r/biostatistics

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Any thoughts? Is it worth it?

What kind of companies or hospital hiring for this role?

And what kind of work is completed? Any evidence that this degree will have high demand in the future ?

I have held this opinion for a long time. I'm wondering what people think.

I keep running into the same thing in medical papers: the first stats you see are already the fancy ones. Mixed models, tons of covariates, custom link functions, the whole works. And I’m always thinking: what the heck did the data look like before making all those assumptions?

I want to see the boring, simple analysis first. Means. A scatterplot. A simple comparison. An effect size. Let me see if there is a signal before making multiple assumptions, multiple adjustments, complex analyses. If the basic and complex analyses agree, great. The model probably adds something useful.

But if there’s nothing much there until the fifth layer of adjustment, that’s when I start getting suspicious. Is this just p-hacking due to known publish-or-perish incentives?

Not because complex models are bad. Sometimes they’re exactly what the question requires. My conspiracy-theory antenna goes off when the paper never shows the simple version at all.

Thoughts? Does anybody have insights into this? I'm genuinely curious whether I'm just overly skeptical of much of the biomedical research literature, or whether basic descriptive and inferential analyses still play a critical role and should be included in every research paper.

Question in title! I have a strong statistics background but my program does not offer strong training in statistics. I’m looking to further build my skillset but was wondering if the CenterStat course was worth it (coming from a poor graduate student). I think I would also benefit from the structure the course provides (vs. trying to learn on my own). Curious if anyone has taken the course or their experiences. (Or any other helpful resources!) TIA for any insights!

Basically the title. I am applying to graduate programs in (bio)statistics this fall. I found biology concepts to be interesting and was going to major in it (I was considering pre-med), but I ended up deciding that I was more oriented towards quantitative and computing work. I am now an economics research assistant but found that I want to combine my interest in math/stats (esp assumption-lean inference) and computing with public health, so a biostats PhD seems like the right path for that. However, since I have not that much exposure to public health, I was wondering (a) what ways are best to get exposure to public health for a biostatistics graduate program, if necessary, and (b) how can I communicate my interest in biostatistics in an SOP and personal statement such that it makes sense why I may gravitate towards biostatistics rather than statistics (although I like both and plan to apply to both).

Hi everybody. I’ll be applying to 13 PhD programs in stats/biostats this upcoming cycle and wanted to post here to receive any kind of thoughts/feedback on whether my list is reasonably balanced for my profile. I’m particularly interested in whether I’m aiming too high or too low, or if there are any programs I should consider adding or removing. Worth noting is that biostatistics is my preference, though i’m applying to statistics programs where I believe I can work on similar research.

Background

- Domestic undergraduate applicant at small, teaching-focused public university in the northeast.
- Mathematics major with Statistics concentration & computer science minor
- 3.9 gpa
- Departmental honors track which includes a two-semester honors thesis’
- Relevant coursework includes Probability, Mathematical Statistics, Numerical Analysis, Real Analysis 1&2 (to be completed in fall and spring of next AY), Calc 1-3, Linear Algebra, Machine Learning, Data Mining, other intro stats & cs courses
- No GRE currently planned (no schools on list require, though some recommend)

Research

- Participated in summer biostatistics research program at R1 university. My project involved statistical genetics, including genotype QC and statistical analysis of genetic and clinical data.
- Completed a separate funded summer research project involving mathematical modeling of infectious disease outbreaks and machine learning. My honors thesis will be built upon this work.
- Broadly interested in computational/applied statistics, genetics, machine learning, and biomedical applications.

Teaching

- Several semesters of experience as TA for calculus 1/2.
- Also a few semesters of experience in general tutoring which includes intro math & stats classes.

Letters

I expect 2 strong letters and 1 okay letter.

1. Research Advisor for independent research(
   (will speak on research independence and capability for graduate research)

2. calc 1 & 2 professor, will also be analysis 1 & 2 professor (can speak on mathematical maturity and growth)

3. Probability & Mathematical Statistics professor (professor expressed enthusiasm in writing it for me)

Schools

Biostatistics:
- UNC
- Boston University
- University of Minnesota
- UMass Amherst
- University at Buffalo
- University of Rochester

Statistics:
- University of Toronto
- Virginia Tech
- Boston University
- University of Wisconsin - Madison
- University of Connecticut
- University of Minnesota

Data Science:
- UC San Diego

I’m aware that PhD admissions are unpredictable and I’m not looking for precise admission odds. I’m more interested in whether this looks like a reasonable distribution of reaches/targets and whether there are weaknesses in my profile or gaps in my school list that I should consider.

Since biostatistics is my preference, I’d also be interested in whether people think I should replace any of the Statistics programs with additional Biostatistics programs that would fit my interests.

Any feedback from people familiar with Statistics/Biostatistics PhD admissions would be appreciate!

hi! i'm applying this fall for phd programs starting fall 2027. my focus is in substance use / harm reduction, leaning hard into computational and causal methods over descriptive epi.

quick background: us citizen, did undergrad and grad school both in the us. selective research university for undergrad, interdisciplinary major closest to cognitive neuroscience with a math minor, 5+ years working inside statewide harm reduction infrastructure (drug checking, distribution networks, plus time managing people and coordinating outreach), master's in math with an emphasis in data science, plus a graduate certificate in public health. handful of publications and presentations in the space.

what i actually want long term isn't just to publish about harm reduction from the outside. i keep coming back to something like a "science as service" model, where research is leverage to move resources and legitimacy toward the community orgs already doing the work, rather than research being the end goal itself.

methods wise: causal inference, decision/cost-effectiveness modeling, implementation science, resource allocation optimization. i'd like simulation modeling in there too, feels like where a lot of the field is heading and i want to build toward what these fields will actually need in five years, not just what's fundable right now. substantively i keep coming back to stimulant use and drug checking as topic interests specifically.

fields i'm currently looking at: epidemiology, public health more broadly, health services research, health policy, social work, computational social science, health behavior. not attached to any one label, just what's come up so far in my own search.

my undergrad background is basically the wider intersection (cog neuro, psych, philosophy, some math) and i'm genuinely still drawn to that world, including policy and systems science too. i def want a real interdisciplinary program instead of a straight epi department, but a lot of those got hit hard by recent funding cuts and i don't know if that's a live option right now.

within epi/public health specifically, the strongest names in this space seem to split into two camps: harm reduction people without much computational depth, or modelers without harm reduction grounding. not sure how people usually find or build toward an advisor situation that bridges both instead of just picking a lane.

my ask: what am i not seeing? i'm looking for the fields, departments, or angles someone who's actually been through this would flag that i wouldn't think to search for myself. same goes for methods, i want to be building toward something that holds up long term

tl;dr: applying to phd programs this fall for substance use / harm reduction with a computational methods focus (causal inference, decision modeling, simulation, resource allocation). trying to figure out what fields, departments, or methods i'm not thinking of.

I am becoming more interested in bioinformatics after learning epidemiology which involved coding, biostatistics, and core biological concepts. Where are the best resources to learn?? Anything I can put on a resume to show I would also be trained in bioinformatics?

Hi everyone! I have a Master’s degree in Biotechnology and I’m looking to do a one-year postgraduate diploma in Bioinformatics/Biostatistics in Europe. Does anyone know of any good programs that are fully online/remote and can be started at any time of the year, or around autumn/winter? It is easy to find a job after?

Thanks in advance!

We're running a pilot clinical study and management has asked me to build them a secure database, something the organisation genuinely owns and can build on for future studies, rather than just Excel files in SharePoint.

Before I get into tool-specific questions, I want to ask the general one: for a small org with no internal IT team, what does "having your own database" actually look like in practice? Do you end up with your own cloud environment (Azure/AWS) that you own outright, or does "ownership" in this context usually mean something more modest, like owning the exported data itself, while the collection system lives somewhere else?

I have sponsorship available if we go the institutional route, that's not the blocker. What I'm trying to work out is what the end state actually looks like for an org our size.

Here's how I've broken down the options so far, and where I'm unsure:

1. REDCap

• a) Hosted by an institution (university/hospital), do we still end up with our own Azure environment for the exported data, or does "our database" just mean our own storage/SharePoint area at that point?
• b) Hosted by a commercial REDCap vendor, same question. Does the org still need its own Azure, or does owning the exported data in something simpler cover it?

2. A different platform entirely (Castor or similar, bundled hosting): same question again: is there still a reason to also stand up our own Azure environment, or does that become unnecessary once the vendor is holding everything?

Basically: at what point, if any, does a small org actually need its own cloud environment, versus just owning a clean, well-structured export from wherever the data was collected?

For people who've actually built this for a small org, what did "the database" end up being, concretely? Would genuinely appreciate real examples over general advice.

Guys i took a drop year for neet and now i am planning to choose statistics for my bachelors.. If anyone working in this industry please give some suggestion's. Is it a good career path?. What if i get into mba after it with statistic specialization...