r/biostatistics

Hey everyone !!!

I’m (24F) currently finishing up my Master’s in Biostatistics in Belgium (graduating this June!) and trying to figure out my next move.

To give a bit of context, I actually started out with a bachelor's in Political Science. Back then, I took an elective intro course to R out of curiosity, absolutely loved it, and decided I strictly wanted to pivot into a data-related field.

Since Biostatistics was pretty much the only data Master's that would take a chance on me with my PoliSci background, I jumped at the opportunity.
Going straight into a stats-heavy program was a massive leap, but it proved to me that I'm not afraid to jump into the deep end and learn entirely new disciplines from scratch.

Now, as graduation approaches, I’m seriously considering enrolling in a specialized Master’s in Big Data right after.

What do you think? I’m open to any advice and/or suggestions :)))

Thank you so much in advance

Hey, so I have a BS in Biochemistry, an MPH in Epidemiology with a graduate minor in Biostatistics, and I'm currently doing an MA in Behavior Analysis.

I love statistics and its application to health behavior and human interactions. I knew I wanted to do my PhD in Biostatistics during my MPH but I felt insecure to apply because my degree didn't have Biostatistics in the title. Silly, I know that now.

However, I haven't had experience with Biostatistical research since my master's which I graduated from in 2022. I did do qualitative and mixed methods research as a Program Manager at my university that I got my master's from until 2023. I currently work in the field of special education and it kind of fell into my lap mostly because of the terrible MPH employment market and also because I do like what I do. I just don't like it enough to make it my lifelong career.

I want to do translational research in mental health and health behavior. I want to study how to apply statistical principles to those areas.

My question is, is it feasible for me to pivot to a PhD in Biostatistics? Is there something else I need to do to make myself a better candidate for admission? I was considering another master's in Data Analysis or Statistics of other related fields. Is that necessary or are there simpler ways for me to get there?

ETA: I did get into a PhD in Epidemiology but did not enjoy the theoretical epi as much as the biostats. I got A's in all my biostats courses but didn't do well in some of my epi courses. So I left the program.

Half of my courses (literally split down the middle) in my MPH were biostats because I had the flexibility to choose them as electives. I learned SAS and R.

I wrote this up for r/Salary and thought I would post here, too.

Salary Progression - 34F PhD aka what I did with my bachelor’s degree in biology

Thought I’d share my salary progression because I finally got my dream job and I can’t tell anyone about it. See also: women in STEM, making the best of an unwise undergraduate major, career trajectories in public health. Also working parents, working single mom. Just all the things.

Education:
2013 - Graduated college, S.B. in Biology
2019 - M.S. in Biostatistics
2026 - Completed Ph.D. in Epidemiology
Salary progression:
2013 — AmeriCorps Member: ~$10,000
2014 — Research Assistant: $24,000
2015–2017 — Clinical Research Coordinator: $37,000
2018–2020 — Chronic Disease Data Coordinator: $54,000
2020–2022 — Same/general career track: $57,000
2022–2025 — Federal Government Statistician: $125,000
2025–2026 — Data Scientist, Public University: $83,000
^ Big pay cut. My COVID-era government funding ended, and I took the first solid job I could find and stuck it out until I found my dream job.
Later 2026 — Biostatistician, Privately Held CRO: $157,500 + 10% bonus

So, roughly:
$10k → $24k → $37k → $54k → $57k → $125k → $83k → $157.5k + bonus

The biggest lesson for me has been that salary progression is definitely not linear. I went from $125k down to $83k because of circumstances outside my control, then nearly doubled my base salary about a year later.

It also took a long time for the combination of my experience + quantitative training + graduate degrees to really translate into higher compensation. I spent a lot of years in the $20k–$50k range before things accelerated.

Current total target compensation with the 10% bonus is about $173,250/year.

I'm preparing POET II for a journal club (open-label RCT of response-tailored vs standard antibiotic duration in left-sided endocarditis, NEJM, Aug 2026) and can't reconcile the sample size with the stated confidence level.

Stated in the paper and supplement: safety endpoint, non-inferiority, assumed event rate 8%, margin 7.5 points, 90% power gives 450 patients, "with a one-sided confidence interval of 97.5%". Per pathogen: margin 12.5 points, 80% power gives 118 patients.

My check, using n per arm = (Zα + Zβ)² × 2p(1−p) / Δ², with p = 0.08 and no true difference:

	Overall (Δ 7.5, 90% power)	Per pathogen (Δ 12.5, 80% power)
One-sided alpha 0.05	224 per arm (~450)	59 per arm (118)
One-sided alpha 0.025	275 per arm (~550)	74 per arm (~148)

Only alpha 0.05 reproduces the stated numbers. The 2020 design paper (margin 5%, 80% power, "alpha 5%") gives 730 patients, which also fits one-sided 0.05 (about 924 at 0.025). The results report two-sided 95% CIs (equivalent to one-sided 97.5%), so the analysis is stricter than the calculation. By my numbers, power is then about 83% overall (about 88% with the 508 randomised) and about 70% per pathogen.

Questions:

1. Is there a standard variant (continuity correction, variance under the null, software default) that gives 450 and 118 at one-sided 0.025? I couldn't find one.
2. If it is just a labelling slip, would you treat it as a minor wording issue or a real power issue in an appraisal?