Evidence of Jones' Guilt

@kflo you gotta admit. Even though we disagree a lot throughout this thread you gotta give me props for

1) recreating Cowan's model (for the accumulation segment)

and

2) Fitting a third order solution to a differential equation to the data (after all previous orders were said not to work so I took the next step)

I know you give me props, I just wanted to pat myself on the back again :)

Even Cowan said he had trouble constructing the model (solely for the accumulation part) which I was able to do with relative ease.

BTW I didn't copy a script from stack exchange for exponential fitting. I ready the theory and created my own.
That definitely took this whole thread in a decidedly un-Sherdog direction. Love it.
 
That definitely took this whole thread in a decidedly un-Sherdog direction. Love it.

Thanks man.

I’ll admit my views have changed since I first started discussing this topic. For instance I thought “pulsing” (detectability vs. non-detectability from sample to sample in my judgement) was contrived and only recently have I conceded that this is not the case. I don't think fat sequestration is the mechanism but rather changes in specific gravity.

I think previously I was just throwing shit at a wall tbh and you and @kflo pointed much of it out so thank you for that. But now with the new paper and subsequent formulation (in terms of a solution to a linear non-homogeneous third order differential equation that resulted in a good fit to experimental data) coupled with Cowan’s model (and my extended assumption) I think I’m making progress. I'm not going to assert that I'm right but I think at least now I'm cooking with gas.

@kflo was right, previously I was interpolating/extrapolating with various sorts of methods without knowing what the data was like in between (i.e. not controlling for bias and variance) resulting in "over-fitting". I think that defect is now alleviated with a proposed theoretical model (third order differential equations as opposed to second order differential equations).

Again, I still may be wrong but now my analysis is more reasonable. I don't think it's a coincidence that moving up to the next logical step of complexity accounts for what the previous level of complexity couldn't account for.

I would really LOVE to get my hands on all of the data from the new paper. It would help to account for inter-individual variability and the effect of changing doses.

I still have a lot left to do before I can present this to any serious scientist as far as communicating the ideas effectively and making a general/versatile program (and possibly app).
 
@kflo @fzoid4454 I'm not finding a significant difference between single and multiple doses. If you recall I was able to reproduce Cowan's model for the "introducing multiple doses part". However Cowan's model does not account for decay after the "introducing multiple doses part" so I had to make some assumption. Simply put I made the assumption that "after accumulating it decays as it would have had it not accumulated". I think that is perfectly reasonable.

Keep in mind that despite the lack of a "jigsaw saturation pattern" the for loop I implemented was the exact same. The only reason it looks different is because in prototypical previous examples we introduced new doses at a slower rate than the decay rate. For turinabol m3 which has a very slow decay rate, we introduce doses much more frequently in relative terms.


Cowan's model for the accumulation segment:

rg41EWg.jpg


My extension of Cowan's model using the aforementioned assumption:

LQvCVjB.jpg


283 days for a single dose of 20mg , 292 days for 30 doses of 20mg daily

This case may be pathological, I need to come up with a procedure for the decay segment that generalizes to other data sets.
i have a few questions - for the first graph, how are you validating that it is consistent with cowan's model? i don't see anything in the other paper that presents a similar shaped graph. so it's difficult to verify.

for the 2nd graph, it just doesn't make any intuitive sense. you actually have a crossover of the 2 lines at around 50 days. it just doesn't seem plausible that you would dose for 30 days and actually have LESS by about day 50. the graph just doesn't make sense to me. at day 30, what are the amounts? i can't plot it visually.
 
i have a few questions - for the first graph, how are you validating that it is consistent with cowan's model? i don't see anything in the other paper that presents a similar shaped graph. so it's difficult to verify.

for the 2nd graph, it just doesn't make any intuitive sense. you actually have a crossover of the 2 lines at around 50 days. it just doesn't seem plausible that you would dose for 30 days and actually have LESS by about day 50. the graph just doesn't make sense to me. at day 30, what are the amounts? i can't plot it visually.

Good points. I'll run the exact same script but with a different sets of data. Namely the parameters illustrated by Cowan in that one anti-doping conference document. You'll have to trust me that I'm doing it faithfully and the for loop structure is the exact same for the accumulation part. (Addressing the first graph)

Likely the "decay part" will have to be different but analogous to the one I ran above; If the final data point in the accumulation phase is higher than anything in the single dose graph the notion of "decaying as it would be at that quantity had it not accumulated in the first place" needs to me generalized to "decaying as it would from one of the previous doses iteratively going backwards" (or something to that effect). (Addressing the second graph)

As for the minute crossover (the decay part with my assumption) I think that's inevitable when you have a local minima in graph 1, shift it over a small amount, and compare it to the old graph. This effect is very minute imo.

I can't divulge the logic statements (if blocks and for loops) here without the risk of someone stealing my program and taking credit here (on the off chance that I'm offering something novel I'd like to get credit for it). This limits our ability to have a thorough discussion but unfortunately those are the constraints I'm working within.



As for the first single dose graph we are at 28 pg/ml on day 30. For the multiple dose graph we are at 57 pg/ml on day 30.

I'll produce the graphs of the prototypical examples from Cowan's presentation shortly.
 
Good points. I'll run the exact same script but with a different sets of data. Namely the parameters illustrated by Cowan in that one anti-doping conference document. You'll have to trust me that I'm doing it faithfully and the for loop structure is the exact same for the accumulation part. (Addressing the first graph)

Likely the "decay part" will have to be different but analogous to the one I ran above; If the final data point in the accumulation phase is higher than anything in the single dose graph the notion of "decaying as it would be at that quantity had it not accumulated in the first place" needs to me generalized to "decaying as it would from one of the previous doses iteratively going backwards" (or something to that effect). (Addressing the second graph)

As for the minute crossover (the decay part with my assumption) I think that's inevitable when you have a local minima in graph 1, shift it over a small amount, and compare it to the old graph. This effect is very minute imo.

I can't divulge the logic statements (if blocks and for loops) here without the risk of someone stealing my program and taking credit here (on the off chance that I'm offering something novel I'd like to get credit for it). This limits our ability to have a thorough discussion but unfortunately those are the constraints I'm working within.



As for the first single dose graph we are at 28 pg/ml on day 30. For the multiple dose graph we are at 57 pg/ml on day 30.

I'll produce the graphs of the prototypical examples from Cowan's presentation shortly.
But what logic would dictate he’s at 57 pg/ml on day 30 of a 30 day cycle? Why would the pattern of the 30th dose with prior accumulation be so different from the 1st dose?
 
@kflo I think I fucked up with my for loop, I combed back through my accurately reproduced Cowan model script, made appropriate adjustments to my current script and I got a peak of 2,977 pg/ml at 30 days for a multiple dose. I think this loading period graph is correct now.

aRgL1xL.jpg


If Cowan's model is applicable and I am really inclined to believe it is because it was applied in Dylan Scott's case then "trace amounts" is really a legitimate thing. Well to be fair one of Cowan's models was applied but since this one was the only one I could find I'll go with it. I really hope I didn't fuck up a second time.

This graph really makes it hard to do a decay process.......there's no values to match up at the end of the multiple dose excretion curve with the single dose excretion curve which has been my theme all along. This one is going to take a little bit more care, actually a lot more care.

But your analysis makes sense; If administration frequency is short compared to decay rate then it should accumulate A LOT. It does now unlike my other graph.
 
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@kflo I think I fucked up with my for loop, I combed back through my accurately reproduced Cowan model script, made appropriate adjustments to my current script and I got a peak of 2,977 pg/ml at 30 days for a multiple dose. I think this loading period graph is correct now.

aRgL1xL.jpg


If Cowan's model is applicable and I am really inclined to believe it is because it was applied in Dylan Scott's case then "trace amounts" is really a legitimate thing. Well to be fair one of Cowan's models was applied but since this one was the only one I could find I'll go with it. I really hope I didn't fuck up a second time.

This graph really makes it hard to do a decay process.......there's no values to match up at the end of the multiple dose excretion curve with the single dose excretion curve which has been my theme all along. This one is going to take a little bit more care, actually a lot more care.

But your analysis makes sense; If administration frequency is short compared to decay rate then it should accumulate A LOT. It does now unlike my other graph.
keep at it bro. fall forward....
 
keep at it bro. fall forward....

I’m trying to man haha. I have an idea about how to proceed but it’s gonna require a lot of complex book keeping. It might not be effective but it might be.

After this I want to run multiple kinetic order regressions on all the clomiphene subjects and choose the best for each. I’m going to have to create a pipeline/grid search to automate it which will require some ingenuity imo.

If I appear to be successful I am going to do a technical write-up that clearly explains the approach. I wouldn’t be surprised if it was 100 pages after all is said and done.

Honestly I should probably be doing this in Python/Jupyter (instead of matlab) notebooks so I can have it publicly accessible on GitHub. Jupyter is the shit, it allows you to explain your approach with Latex/Markdown and run individual blocks of code without having to keep track of a million variable names.
 
I’m trying to man haha. I have an idea about how to proceed but it’s gonna require a lot of complex book keeping. It might not be effective but it might be.

After this I want to run multiple kinetic order regressions on all the clomiphene subjects and choose the best for each. I’m going to have to create a pipeline/grid search to automate it which will require some ingenuity imo.

If I appear to be successful I am going to do a technical write-up that clearly explains the approach. I wouldn’t be surprised if it was 100 pages after all is said and done.

Honestly I should probably be doing this in Python/Jupyter (instead of matlab) notebooks so I can have it publicly accessible on GitHub. Jupyter is the shit, it allows you to explain your approach with Latex/Markdown and run individual blocks of code without having to keep track of a million variable names.
i would personally caution over too much complex analysis. your binding issue is still the limitations of your data. you will NOT end up with solid conclusions. just more tests to perform.

i would suggest you try and define what questions you're trying to answer. again, you're running regressions to approximate data you will never know.

again, it's not clear to me what you expect to learn from your clomiphene regression. i'm interested but it's just not clear without hearing your intent.

in the end you will need further studies. there's no getting around that. you don't have a long term study with sufficient data points or points beyond a certain date. again, same for the dhcmt data.
 
i would personally caution over too much complex analysis. your binding issue is still the limitations of your data. you will NOT end up with solid conclusions. just more tests to perform.

i would suggest you try and define what questions you're trying to answer. again, you're running regressions to approximate data you will never know.

again, it's not clear to me what you expect to learn from your clomiphene regression. i'm interested but it's just not clear without hearing your intent.

in the end you will need further studies. there's no getting around that. you don't have a long term study with sufficient data points or points beyond a certain date. again, same for the dhcmt data.

I’m studying clomiphene to assess inter-individual variability and for shits and giggles apply it to tbol.

Normally regression would be dangerous given the unknown data in between but I think a semi-theoretically informed model is useful (third order differential equation). Even without that there are ways to control bias and variance.

But yes, more data is always better and if I can slap together something respectable I may be able to convince the authors of the paper to give me the data.

I wasn’t talking about complexity in terms of kinetic order or spline polynomials and whatnot. I was talking about the decaying part and the logic loops and statements associated with it. “Decaying as it would have after accumulation as if there was no accumulation” has been my approach thus far (I think it’s sensible) but it lacks meaning if the final accumulation point is 100x higher than any point in the single dose data set. We have to carefully go backwards while moving forwards. It’s hard to explain without a picture. (Which would take a lot of time to Latex up but it would be in my write-up)
 
I’m studying clomiphene to assess inter-individual variability and for shits and giggles apply it to tbol.

Normally regression would be dangerous given the unknown data in between but I think a semi-theoretically informed model is useful (third order differential equation). Even without that there are ways to control bias and variance.

But yes, more data is always better and if I can slap together something respectable I may be able to convince the authors of the paper to give me the data.

I wasn’t talking about complexity in terms of kinetic order or spline polynomials and whatnot. I was talking about the decaying part and the logic loops and statements associated with it. “Decaying as it would have after accumulation as if there was no accumulation” has been my approach thus far (I think it’s sensible) but it lacks meaning if the final accumulation point is 100x higher than any point in the single dose data set. We have to carefully go backwards while moving forwards. It’s hard to explain without a picture. (Which would take a lot of time to Latex up but it would be in my write-up)
what data are you asking the authors for?

again, the issue is the data that hasn't been collected.......

you don't have complete data sets. all of them are incomplete.
 
what data are you asking the authors for?

again, the issue is the data that hasn't been collected.......

you don't have complete data sets. all of them are incomplete.

The time series for the excretion data for each subject for each metabolite.

I’m not so much trying to draw far reaching conclusions anymore but rather develop the tools so that when enough data comes along it can be worked with. (And even tested against theory)

I know it sounds arrogant to say “look at me I’ve possibly figured out something WADA hasn’t” but I did figure out Cowan’s model and I did fit a 3rd order differential equation to the data (something I haven’t seen anyone do). If someone had decided to model excretion curve with second order kinetics I’m sure the paper would have mentioned it instead of just saying “first order doesn’t work”.
 
@kflo you know how yesterday I was trying to generalize the notion of "decaying as it would have otherwise at the same quantity had it not accumulated in the first place". In my judgement that approach could easily fall apart in certain circumstances; if you attach a pure exponential (a prototypical example) to a higher point it probably won't go to zero in the long run as it should because the asymptotic behavior will merely be shifted up.

I think I have come up with a general procedure.

Instead of "decaying as it would have otherwise at the same quantity had it not accumulated in the first place" I propose that it would "decay is it would have otherwise at the same quantity had we stopped accumulating at the previous dose......this is done iteratively". There's some in between steps to make sure everything is "lined up" but that's the long and short of it.

The idea is not solidified/tested. All I know is that it will be a bitch to program.

Here's some shitty pictures to illustrate. I hope the behavior of the actual program lines up with what I think should intuitively happen in two typical cases.

First the trivial familiar one.

IVA59q1.jpg



Now the actual one I'm trying to deal with.

Ha9rLDP.jpg


I really hope this idea works. At least with this idea there is a vague feeling of consistency/symmetry between how something accumulates from multiple doses and how it decays; It accumulates iteratively from previous doses and it decays iteratively from previous decays.

Hopefully my intuition works.
 
@kflo you know how yesterday I was trying to generalize the notion of "decaying as it would have otherwise at the same quantity had it not accumulated in the first place". In my judgement that approach could easily fall apart in certain circumstances; if you attach a pure exponential (a prototypical example) to a higher point it probably won't go to zero in the long run as it should because the asymptotic behavior will merely be shifted up.

I think I have come up with a general procedure.

Instead of "decaying as it would have otherwise at the same quantity had it not accumulated in the first place" I propose that it would "decay is it would have otherwise at the same quantity had we stopped accumulating at the previous dose......this is done iteratively". There's some in between steps to make sure everything is "lined up" but that's the long and short of it.

The idea is not solidified/tested. All I know is that it will be a bitch to program.

Here's some shitty pictures to illustrate. I hope the behavior of the actual program lines up with what I think should intuitively happen in two typical cases.

First the trivial familiar one.

IVA59q1.jpg



Now the actual one I'm trying to deal with.

Ha9rLDP.jpg


I really hope this idea works. At least with this idea there is a vague feeling of consistency/symmetry between how something accumulates from multiple doses and how it decays; It accumulates iteratively from previous doses and it decays iteratively from previous decays.

Hopefully my intuition works.
how do you evaluate whether it works or not?
 
how do you evaluate whether it works or not?

At the very least I'm looking for long term asymptotic behavior towards zero. I think if I can get that sort of long term behavior while obeying "symmetry" between Cowan's proven accumulation scheme and my decay proposition that it would at least inspire some degree of confidence.

I guess another litmus test I can run is as follows

1) Integrate a single dose excretion curve and divide by 20mg

2) Integrate the resulting multiple dose excretion curve and divide by 600mg (for 30 doses)

3) If the proportions of biological recovery are similar then that would inspire some more confidence

I mean there's no way I can know for sure but there are still certain check marks that must be checked off before a model can even be considered a viable candidate. I guess at this point I'm merely developing a theory from imposing symmetry between "A" (accumulation model that is accepted) and "B" (decay model that I'm proposing).

Again I can't know for sure without testing against actual sufficient data but if "decaying iteratively in a similar fashion as it accumulates" passes certain litmus tests I would get a warm fuzzy feeling.

Forgive me for using "symmetry" as a buzz word; I know those who are trained in mathematics cringe when someone does that.
 
being a dad, training a lot, working a bit. Boring/fun stuff.
How you been mang?
congrats on being a dad. it's the best.

im being a dad to kids in middle / high school, lifting alot, working some and spending alot of time home. all good. cheers brah.
 
@kflo you gotta admit. Even though we disagree a lot throughout this thread you gotta give me props for

1) recreating Cowan's model (for the accumulation segment)

and

2) Fitting a third order solution to a differential equation to the data (after all previous orders were said not to work so I took the next step)

I know you give me props, I just wanted to pat myself on the back again :)

Even Cowan said he had trouble constructing the model (solely for the accumulation part) which I was able to do with relative ease.

BTW I didn't copy a script from stack exchange for exponential fitting. I ready the theory and created my own.
@Parmenides look what the heavies had turned into...lol
 
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