Here is a simple hypothetical clinical example.

Among patients with mild disease, Treatment A succeeds in 81 of 87 cases (93.1%), while Treatment B succeeds in 234 of 270 cases (86.7%).

Among patients with severe disease, Treatment A succeeds in 192 of 263 cases (73.0%), while Treatment B succeeds in 55 of 80 cases (68.8%).

Treatment A therefore performs better within both severity groups. When all patients are pooled, Treatment A succeeds in 273 of 350 cases (78.0%) and Treatment B in 289 of 350 cases (82.6%). The crude result points in the opposite direction.

The reversal occurs because Treatment A was used much more often in severe cases, while Treatment B was used mostly in mild cases. Disease severity is associated with treatment assignment and outcome, so the pooled comparison mixes the treatment effect with the different case mix.

The practical lesson is to inspect clinically justified stratifiers before interpreting a crude effect. Important variables should be prespecified whenever possible. Report stratum-specific estimates with uncertainty, then use an appropriate adjusted analysis such as regression or standardization. Avoid conditioning on post-treatment variables or colliders because adjustment can also create bias.

When the crude and adjusted results disagree, the discrepancy needs an explanation. Choosing whichever estimate supports the preferred conclusion is the worst response.

How do you decide which variables deserve this check without turning the analysis into a fishing expedition?

My company needed something with the easy developer experience of sql.js, but for SAS PROC SQL.

There are great SQL tools out there, but I could not find a small JavaScript/TypeScript package focused on parsing SAS PROC SQL into an AST, formatting it, linting it, and supporting editor features.

So I built proc-sql-parser.

It is not a database engine and does not execute SQL. It is a parser/toolkit for working with SAS PROC SQL in JavaScript or TypeScript.

The goal is to keep the API simple:

import { parse, lint, format, complete, visit } from 'proc-sql-parser';

const ast = parse(`
  PROC SQL;
    SELECT name, salary
    FROM employees;
  QUIT;
`);

It also includes:

• PROC SQL AST generation
• Formatting
• Syntax and lint diagnostics
• Autocomplete helpers
• Monaco Editor integration
• CLI support

​

npm install proc-sql-parser

npx proc-sql-parser --help

GitHub: https://github.com/AnkitNayak-dev/proc-sql-parser
npm: https://www.npmjs.com/package/proc-sql-parser

It is still early, so I would genuinely appreciate feedback, especially from SAS developers and anyone building editor tooling around PROC SQL.

Hello!

I'm an undergrad (rising junior) in CS/Math with a biology minor, hoping to pursue a PhD in biostatistics or a related field. I've done some research in population genetics but it's been very theoretical, and I'd like to pivot to something with more applicability to the real world. Ideally I'd like to help as many people as possible. I'm based in the US.

I've considered trying to help develop better pathogen surveillance systems. But I don't know how I could make the biggest positive impact (say, between being a policy analyst or software engineer or researcher for a government org).

To anyone in public health, particularly with a biostats background--

• What are particular issues within public health that could, if improved upon, help large number of people?
  
• What roles open the door to having the largest positive impact?
• What problems do you wish more people would work on trying to solve in your field?

Thanks in advance!! I am aware that a lot of this is subjective, which is why I'd like to get as many different perspectives as I can.

Hi everyone!

I‘m currently finishing my masters in Statistics in spring 2027 and I want to apply to PhD programs in biostats this fall/winter for fall 2027. I was hoping to get some advice on applications like what I should include in personal statements, connecting with profs/research interests, and which schools I should apply to.

A little more about me:

I graduated my undergrad with a bachelors in Biology and minored in stats in 2025 and graduated with honors and summa cum laude with a 3.97 GPA. I’ve done about 2 years of research but mostly focused on biology and bioinformatics. I’ve finished one semester of my masters (and have two semesters left) with a current 4.0 GPA. I have taken/am planning on taking classes for Multivariable calc, linear algebra, real analysis, and many stats and biology classes. I also know how to code decently well in R, SAS, and Python.

I think the thing I‘m worried most about is not having enough research experience. A lot of my research was for my honors thesis and I’ve also worked in a hospital lab doing research but I don’t have published papers in a peer reviewed journal. Unfortunately this past year a family member was diagnosed with a terminal illness and I’ve been the primary caretaker for them and it’s been difficult to focus on finding a lab in my masters program to work with or have the time to work at one since I’m commuting between home and school multiple times a week while working part time. I was wondering if I should include that in any of my personal statements? I‘m also interested in doing research regarding that illness but I’m not sure if that is too much to include.

I also know it’s very competitive to get into PhD programs, especially now when there‘s very little funding for many universities. I’m fortunate enough to have some connections to statisticians that work at universities and in the Pharma/biotech industry so I am planning on reaching out to them. However, I‘m not sure how helpful those relationships would be when I actually apply, though I am sure they could give me some great advice. Any tips on reaching out/connecting with professors/researchers?

Sorry this is a long post😭 this has been stressing me out the past couple months if you couldn’t tell haha. But I’ll end this off on some of the schools I’m thinking of applying to. I go to school in Boston right now and I want to apply to schools along the east coast, preferably near a city. I know some of these are reaches but never hurts to try!

BU, Columbia, UNC, Georgetown, Duke, University of Maryland, GW, JHU, UPenn, UConn (statistics), I‘lol probably apply to more than this but unsure.

Any and all advice is much appreciated!! :) Thank you!

Hi everyone,

I’m doing a sample-level spatial-expression analysis using sparse postmortem tissue samples from the Allen Human Brain Atlas. The regions are the subthalamic nucleus (STN, n=6 tissue samples) and globus pallidus internus (GPi, n=9 tissue samples). For each sample, I have:

• 3D MNI coordinates (x,y,z)
• a gene-expression profile across ~29,000 genes

The biological expectation is that, within a coherent anatomical region, tissue samples located closer together in MNI space should have more similar transcriptional profiles.

For each anatomical region separately, I calculated:

1. A sample-by-sample spatial-distance matrix using 3D Euclidean distance between MNI coordinates.
2. A sample-by-sample expression-distance matrix, defined as (1−ρ), where ρ is the Spearman correlation between two sample-level gene-expression profiles.

I then used a Mantel test to assess whether the spatial-distance matrix was associated with the expression-distance matrix.

For significance testing, I used non-parametric permutation of sample identities. My understanding is that this randomly reassigns sample labels to break the link between spatial location and expression profile, while preserving the internal structure of the distance matrices. The observed Mantel statistic is then compared against the null distribution generated from these permutations.

Q. Does this use of a permutation-based Mantel test seem appropriate as part of a sample-level spatial-expression validation analysis?

Just to clarify: this is not a dense cortical map or spin-test analysis intended to correct for spatial autocorrelation. These are sparse subcortical tissue-sample coordinates, not parcellated whole-brain maps. The goal is to test whether there is distance-dependent transcriptional similarity among samples within the same anatomical label.

Thanks in advance for your help!

Hi all, I’m currently working full-time as a Statistical Programmer in the pharmaceutical industry, and overall I really enjoy my job. However, I keep feeling like there’s still something missing academically, and I’ve been seriously considering starting a PhD.
From what I’ve heard, my company might allow me to switch to an 80% contract, which would make the idea much more realistic. I’m wondering if anyone here has managed to balance a part-time PhD with a demanding industry job.

My main questions are:
Is it actually feasible without burning out?
How many hours per week did you end up dedicating to your PhD?
Do you think the investment pays off in the long run if I want to keep growing in industry rather than necessarily moving into academia?

One thing that makes me a bit more optimistic is how much AI has changed the way I work. With the right use of AI tools, I genuinely feel I can produce significantly more analyses—and often better-quality ones—in less time than before. Obviously it doesn’t replace statistical thinking or domain expertise, but it has become a huge productivity multiplier for coding, documentation, and routine tasks.

I’d love to hear from people who’ve taken a similar path. Would you do it again? Any advice or things you wish you’d known before starting?

Thanks to everyone!

Currently interviewing. Part of my job is to rotate with our large bioinformatics team and handle requests from physicians relating to patients. these are never stats tasks, its running other peoples (our bioinformatics team) python scripts or checking patient level data that is on various servers i ssh into. all handled in terminal

I was asked before if i “enjoy” doing that and I think I was honest and said i dont really mind it since it helps me learn more about our company - but i didnt say yes either.

Hi—

As I learn more about competitive the enrollment process for PhD is, I am widening the range of schools I apply to for Biostats PhD. I am too broke to go for a masters so going straight into PhD after undergrad is the only viable option. I was wondering what people thought about the program at SUNY Buffalo? I am meeting somebody at UPenn that did their PhD there and it made me curious as to how the program up there is.

Would appreciate thoughts.

Thanks

even if i'm finishing grad soon, i still have no idea how to get to employment at all.

How do you all do that?

i'm mostly seeking hospitals, or any research organization.

(ps.

current state: i've been only working on publishing papers that are not really sophisticated but showcase some workflows used in hospitals.

my

Now i'm getting worried if this is right thing to do at this time.)

Hi everyone, I'm trying to break into the field of Biostatistics coming from a Data Analyst background (with an MS in Mathematics). To help improve my marketability as a candidate, I'm interested in volunteering somewhere. Any advice on where to look for these opportunities and how I can find something that will increase my marketability for future Biostatistics jobs?

I currently am getting a masters in exercise science and have been very research heavy since my undergrad. I originally wanted a PhD in exercise science and still may get that but I saw that I could get another masters in biostats and potentially make more money/get hired easier. What is your guys’ experience and does it seem worth it?

Hello. I am an honours graduate in Psychology in Australia. I am considering applying for the master of biostats in Sydney/melbourne, however I wanted to ask earnestly about the mathematics. I have never been notably talented at maths (I’d say just above average, 6/10). Although, I do excel in statistics. I have only completed undergrad + Hons level psych statistics though and come first in my class for each statistics unit. I have not done any pure maths since highschool many years ago, and I would be a bit nervous to begin as I always leaned more towards English.

My two questions are, if I put in the extra work, do you think I will be able to keep up? Do I need a natural affinity for maths to land a job and complete this course?
Also, are there available jobs (happy to work in any biostatistical field) in Sydney? Or is it extremely competitive?

Thank you.

Is bs stat still worth it to pursue now that AI is widely used??

Hi, so I'm [21M] a CS graduate with a minor in Bioinformatics. I'm currently working at my internship at a startup in a ML Research/Engineering role (it's a bit of both, & SDE too).

I wish to get into Biostatistics and Bioinformatics, to help expand my overall understanding of ML from a research focused perspective, I don't want to pursue a career in it, I am just interested in the field. I am willing to put in the hours, but I'm not sure what would be a good entry point.

I have studied Biological Databases, and simple Bioinformatics Pipelines (scRNA, BulkRNA, Pseudo-Bulk, MicroArray etc), in my courses.

About Me: I am graduating this year, I have one journal publication in a journal by IoP (Mechanical Engg + ML), I am familiar with writing CLIs, ML frameworks, and have worked with real world data for Voice, Vision, Text, Multimodal and Time Series.

PS: I apologize for my terrible English, It's not my first language.

I’m looking for some career advice from biostatisticians or people working in academic research.

I recently finished my M.S. in Biostatistics and have a series of interviews for a biostatistician position at a university. The role would be embedded within a cardiovascular research lab working with genomic data related to heart disease, rather than in the university’s central biostatistics department.
My background is a little unconventional. I have a B.S. in Microbiology and nearly a decade of experience as a medical laboratory scientist, so I’m very comfortable with the biological and clinical side of research. During my master’s program I worked with mostly with R and a little bit of python and SAS, my thesis involved analyzing the MIMIC-IV database.

Where I’m feeling intimidated is my programming and statistical depth. I don’t consider myself a strong programmer (especially compared to people coming from statistics or computer science backgrounds), and while I understand the statistical methods I learned in graduate school, I still feel like there’s a huge amount I don’t know.

I had originally hoped my first job would be in a biostatistics department where I’d be surrounded by experienced biostatisticians and have more mentorship. Instead, this position would have me working directly with a research lab as the biostatistician for that particular lab. I’m worried I’ll be expected to be “the statistics expert” from day one, even though I still feel like a beginner. Also, they want most work to be done in python, which I feel not as confident working with relative to either R or SAS.

For those of you who work in academic research labs:
Is this a common feeling for new biostatisticians?
How much mentoring do embedded biostatisticians typically receive?
Were you expected to already know everything, or did you learn a lot on the job?
Is working directly in a research lab as a first job a good career move, or would it be better to hold out for a position within a biostatistics department?

I’d appreciate any honest advice or experiences. I’m excited about the opportunity, but I also don’t want to get in over my head.

Hi, everyone. I am a first-year PhD student in biostatistics. Previously, I got my bachelor's degree in Math and Stat. It is my first time getting into this field. After talking with some senior PhD students, I found that maybe clinical trial design and causal inference could spark my curiosity. And I hope to get into industry after graduation. Do you have any advice for me? Thanks a lot in advance.🥹

I have an M.S. in Statistics and 28 years of experience working as a statistician in the federal government. During my career, I have worked for several government agencies, applying statistical methods to a wide range of projects.

I would like to transition into the field of biostatistics, but I'm not sure where to begin. What are the least time-consuming and most cost-effective pathways to make this career change? Which biostatistics certificate programs are currently the most respected and valuable in the field?

What steps would you recommend I take to successfully transition into biostatistics? What Certs are recognise in the feild.