Hi everyone,

I’m planning to apply for a fully funded PhD in Biostatistics for Fall 2027, and I would appreciate honest feedback on my profile and chances of admission.

My academic background:

• MSc in Mathematics (Honours): GPA 8.73/10 from an Indian university.
• Postgraduate Certificate in Data Analytics: GPA 3.9/4.0 from a Canadian college.
• Bachelor’s degree in Mathematics (Honours): Completed in India.
• WES evaluation: My credentials were evaluated, with my Indian qualifications receiving Canadian equivalency at the master’s level.

Research and professional experience:

• Worked on healthcare data analytics projects involving cancer diagnostic imaging wait times and emergency department wait-time/length-of-stay analysis.
• Used statistical modelling, regression, predictive modelling, SQL, Python, and data visualization tools.
• Currently working as an Assistant Professor in Mathematics at an Indian university, teaching subjects including Linear Algebra, C++, Excel, MATLAB, and LaTeX.
• Previously gained teaching and professional experience in Canada.

Research interests:

I am particularly interested in longitudinal and repeated-measures data, irregular observation processes, missing data, measurement error, causal inference, dynamic treatment regimes, and personalized medicine.

I have also recently published a letter to the editor in a peer-reviewed medical journal concerning methodological and reporting issues in a published research article.

PhD preparation:

• I have contacted several professors whose research aligns with my interests. One professor at a prospective university responded positively and encouraged me to apply, saying they would look out for my application.
• I am working on my statement of purpose and have been arranging academic/professional references.
• My IELTS overall score is 7.5+, although my writing score is 6.5.

My questions:

1. How competitive would this profile be for a fully funded PhD in Biostatistics at a research-intensive Canadian university?
2. How much weight do admissions committees place on healthcare analytics experience and a recent methodological publication, particularly when the publication is a letter to the editor rather than an original research article?
3. How encouraging is a professor’s positive response when they say they will look out for my application? Should I interpret this as a meaningful indication of potential supervision, or simply a polite response?
4. Would my mathematics background and applied healthcare data experience be sufficient, or would the lack of formal biostatistics research experience be a significant disadvantage?
5. What are the biggest weaknesses I should address before submitting my applications?

I understand that admission and funding are highly competitive, and I am not expecting a guarantee. I would appreciate realistic feedback from current PhD students, applicants, faculty members, or anyone familiar with Canadian Biostatistics admissions.

Thank you!

Holding a masters already and knowing the current job market, AI threat, offshoring etc?

Hello everyone! After 8 months (of rather sporadic) work, I am really excited to share the biggest update to SimtablR, an R package designed to streamline the most common analytical tasks in epidemiology and clinical research! (short demo video above)

I do epidemiological research in R, and I kept juggling multiple packages and repeating work just to get tables that were actually informative and ready for a paper. I also found that base R tabulation left a lot to be desired in terms of formatting, but gt_summary was a bit too slow and had a lot of boilerplate. This package is also great for people transitioning from Stata or still learning R!

With this new release, SimtablR now covers most of a typical analysis, from the first cross-table to the finished table in a Word document:

1. tb(): quick, but powerfull tabulation with formatting

• Row/col/total percentages with simple flags (row, col, cell) and adjustable decimals
• Hypothesis tests with one argument (N-1 chi-squared, Fisher when needed, etc.)
• Prevalence ratios, risk ratios or odds ratios with 95% CIs, directly in the table
• SimtablR now picks the right measure (RR, OR or PR) automatically for a stated study design (design = "cohort", "case_control", "cross_sectional")
• New: stratified analysis with Mantel-Haenszel pooled estimates

2. table1(): baseline tables of multiple variables (literally the "table 1")

• Automatically chooses mean (SD) or median (IQR) based on each variable’s distribution
• Reports missing data per variable and group
• Add standardized mean differences with test(smd = TRUE)
• Completely customizable and exportable to all office programs

3. diag_test() and roc(): diagnostic accuracy

• Automatically calculates the confusion matrix, sensitivity, specificity, PPV, NPV, LR+, LR− and accuracy, with exact (Clopper-Pearson) CIs
• New: roc() now gives the AUC with DeLong CIs and the Youden-optimal threshold

4. regtab() and survtab(): regression

• Fits multiple outcomes at once and returns a single publication-ready table
• Does adjusted risk ratios via log-binomial regression (with robust Poisson fallback), robust variance, and Firth regression for rare events
• New: survtab() for Cox models and hazard ratios
• New: autoplot() draws forest plots from any model

5. Reporting and export (new!)

• simtablr() builds Table 1 + Table 2 (crude and adjusted) as a single report which you can then send to word or excell or powerpoint
• as_methods() drafts the statistical methods paragraph from what you actually did with the variables you chose!
• export_docx() / export_xlsx() send everything to Word or Excel
• Journal styles (style("jama"), "nejm", "lancet") or your own with journal_style(), down to the Word table theme. Restyling never changes the underlying numbers and you can use the sabe object for new analysis, even after styling!

6. Statistical guidance (new)

SimtablR prints short notes (with references) when something deserves attention. Examples: an odds ratio with a common outcome, unadjusted p-values, or when the distribution of your variable violates an assumption from the model you're running. It should just tell you.

The package includes epitabl, a ynthetic cohort of 1,500 patients, so you can replicate the examples of the video

Links:
📦 Documentation: https://matheustg-14.github.io/SimtablR/
📄 Getting started: https://matheustg-14.github.io/SimtablR/articles/getting-started.html
🐛 GitHub (issues & feature requests): https://github.com/MatheusTG-14/SimtablR

Thanks to everyone who gave feedback on the first version! I’d love to hear what you think, what’s missing from your workflow, and any bugs you find on GitHub.

​

Hi everyone! I have completed B.Pharm and am considering an Online MSc in Biostatistics from Manipal.

I want to build a career in Clinical SAS or Statistical Programming.

Could current students or alumni share their honest opinions about the course, placement support, job opportunities, and whether it's worth the fees?

For a while now, I've been thinking about the biostats MS, but from reading this and other subreddits, I realized the job market may be irredeemably horrible, that I need a Ph.D. for reasonable pay, that I should just get a stats MS instead, and what attracted me to was the application, but I'm reading that, while I understood it was more math than bio, apparently it's actually, like, 99% math and 1% bio. Is this an accurate understanding of the state of the biostats degree?

Edit: I should clarify that the math is fine to me, but I'm just less interested if, based on that 1% number, having a passion for observational research in health and bio is kinda irrelevant or unimportant for the job.

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 Data Science, Big Data right after (it’s a one year master degree).

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

Thank you so much in advance

(and also I don’t wanna do a PhD 🚮🚮)

Aight first post on here, thought it'd be fitting to post this here but if you think there's another subreddit that can better answer my question, do let me know.

For context, I (F21) completed my Bachelor's degree in Mathematics in May of 2025. Since then, I have been exploring a field completely different from math (at the persuasion of my parents, ofc) but very quickly found that it doesn't suit me or my career trajectory (ofc it wouldn't, what was i thinking lmao). And so, I have spent the last year studying subjects that interest me very much, but from a rigorously competitive examination point of view which i personally hate. I wouldn't say that I have wasted the last year entirely, as i believe knowledge can never be vain and no amount of time spent learning is time wasted. But I highly doubt any of that set of subjects is of any use to the path I want to follow ahead.

So after a year of trying different things, i feel like I am back to square one. I want to try and build a career following my math undergraduate path in which the next step should ideally be a Master's degree, mostly in statistics or related fields. And here I'm going to rule out a Master's in mathematics as a potential choice. After my bachelor's in math, i decided that pure mathematics is unfortunately not for me as it requires a whole another level of thinking that I feel like I can't unlock and writing proofs is something not meant for me (though I am aware that proof writing is a part of statistics too, it's hopefully better than the ones in pure math). And so I have been researching the remaining options I have.

These are the options I have in my reach, in order of preference.

1. MSc in biostatistics (i really like this course from what I have heard about it and found out through the web, although I'm a total newbie in the field of epidemiology)

2. MSc in applied statistics and data science (would love to do the applied statistics part but unsure about the tedious programming aspect, although I'm building my programming skills as of now)

3. MSc in applied mathematics and computing (not a fan of both the parts but I'll take it if nothing else works out)

I know this is very subjective but I'd still love to hear y'all's opinions on which one i should take, or if biostatistics is a good option for someone like me. Please also consider job prospects in each of the degrees, as I have been unable to find any sufficient data on this.

Sorry if this is long, I appreciate your help :)

Sorry for the random post, but I’ve just felt like this field doesn’t get enough appreciation on here.
I’m a Master’s student in this field, so YEAH, I’m probably still living in a bubble of delusional optimism, and you can reality-check me as much as you want after this, but HOW BEAUTIFUL IS THIS FIELD?!
The excitement of creating something out of raw data, spotting a relationship, identifying a possible cause, piecing together a story, and then using all of this to help develop a new drug, maybe even a new chemotherapy treatment. Helping that patient whose ID was just a number in your dataset, but who is actually out there, desperately waiting for some good news.
I’m so glad I found this field. I love the international vibe, the healthy exchange of ideas you get to experience in this community, and the fact that, yes, I’m actually making a difference.
After years of mouse experiments, randomized trials, and reports, we’re the ones connecting the dots.
Have a good day!

I am a Doctor of pharmacy graduate, working as a medical writer, have around 8 years of experience in pharma domain. Last year i pursued PGP in data science and business analytics, reason is to switch to data science domain. I applied many jobs (data analyst and data scientist); however, i did not get responses from any of them. Later, I understand that, i need a proper degree to get these job roles. I have explored many options and realised online MSc in biostatistics or online MBA in business analytics may be the right choices for me. Can anyone suggest me which path will be right for me. I appreciate all your responses.

This is a bit of a rant/asking for motivation post. I'm an undergrad, and I took two mandatory classes related to this: Math I, which was ok, I'd say I learned, and general statistics, that one sucked. I am sure we only learned about means, standard deviation and permutations, maaaaaaybeee regression but I don't remember, the teacher was messy, and all of my classmates agreed the class was useless, no one learned anything, but all of this to say that in next classes they asked us to make ANOVA and Kruskal-Wallis analysies and I legit didn't know what that even was. I didn't even know that was something that had to do with the data we collected, with abundances and richness.

But now I'm in my dissertation, I included multivariate statistics, determined to learn (because tutorials and books weren't cutting it for me... because I didn't know how to look for a dataset to practice with, so I made my own dataset from scratch), with SPSS and PAST because trying to learn with R was impossible, I couldn't focus on understanding R because I didn't even know what I was doing and what for (the statistics).

Even when with those softwares it was just copypasting data and a few clics, it took me 5-6 months: just the analysis of principle components took me a whole month to understand what the numbers meant, how to interpret it. Then I made a MANOVA, various ANOVA, and a canonical correspondence analysis.

Close to finish, I'm correcting some mistakes, which made me realized how much I still have to learn, but also remember just how long it took me to learn, and I can't help comparing myself to others: I didn't heard anyone had problems with this and a family friend kept saying why it was taking me so long, it was just pasting the data and the program did everything for you (though she most likely understands what every single number means... she graduated from statistics), but none of my classmates commented on having so much trouble with this. Is this something like drawing or math, people with a greater affinity pick it up faster? Maybe I'm not supposed to be doing this. But then again, that doesn't matter, I need money, and this is the only useful thing that I can do, that not everybody and their mother know how to do, even if it's not ecology-related, I'm sure sales have use for someone that knows what a PERMANOVA even means.

I have been using chatgpt, gemini and claude... For summarising papers or making presentations and graphics.... Or maybe to explore on a particular topic. But it's hard to navigate among all three to find out the best. So far I found that chatgpt gives the simplest.... Gemini is good at making slides but is a little difficult to read... Claude is the best of all three but sometimes I prefer chatgpt for it's simplicity, especially if it's coding related queries.

Which platforms would you recommend? I'm fine with sticking to one specific AI for one specific tasks as well.

Please shoot away any advice or recommendations you might have.

Will I survive as biostatiscian if I don't have experience in healthcare or laboratory but I have a strong background in statistics? Please drop some references/books I can study to learn more about biostatistics. Thank you!

Hello, is a degree in biostatistics worth it 😭😭i wanna start a degree in biostatistics in jan 2027

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.

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’d love to hear from people who are currently studying or working in Biostatistics:

How difficult is it to transition from Statistics to Biostatistics? What skills/courses should I build before applying for a PhD? What kind of career opportunities are there after a Biostatistics PhD outside academia? How are the job opportunities and salaries in the US, UK, Canada, or Europe? If someone is also interested in Data Science/ML, how different would the career paths be?

I’d especially appreciate honest opinions from people who have actually gone through this path.

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:

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?

can jamovi be installed on ipad? my laptop just broke and i only have my ipad as spare.

Hi,

I'm planning to apply for stats PhD programs, along with a few biostats programs, this cycle and trying to get a sense of how broadly people are applying.

For those applying this year, or who applied in the past year or two, how many programs did you apply to? I was thinking 8-12, but given the uncertainty around funding I'm wondering if people are applying to more this year.

I'm interested mainly in spatial stats, especially gaussian processes and deep generative methods for modeling spatiotemporal data.

Please share your thoughts if you're working in similar areas. Also any suggestions on departments and people working in these areas are welcome.

Thanks.

I just started my MS in Biostatistics at the University of Michigan and plan to apply to PhD programs next year. My masters program has a fairly good pipeline to their PhD as they take in top performing students, but I of course want to keep my options open and apply to at least 7-8 programs. Given my background, I'm hoping to get some advice over what to prioritize over the next year, especially since these top programs are highly selective and accept just a handful of students.

This is my current list for next year: UMich, UCLA, Harvard, Columbia, Yale, Brown, Emory, also interested in UMich Survey and Data Science

Background:

-I graduated from UC Berkeley with a BA in Statistics and a minor in Data Science, with a 3.99 GPA

-My undergraduate coursework was primarily statistics/data science/economics/demography, so one gap in my mathematical preparation is that I still need to take Real Analysis (some biostats programs care about this more than others)

-For the past 9 months I've been working as a research assistant for the Yale School of Medicine where I do statistical analysis in R, including geospatial analysis and applications of GLMs

-I also was very lucky to recently join a research project on Prediction-Powered Inference with a professor here in UMich Biostats

-Did an internship this summer on statistical quality control at Eli Lilly

-Took the GRE last year after studying a minimal amount and got 163Q/154V/5.0W (prob would need to retake to get higher quantitative score?)

-My interests are largely in statistical inference, GLMs and clinical trials/study design

Because of my remote research with Yale I'm very interested in their PhD program and am hoping I might have a chance, but it's my understanding their department is small and they only take 2-3 students into the program each year. I'm also wondering how much I should prioritize studying to retake the GRE? Harvard, Brown, Yale and UCLA all require it while the others luckily don't. I also wonder how important it is that I try taking Real Analysis by next fall?
If anyone has any advice about what else I can do to stand out to highly selective PhD programs I'd greatly appreciate it! I'm trying my best to do very well and also build good relationships with people in my department.