE analyses are considered too advanced - we're given lectures on what it is but aren't assigned homework on it unless you continue with the PhD or DrPH program. Plus I wasn't allowed to look for my own project and had to rely on faculty to arrange this, and I started 5 months later than I was originally supposed to. I hate this place lol

https://preview.redd.it/nipae9mw935h1.png?width=1772&format=png&auto=webp&s=13a25fa08af104ddc8d12f507e7134ebf56443d3

I had the idea to remove the age component from CCI (the Charlson Comorbidity Index) since it's being used for multiple sclerosis, and disease increases with time so this would contribute to confounding - we're not predicting anything, the doctor who owns the data seemed to have just Googled "comorbidity metric" and ran with it. With the interaction term, this becomes important because Black patients have an earlier disease onset and white patients are wealthy enough for decades of follow-up. My advisor told me to categorize CCI and I trusted she knew better than I did, but she actually didn't know anything about the metric beyond what I'd explained to her

So a couple of questions because my diploma is as worthless as toilet paper. Also EDSS was estimated as ranges of scores retrospectively so I don't have the full scale

• Since CCI is a class variable here, is it expected for a correlation matrix to show values > 0.5 between classes, and does that contribute to model instability?
• Beyond losing information and adding additional terms to the model, what else is wrong with categorizing continuous predictors?
• Should I not have controlled for baseline EDSS? I don't have much training with longitudinal studies and correlation/clustering/repeated measures. Plus there are few patients who present with moderate disability (EDSS 4-5.5) and very few for severe disability (EDSS 6-10) causing large standard errors
• I used this project for another class on geospatial data and most Black patients physically are gathered around the city this university is located, whereas white patients live in richer areas, many are hours from the city or from out-of-state. Is this an example of clustering?
• Would a GEE analysis be a better approach than generalized logistic regression?