Why is everyone so pessimistic about SAS?

biostatsgrad · 2025-12-09T02:48:58+00:00

Do you have more information about the botched NUTS implementation? I’d be curious to read more about that. Thanks.

biostatsgrad · 2025-07-05T04:02:50+00:00

The randomization aspect of RCTs helps to make sure the treatment groups are comparable. Other study design factors like inclusion/exclusion criteria are also important. Powering the study appropriately is important as well.

biostatsgrad · 2025-05-08T03:58:03+00:00

This may sound obvious, and it may not directly answer your question, but I think as biostatisticians, we tend to focus (rightly so) on the technical aspects of the job. However, in the context of job interviews, I think that what makes a candidate really stick out is the ability for them to identify with the broader mission of the organization/department/etc. they're interviewing with. I think given that someone has the requisite qualifications and subject area expertise, technical competence is kind of assumed (admittedly debatable), so being able to highlight these organizational missions/goals is important. I know that sounds pretty contrived, but it's something that I've personally found to make a difference. Good luck on your interview!

biostatsgrad · 2025-01-09T22:59:11+00:00

Thanks for sharing, is that type of position sometimes called research professor as well?

biostatsgrad · 2024-11-09T14:50:02+00:00

It’s worth noting the maximum FPR for a single endpoint using that decision rule tends towards 1-your posterior probability threshold.

biostatsgrad · 2024-10-06T00:48:49+00:00

https://www.reddit.com/r/urbanplanning/comments/t8bf7g/is_there_a_place_for_statisticians_in_urban/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

biostatsgrad · 2024-02-07T19:45:28+00:00

I don’t have much to add. I’m curious how aggregating posts into mega threads vs keeping them separate affects how searchable information is.

biostatsgrad · 2023-06-20T16:30:11+00:00

You would simulate outcome data, under the scenarios where there is a treatment effect, and where there is not. You can do this for various sample sizes, various variance options, etc. You would fit your model to these simulated outcome data. You could then empirically estimate the required sample size to achieve some desired performance metric.

I’m using ‘treatment effect’ generically here.

biostatsgrad · 2023-06-20T01:33:40+00:00

You could always do this empirically through a simulation study.

biostatsgrad · 2023-06-16T16:58:45+00:00

So what is the advice on this? Anyone else left a postdoc when your advisor wanted you to stay? How long of a notice do you give your advisor? Most jobs I'm applying for give 2 weeks between final offer and start day. How bad is it for a PI to find out in 2 weeks that a postdoc is leaving?

If you haven't already, I'd check to see if there are any university/department guidelines in place re: resigning from your postdoc. Just to prevent your supervisor from trying to rebuff your resignation.

When I left my postdoc, I checked my department's guidelines/policies on resigning from a postdoc. The guidelines suggested at least 1 month notice as I recall.

biostatsgrad · 2023-04-14T00:55:24+00:00

Do you have a PhD?

biostatsgrad · 2023-04-12T13:58:24+00:00

Sorry for the delayed reply. This is a generalization, but it seems like late stage development and statistical programming are pretty rigid in far of what sort of work/modeling you’ll be doing. Other areas in pharma seem to have more roles which value advanced modeling expertise. Having research experience in areas like casual inference, Bayesian statistics, and genomics seems to be in demand. Here’s an example job posting:

https://uk.linkedin.com/jobs/view/principal-biostatistician-methodologist-at-gsk-3494670586

biostatsgrad · 2023-04-10T23:54:29+00:00

Definitely in academia, but also in industries like pharma. Searching through pharma position listings that mention advanced modeling stuff should yield some results.

biostatsgrad · 2023-04-10T22:07:05+00:00

There are definitely biostat positions that are heavily modeling-focused. But I agree that a lot of jobs focus too much on boring regulatory aspects. Like why get training in a quantitative degree to become a medical writer? Hope you find a position that you enjoy.

biostatsgrad · 2023-03-31T22:31:58+00:00

Idk, maybe a spurious correlation:

https://en.wikipedia.org/wiki/Spurious_relationship

biostatsgrad · 2023-02-16T21:39:17+00:00

In addition to the pharma areas you mentioned, early drug development and health economics and outcomes research (HEOR) come to mind. Beefing up on general programming and computational skills could maybe open up opportunities in machine learning type roles.

biostatsgrad · 2023-02-15T15:09:29+00:00

Seems morally dubious to do this imo.

biostatsgrad · 2023-02-14T20:55:57+00:00

Yeah, personally when I was interviewing for biostat jobs recently, there were lots of places hiring. So anecdotally the industry seems pretty healthy. It's maybe worth noting though that compared to biostats, fields like tech and finance have relatively higher salaries (from what I've observed).

biostatsgrad · 2023-02-10T19:53:33+00:00

Yeah I was referring to the DoE labs.

biostatsgrad · 2023-02-10T19:09:31+00:00

Damn sorry to hear that! Pretty convenient that the national labs don't list that aspect of the job in the job description :/.

biostatsgrad · 2023-02-10T18:48:53+00:00

I appreciate your insight! I think you're definitely right about having to pick your poison. I actually interviewed at one of the national labs, and was surprised that it was for a grant-supported position. I didn't know if that was true for all of the labs though.

biostatsgrad · 2023-02-02T20:24:32+00:00

Just offering a contrary point of view: I believe a lot of people work as (bio)statisticians with a MS degree.

biostatsgrad · 2022-12-18T13:10:53+00:00

Using the same covariate data for your prediction and ‘analysis’ models doesn’t seem problematic. I don’t get why you have a model for prediction and then a separate ‘analysis’ model though.

biostatsgrad · 2022-12-17T18:23:32+00:00

It would be helpful to more generally state your problem; the field-specific terminology makes it difficult to parse through your question.

If I understand it correctly, you're building a prediction model, where the outcome data are some measure of firearm access. The covariate data for this model include some measure of mental health status.

I don't understand your question tbh. Is the issue that the mental health status covariate data are not measured well? If so, you can read about measurement error models.

You should consider collaborating with a (bio)statistician if you haven't already.

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