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Post by goyocafe on Sept 11, 2017 16:55:45 GMT -5
Thought this would be a way to pass 15 more seconds of our wait time...  |
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Post by dreamboatcruise on Sept 11, 2017 17:37:40 GMT -5
Before is what the Kat (vegan product) entrails look like.
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Post by zzhoskins on Sept 11, 2017 17:59:07 GMT -5
Has MNKD ever published what exactly they wanted to change on the label?
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Post by peppy on Sept 11, 2017 18:30:18 GMT -5
Has MNKD ever published what exactly they wanted to change on the label? Michael Castagna
Sure thank you Jason. First the label change has a couple aspects to it and I think about it in three steps one there is the ability to articulate that the drug starts working within five minutes. The second part is we can articulate that it is faster than the competition. And the third is asking for a different category altogether. The important of these commercially really vary in terms of impact now in the second half of 2017 but as we go in 2018 and beyond.
As repute to communicate we expect PDUFA approval by the end of Q3. So this will be a Q4 event in terms of launch and impact. Depending on which combination of those three activities happen really will depend on the upside in terms of the forecast and the accelerate growth that we expect post the label change.
seekingalpha.com/article/4096149-mannkinds-mnkd-ceo-michael-castagna-q2-2017-results-earnings-call-transcript?part=single
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Post by mnkdfann on Sept 11, 2017 18:36:15 GMT -5
I'm expecting a split decision on the label. MNKD gets some, but not all, of what it wants.
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Post by zzhoskins on Sept 11, 2017 18:49:12 GMT -5
Has MNKD ever published what exactly they wanted to change on the label? Michael Castagna
Sure thank you Jason. First the label change has a couple aspects to it and I think about it in three steps one there is the ability to articulate that the drug starts working within five minutes. The second part is we can articulate that it is faster than the competition. And the third is asking for a different category altogether. The important of these commercially really vary in terms of impact now in the second half of 2017 but as we go in 2018 and beyond.
As repute to communicate we expect PDUFA approval by the end of Q3. So this will be a Q4 event in terms of launch and impact. Depending on which combination of those three activities happen really will depend on the upside in terms of the forecast and the accelerate growth that we expect post the label change.
seekingalpha.com/article/4096149-mannkinds-mnkd-ceo-michael-castagna-q2-2017-results-earnings-call-transcript?part=single
Thanks, but what I meant was what exactly is the wording that they want. Technically, any drug starts working the second it enters your system, although meaningful effects may vary. And MNKD appears to have already shown that afrezza is faster but the difference wasn't statistically signifcant. I presume this is the new data they used:
www.mannkindcorp.com/assets/Baughman-2016-TI-displays-earlier-onset-and-shorter-duration-than-insulin-lispro-ADA-100-LB.pdf
I don't see any statistics. I would think they'd be required to prove statistically signifcant differences.
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Post by peppy on Sept 11, 2017 18:52:45 GMT -5
Michael Castagna
Sure thank you Jason. First the label change has a couple aspects to it and I think about it in three steps one there is the ability to articulate that the drug starts working within five minutes. The second part is we can articulate that it is faster than the competition. And the third is asking for a different category altogether. The important of these commercially really vary in terms of impact now in the second half of 2017 but as we go in 2018 and beyond.
As repute to communicate we expect PDUFA approval by the end of Q3. So this will be a Q4 event in terms of launch and impact. Depending on which combination of those three activities happen really will depend on the upside in terms of the forecast and the accelerate growth that we expect post the label change.
seekingalpha.com/article/4096149-mannkinds-mnkd-ceo-michael-castagna-q2-2017-results-earnings-call-transcript?part=single
Thanks, but what I meant was what exactly is the wording that they want. Technically, any drug starts working the second it enters your system, although meaningful effects may vary. And MNKD appears to have already shown that afrezza is faster but the difference wasn't statistically signifcant. I presume this is the new data they used:
www.mannkindcorp.com/assets/Baughman-2016-TI-displays-earlier-onset-and-shorter-duration-than-insulin-lispro-ADA-100-LB.pdf
I don't see any statistics. I would think they'd be required to prove statistically signifcant differences.
Cmax and AUC were dose proportional for TI but slightly sublinear for
Lispro; saturable GIRmax was obtained over the dose range for both
insulins. Onset of activity for TI occurred ca. 25-35 minutes earlier than for
Lispro. TI duration of action is about 2 hours shorter than an equivalent
dose of Lispro. Dose-response was almost linear up to 48U TI and 30 U Lispro. www.mannkindcorp.com/Collateral/Documents/English-US/Baughman%20poster%20100-LB%20FINAL%20X2.pdf
you have to read the stuff yourself now ZZ.
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Post by zzhoskins on Sept 11, 2017 18:59:01 GMT -5
Cmax and AUC were dose proportional for TI but slightly sublinear for
Lispro; saturable GIRmax was obtained over the dose range for both
insulins. Onset of activity for TI occurred ca. 25-35 minutes earlier than for
Lispro. TI duration of action is about 2 hours shorter than an equivalent
dose of Lispro. Dose-response was almost linear up to 48U TI and 30 U Lispro. www.mannkindcorp.com/Collateral/Documents/English-US/Baughman%20poster%20100-LB%20FINAL%20X2.pdf
you have to read the stuff yourself now ZZ.
Yes, but there's no p-value to support the argument that the effects are statistically significant. The FDA and insurers usually check to see whether claims are supported by tests to show that a difference has a p<0.05. |
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Post by akemp3000 on Sept 12, 2017 16:29:56 GMT -5
Considering that today's language is basically that Afrezza is "non-inferior", almost any label change would be significant.
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Post by peppy on Sept 12, 2017 16:58:23 GMT -5
Cmax and AUC were dose proportional for TI but slightly sublinear for
Lispro; saturable GIRmax was obtained over the dose range for both
insulins. Onset of activity for TI occurred ca. 25-35 minutes earlier than for
Lispro. TI duration of action is about 2 hours shorter than an equivalent
dose of Lispro. Dose-response was almost linear up to 48U TI and 30 U Lispro. www.mannkindcorp.com/Collateral/Documents/English-US/Baughman%20poster%20100-LB%20FINAL%20X2.pdf
you have to read the stuff yourself now ZZ.
Yes, but there's no p-value to support the argument that the effects are statistically significant. The FDA and insurers usually check to see whether claims are supported by tests to show that a difference has a p<0.05. I do not know what p value is.
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Post by dreamboatcruise on Sept 12, 2017 17:30:41 GMT -5
Yes, but there's no p-value to support the argument that the effects are statistically significant. The FDA and insurers usually check to see whether claims are supported by tests to show that a difference has a p<0.05. I do not know what p value is.
It's basically the probability that the result being claimed is actually false, so the lower the better. p < 0.05 would mean at least 95% probability that the stated outcome reflects reality and not just a statistically anomaly. If you rolled a die twice and both times it showed 1, you could statistically say that the average based on your sampling was 1... but the p value would be very high because you lack enough samples to have statistical rigor and in fact we know 1 isn't the real average when rolling a die. |
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Post by contrastock on Sept 13, 2017 0:50:36 GMT -5
I do not know what p value is.
It's basically the probability that the result being claimed is actually false, so the lower the better. p < 0.05 would mean at least 95% probability that the stated outcome reflects reality and not just a statistically anomaly. If you rolled a die twice and both times it showed 1, you could statistically say that the average based on your sampling was 1... but the p value would be very high because you lack enough samples to have statistical rigor and in fact we know 1 isn't the real average when rolling a die. Without having all the data and just this table I can run a rough estimate. It has to make a few assumptions like a normal distribution and that they have the same variance (which they definitely don't). I made the assumptions on the conservative side. z = (1.82 - 1.23)/(1.114/(29 .5)) = 2.8521 2.8521 -> .9978 1 - .9978 = .0022 is our p value I am guessing that they did not include a p-value since the data can pretty easily be manipulated to spit out something that makes it statistically significant. A few outliers on either could pretty easily skew the results. FDA would take these outliers into consideration and see if removing them also changes if it is statistically significant. They might not have included it since we don't know what the FDA is actually looking for. A pill might make a broken arm be healed faster. It could go from taking 504 hours to heal to 503 hours to heal. If we had a large enough sample (and/or the variance was small enough), we could say that the pill does have statistically significant impact on healing a broken arm. But is one hour an amount that the FDA is looking for? Another scenario, you measure the number of purses owned by 100 random people. You then also count the number of hairs on their chins. You find that the less purses you own, the more hair you have on your chin. Owning purses is statistically significant.... well not exactly. Men are on average going to own less purses than women and men are going to have more facial hair (hopefully). Owning purses doesn't cause less facial hair to be grown. The point I'm trying to make is that I wouldn't worry that a p-value isn't included (oh and I think the results look pretty good  ). Any company could manipulate their data to generate a p-value that is statistically significant. |
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Post by dreamboatcruise on Sept 13, 2017 1:00:50 GMT -5
contrastock ... like your example of correlation vs causation. That's one reason retrospective population studies are not considered in nearly the same way as a well designed double blind for medicine. I'd bet my best purse on it.
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Post by derek2 on Sept 13, 2017 9:00:18 GMT -5
Statistical Evidence:The average human life expectancy at birth is 78 years with a standard deviation of 15 years. 67% of people will attain an age of 63 - 91 years 95% of people will attain an age of 48 - 106 years Based on 20,000,000 birth / death statistics from 102 countries. +- 0.15 years 95% of the time. 
Anecdotal Evidence:I woke up today, so I'm going to live forever. So far, so good.
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Post by peppy on Sept 13, 2017 9:28:26 GMT -5
Statistical Evidence:The average human life expectancy at birth is 78 years with a standard deviation of 15 years. 67% of people will attain an age of 63 - 91 years 95% of people will attain an age of 48 - 106 years Based on 20,000,000 birth / death statistics from 102 countries. +- 0.15 years 95% of the time.
Anecdotal Evidence:I woke up today, so I'm going to live forever. So far, so good. Live Forever - Shaver www.youtube.com/watch?v=2r57rzLYqSs
Good thing we are eternal spiritual beings, we will be back, unless we escape.  This life has been a pretty good one, air conditioners and all.
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). Any company could manipulate their data to generate a p-value that is statistically significant. 
