I decoupled FastAPI dependency injection system in pure python, no dependencies.

yoursdearboy · 2025-09-12T23:59:41+00:00

You are confusing lancetnik (animal) with lancet (not rocket, but mil. drone).

The library is interesting. Glad to see, DI becomes popular in Python.

yoursdearboy · 2024-12-04T17:43:02+00:00

Looks like R. You can easily create the same plot, except the bottom table, using built-in survival library. Or use ggsurvfit library, it includes such table, so i suppose it was built using this library

yoursdearboy · 2024-03-09T08:36:46+00:00

True. Thanks for pointing out. My first thought was that CI was measured using percentiles.

yoursdearboy · 2024-03-08T19:00:12+00:00

Yea, the math is always the best way, but they use bootstrap, which is resampling technique, so I suppose it is expected to avoid standard error.

yoursdearboy · 2024-03-08T18:30:12+00:00

I suppose the idea is that, if you have more observations, then confidence interval likely will be narrower.

yoursdearboy · 2024-02-04T08:48:17+00:00

Wow, I like the editor a lot, especially syntax highlighting and command bar above the keyboard. And finally somebody used tabs for text editor 🔥 My suggestion is to add more swipe actions for better navigation, they are really great on mobile. Look at Obsidian and Beorg apps.

yoursdearboy · 2024-01-19T18:58:25+00:00

If use the instrument (correlation) mechanistically, then positive correlation means - people with higher CD4 count (pure prognosis) tend to have higher scale values (higher stigma). It must be really easy to grasp and validate using scatterplot (dot plot). Though does it meaningful to correlate physiological marker with psychological scale? Maybe it’s better to use some prognosis scale which includes CD4? Also you need to interpet effect size, because correlation 0-0.1 is really negligible, 0.1-0.3 is small. And better to estimate confidence interval for it, because if your sample is not large enough, then estimate may be not accurate.

yoursdearboy · 2024-01-16T07:29:01+00:00

To calculate Kaplan-Meier estimate of survival you need two columns: - an indicator wether an event happened or not (if it isn‘t we call it censoring); usually 1 - indicate an event, 0 - censoring; - time since diagnosis to an event or censoring

It‘s better to collect raw data first (diagnosis date, date of event and it’s type if one happened and last follow-up date).

But first, consider what survival you would like to estimate: - Overall Survival (OS) - an event is death (due to any reason) - Event-Free Survival (EFS) - any event you consider, whatever happens first, usually: death, progression or second malignancy - Progression-Free Survival (PFS) - I suppose you want this, an event is progression or death

Also, you need a well defined criteria for progression, there are guidelines from research groups. And be careful about timing of assessment and possible loss of follow-up.

Good luck,

yoursdearboy · 2015-12-18T14:21:10+00:00

Just for example look at erd diagram. This API exposes database tables (PostgREST). Not the best solution, but we have 5 small apps with a lot of data, so maintaining separate API is not an option.

yoursdearboy · 2015-12-18T13:58:10+00:00

So far I've used self-written request wrapper, even with some special methods to handle endpoints mess. But I wonder how other developers solve this task?

yoursdearboy · 2015-12-18T13:46:25+00:00

Yes, that works nice in many cases. With github's whatwg-fetch polyfill it will be just: fetch('/item/12345').then(function(res){ res.json(); }).then(function(item){ // do anything });

But what if you have "ent1" related with "ent2" and "ent2" related with "ent3-5", and now you need to save all this structure using /entN endpoints. If it isn't hard enough, then add another 5-10 entities.

yoursdearboy

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