Hi everyone,

I’ve (finally) advanced to the dissertation proposal phase of my PhD in public health policy!

As I’ve gotten deeper into this work, I’ve found myself increasingly interested in the relationship between theoretical constructs, their operationalization, and modeling. I’ve been most influenced by King, Keohane, and Verba (KKV) and Charles Ragin (research design, causal inference, case comparison).

Does anyone have other sources they would recommend? I’d also be interested in hearing how others think about moving from a theoretical construct → operationalization → model specification.

Looking for advice on an ideal textbook sequence for someone learning on their own. Have done most of Blitzstein’s book for probability/distribution theory.

Up next I want to cover
- Statistical inference
- Linear regression
- ANOVA and variants etc.

What order would you recommend? Anova pre regression or regression then Anova as a special case?

What textbook stack would you recommend? Something that assumes nothing else but say Blitzstein’s probability

I hate big reference books. I like reading something almost cover to cover in chapter order where the author’s voice is teaching you linearly. Blitzstein was good for that.

Marschner assumes t tests and anovas already. Rice? Seems to cover inference and then does ANOVA/categorical before regression, which then seems like an afterthought. Can’t stand Wackerly.

Overall … What is an ideal pedagogical sequence of textbooks after which someone would have a sound understanding of most day to day clinical biostatistics for a university/hospital department? Ignore R/comp sci side; Wickham covers that well.

Hi,

I have worked for the last 5+ years as a biostatistician at a CRO. I HATE every aspect of the job. One, I am not at all passionate about biostatistics (should've figured that out earlier) and two, I find the job to be so monotonous and technical which I do not enjoy.

I am looking at the most likely pivots to less technical pharma jobs and struggling to find things that I can break into. I have been thinking RWE, HEOR, market access but have not heard back from any positions I have applied to. They all want 5+ years of specific experience in that field.

Any thoughts on job switch options that would value my biostatistics background would be greatly appreciated. Thank you!

Hola! hice un pregrado en psicología, luego una especializacion en epidemiologia y voy terminando una maestria en bioestadistica. Me gustaria saber como esta el mercado laboral en este momento y si alguien podria darme algunas recomendaciones para conseguir trabajo pronto. Gracias!

I recently started a Biostatistician position, and I’m realizing that the role may involve quite a bit more bioinformatics than I originally expected.

The team works with complex genomic data and has a number of Python-based pipelines already built. From what I’ve seen so far, it seems like they primarily need someone to operate and maintain these pipelines, troubleshoot issues, make modifications when necessary, and potentially develop or improve parts of them over time.

My background is in biostatistics. I have experience with statistical modeling, regression, survival analysis, data cleaning, and programming in Python and R, but I don’t have formal training or professional experience specifically in bioinformatics or computational genomics.

I’m trying to figure out how much of a gap there is between my current skill set and what they’re expecting from me. For people who work in bioinformatics or computational genomics, does this sound like a reasonable role for someone coming from a biostatistics background?

I’m also wondering what I should prioritize learning. I’m familiar with Python, but I haven’t worked extensively with genomic file formats, bioinformatics tools, workflow systems, or genomic pipelines.
Would this be a good opportunity to develop bioinformatics skills on the job, or is there enough specialized knowledge involved that I should be concerned about the mismatch between my background and the position?

I’d especially appreciate advice from people who have transitioned between biostatistics and bioinformatics, or who have worked in roles that overlap the two.

Currently, I am very interested in a career in biostatistics. I have a PhD in Chemical Engineering, but I don't have any background in biology/Bioinformatics. I do have some programming experience with Python and some basic experience with statistics, probability, and sampling methods (everything is very, very basic, for experimental design). Since I have an engineering background, I also have a good foundation in calculus and algebra.

However, I discovered that I really like statistics, and I would like to move into a career where I can actually do statistics. I saw that biostatistics could be a good option, so I would like to know how I could transition into this field.

My idea would be to maybe get into an entry-level role first, gain some experience, and then potentially do a Master's in Biostatistics or Public Health if it is necessary to move into bigger roles. The main thing is that I would like to understand how I can get into those initial entry-level roles without having to immediately pursue another degree.

However, I am open to taking an online course if it would give me any additional advantage. For example, I was looking at the Biostatistics in Public Health Specialization on Coursera. Would taking something like this be helpful for someone in my situation?

I would really appreciate any advice from people who have made a similar transition or who work in biostatistics. I am mainly trying to figure out how to get that first opportunity and start building experience in the field.

I'm a student who plans on becoming a biostatistician in the future because of the above average salary it has to offer. I'm currently in high school and I have plans for doing undergrad in the US as a statistics or biology major.

Although I've researched about what the job mainly is like, I would like to hear it first hand from someone actually working and experienced in the field. How is the job like? Is it fun? Stressful? Was the path to getting there difficult? How good is the salary?

Please drop your experiences below, it would be very helpful.

sorry to put another SAS question in this sub …

hi! i’m an undergrad maths&stats student in the UK and have decided i wanna go into biostats. i’ve been able to take classes in R and python but have had no SAS exposure in my undergrad. i’ll be applying for msc medical statistics courses for 2027 entry and all the unis i’m interested in deliver their computational stats courses in R, python & stata, so i wont be abke to picm it up then either…

i am aware SAS is a much needed skill esp in clinical trials which is something i am v interested in. i have the ondemand for academics in my browser but the only learning resources i can find are youtube videos. its datacamp free access week atm and there aren’t many courses on there either, feeling quite stuck on where to begin. pls lmk if you know any great free SAS learning resources!

I am trying to break into the field, looking for experience, and I kinda feel like I have no idea what I'm doing. I just wanna talk in depth with someone in the field and in the area about what exactly to do.

If anyone is open to that, please private message me lol.

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.

R for CDISC submissions has been promoted for several years now. More importantly, it is no longer just a slogan—many organizations have actually started using it in real projects, particularly for CDISC datasets and TFLs.

That raises an important question: What have we learned so far, and is it time to reconsider some of the original arguments for adopting R?

This discussion is limited to R for CDISC datasets and TFLs, not R used for specialized calculations by biostatisticians.

One of the early arguments was:

Students cannot learn SAS sufficiently at university, but they are already familiar with R.

That may be partly true, but being familiar with base R is very different from being familiar with clinical programming packages, CDISC implementation, controlled terminology, traceability, and submission requirements. Students may know R syntax, but they still need to learn the clinical programming ecosystem—and many of those packages are relatively new, constantly evolving, and not necessarily intuitive.

Another argument was:

R is cheaper.

But is it really cheaper for submissions?

Validation, reproducibility, environment management, package qualification, version control, auditability, and regulatory documentation can require substantial effort. Some of the work that was traditionally handled within established SAS programming processes may now shift to programming teams and IT. The cost may not disappear; it may simply move to different parts of the organization.

A third argument was:

R benefits from strong open-source community support.

That is certainly true. The R community is active and innovative. However, the rapid pace of package development also creates challenges. Functions may be deprecated, behavior may change, dependencies may break, and code that worked two years ago may no longer work without modification. I understand `renv` can lock the enviroment for the project, but does it also means Stat_programmer must pay more attention on package learning?

R clearly has meaningful applications in clinical research, especially for exploratory analyses, visualization, interactive reporting, and statistical methodology. However, many of these advantages seem particularly relevant to biostatisticians rather than necessarily to standardized CDISC submission production.

Compared with SAS, R is often more flexible. But flexibility can also increase the coding workload. Statistical programmers may need to spend more time managing code, package versions, validation frameworks, and technical infrastructure—rather than focusing on the scientific or regulatory objectives of the project.

I am 30 yrs female and i am planning to transition from CS to Biostats and need some genuine suggestions whether i could find any jobs and will it be good fit for me :

1. I don’t have any actual industrial experience in Biostats but I have a master degree in statistics. There i took courses like Survival analysis, biostats, Bayesian analysis and longitudinal analysis. It was 3-4 yrs ago but i can grasp the concept .

2. My industrial experience mostly involves data analytics. I have a knowledge on R, python and i am
   Planning to take SAS certification.

3. I don’t have any publications

I am not sure how the job market is rn or am i not finding the opportunities so far.
So i genuinely need a suggestion. I am tired of this CS jobs being unstable and have to worry every single day about layoffs. Moreover i do enjoyed stats in my college.

​

Hi everyone,

I’m planning to apply for Statistics PhD programs for Fall 2027 and would really appreciate advice from people who have gone through the process or are currently pursuing a Statistics PhD.

My current profile is:

1. BSc in Statistics: CGPA 3.6+/4.0

2. MSc in Statistics: Final examinations completed; only the thesis submission remains

3. IELTS: 7.0

4. Currently working on my Master’s thesis

5. I’m also working independently on a research project related to Biostatistics.

My main concern is that I don’t have much formal research experience beyond my MSc thesis and the research project I’m currently working on.

I’m particularly interested in pursuing a PhD in Statistics/Biostatistics/Epidemiology, preferably with full funding.

I would really appreciate your advice on:

1. How should I strengthen my profile before applying for Fall 2027?

2. Is my current profile realistic for getting a fully funded Statistics PhD?

3. How important is formal research experience/publications for Statistics PhD admissions?

4. Would completing my ongoing research project and presenting/publishing the work significantly improve my chances?

Thank you in advance for any advice!

Silly question but worth asking. What if you are really good in biostats and okay in math? Is there a remote chance of passing domain 1?

I have 8 years of CRO experience as biostatistician, and am considering a change. I have always wanted to experience working on the Sponsor side of Pharma, but have had no luck getting my foot in the door. It seems most Pharma has moved operation back in office (of which I’m located remotely in an area with little Pharma operations) or hire remotely but are only interested in folks with prior sponsor experience. Due to this, I have considered looking at chasing an FSP role with the hope of parlaying that experience into something permanent at a Pharma company in the future. My concern is two fold, first I currently have a stable (albeit sometimes frustrating and lacking work life balance) position and second I worry that the fsp experience won’t be viewed as sponsor experience / even more opportunities become in office only eliminating my options further. I love the idea of working for one company, but worry the stability could be to volatile as I am the main source of family income. Any insight or thoughts would be greatly helpful.

For those with FSP experience, on a resume do you highlight you worked for the company you were embedded in or the company that facilitated the hire and paid the salary?

so i’m about to start my second year of my masters and i’m feeling a little lost in terms of the trajectory i would like to pursue immediately after graduating.

i 100% definitely want to work. im not interested in pursuing a PhD immediately after my masters, i want to work for at least a couple of years and if anything i’ll let that guide my decision to pursue doctoral studies or not.

i always figured i’d look for a job at a pharma company or a CRO since i’ve always been interested in clinical trials, i just completed an internship at a small biotech (though my role was mostly basic data analyses, validation of SDTM and ADaM datasets, quality control, but nothing necessarily involving advanced statistical modeling), and entry level pay is generally the best in this industry for master level biostatisticians. however i have grown increasingly fearful that i would not feel fulfilled in a pharma/CRO role at a masters level because i don’t want to be stuck in a purely analysis/reporting position. im more than happy to start off doing these types of tasks given i’m not very experienced but i would eventually want to be able to work closely with other statisticians, get mentorship from them, and especially be able to contribute in meaningful ways to methodology and research instead of just execution of SAPs. i would want to work in a place where there’s an incentive to keep up with new and exciting methodological research, where there’s value in attending seminars and somewhere where i feel like i’m continuously learning and growing intellectually.

so at some point in the last few months, i became very interested and fascinated by casual inference and semi parametric stats, model-robust inference, and how these all come together to shape clinical trial design and analysis. and sadly, i convinced myself that the goals i mentioned before and my interests would not be attainable if i go and work directly in industry given i’ll only have a masters and that maybe my best bet would be to look for full-time work in academia or affiliated institutes within research labs or groups, specifically teams that are methodology-based. however i’ve been told that these jobs, if they even do exist at a master level, are often grant-funded and can be incredibly insecure.

my current plan of action is to just reach out to as many faculty, PIs and research-related places i can to introduce myself, get their perspectives and see if i can use that guidance to hopefully secure a position.

i would love if you guys could tell me based on your experience if i have any misconceptions with the way that i’m looking at things at the moment, if there’s any info that i’m missing, if there’s any general advice that i could use or if you have been through a similar phase in your career.

thanks!

TLDR: I did sociology in undergrad, loved quantitative research, am doing a post-bac at the NIH now and am seeking advice on things I can do during my fellowship to ease the transition—potentially a project?

Hello! First off, I know the transition sounds a bit insane but hear me out. I graduated with a sociology degree this past May and started to realize that I really, really liked the data analysis part of it. I did a lot of independent quantitative work mainly using survey data and also designed experiments embedded in surveys—mostly in political attitudes until my senior year when I did my first public health project and loved it.

I have some experience with multivariate linear and logistic regression, ANOVA, etc. For coding, in sociology we used Stata, however post-grad I’ve been working at the NIH as a post-baccalaureate research fellow in a psych/public health lab and have taught myself R. (All views are my own)

Anyways I realized I think biostatistics is a good mixture of my quantitative research/public health interests and am pursing a masters in two years after my fellowship. I know I still have a significant math gap—I’m taking calculus courses at a community college now to get the pre-reqs out of the way and have been doing pretty well. I was just wondering if anyone in the field had advice for what I could do during my fellowship to help me transition into the field? Also, do you guys think it’s a feasible option for me? I was thinking about doing an independent project using real world data since I’ve heard that people are asking for experience with RWD now. It would still probably be socy/public health related rather than strictly biological but I thought that might be good for my portfolio/grad school apps? Thoughts?

Sorry that was a lot of questions lol I’d appreciate any insight!!

Hi,

I am currently running my first solo experiment and I have four tanks of animals. They must all be within their own tanks to avoid contamination between my experimental tanks and my control.

Would it have mattered had I done my control before or after this experiment if they are in a separate tank? The conditions are being monitored daily and all parameters are within the optimal range for my animals.

I think if I repeat this it would be easier to have one less tank and to do my control separately. Does the control have to take place at the same time as this experiment given the circumstances? I would be keeping the groups of animals meeting the same criteria & being sourced at the same time.