The information listed under Results is particularly interesting.
2017 May 15
Meaningful endpoints for therapies approved for hematologic malignancies.
Smith BD1, DeZern AE1, Bastian AW2, Durie BGM3.
Abstract
BACKGROUND:
Overall survival (OS) is considered the gold standard for determining treatment efficacy in oncology trials, but the relation between treatment and OS can be challenging to assess because of long study durations and the impact of subsequent therapies on outcome. Using OS can be particularly difficult for new therapies in hematologic malignancies (HMs).
METHODS:
This retrospective analysis was conducted to characterize the primary endpoints used to support US Food and Drug Administration (FDA) approvals for new drug or novel HM indications between January 2002 and July 2015. Data on approvals were retrieved from the FDA and CenterWatch websites, and from the FDA prescribing information on respective products at the time of approval.
RESULTS:
Sixty-three FDA approvals involving 35 drugs and 16 HMs were identified. Of the 63 approvals, 45 (71.4%) included response rate (RR), and 17 (27%) included progression-free survival (PFS; n = 14) or time to progression (n = 3), and 1 approval included OS. Twenty-three approvals (36.5%) included trials with an active comparator arm. The median relative magnitude of benefit versus comparator was 71% improvement (range, 26%-127%), with a median hazard ratio of 0.55 (range, 0.16-0.72).
CONCLUSIONS:
FDA approvals for new drug or novel HM indications are often based on endpoints other than OS, such as RR and PFS. Tools for determining the magnitude of clinical benefit and treatment value in HMs should take into account the importance of RR, PFS, and other non-OS endpoints. Cancer 2017;123:1689-1694. © 2017 American Cancer Society.
https://www.ncbi.nlm.nih.gov/pubmed/28222220
Using OS can be particularly difficult for new therapies in hematologic malignancies
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Re: Using OS can be particularly difficult for new therapies in hematologic malignancies
So assuming the results of this study apply to Imet are we once again attempting to use the wrong primary endpoint? Could all of this possibly be more confusing to the average layman/investor?
Re: Using OS can be particularly difficult for new therapies in hematologic malignancies
OS benefit requires large numbers of patients and can take several years.
“…Problems remain with measuring OS (31). First, measuring an OS benefit requires large numbers of patients and can take several years, delaying access to new drugs for very sick patients who lack effective options. Second, clinical trials often permit control-arm patients to cross over to the investigational agent after disease progression, confounding analysis of the impact of the investigational agent on survival. Third, as improved therapies become the standard of care, showing a survival benefit compared with these therapies becomes increasingly difficult. Our research shows that the FDA understands these limitations and is willing to accept notable improvements in intermediate endpoints in place of a demonstrated OS benefit.”
http://clincancerres.aacrjournals.org/c ... 19/14/3722
“…Problems remain with measuring OS (31). First, measuring an OS benefit requires large numbers of patients and can take several years, delaying access to new drugs for very sick patients who lack effective options. Second, clinical trials often permit control-arm patients to cross over to the investigational agent after disease progression, confounding analysis of the impact of the investigational agent on survival. Third, as improved therapies become the standard of care, showing a survival benefit compared with these therapies becomes increasingly difficult. Our research shows that the FDA understands these limitations and is willing to accept notable improvements in intermediate endpoints in place of a demonstrated OS benefit.”
http://clincancerres.aacrjournals.org/c ... 19/14/3722
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Fishermangents
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Re: Using OS can be particularly difficult for new therapies in hematologic malignancies
All good points, important to be aware of. In the case of IMbark and the high risk status fo these patients, OS may not be the right endpoint. But median OS might, as that takes into account that at a certain moment in time certain patients may not have died yet, whereas median OS could have been reached. It is a kind of short cut to get some solid idea of the efficacy of drug, comparing to others (e.g. SoC or BAT) or to no treatment at all.
This article explains it an a comprehensible way:
====
How to Calculate Median Survival Time
By Peter Flom; Updated April 24, 2017
Survival time is a term used by statisticians for any kind of time-to-event data, not just survival. For example, it could be time-to-graduation for students or time-to-divorce for married couples. The key thing about variables like this is that they're censored; in other words, you usually don't have complete information. By far the most common type of censoring is "right-censoring." This occurs when the event in question doesn't happen to all the subjects in your sample. For example, if you're tracking students, not all will graduate before your study ends. You won't be able to tell if or when they'll graduate.
List the survival time of all the subjects in your sample. For example, if you have five students (in a real study, you'd have more) and their times to graduation were 3 years, 4 years (so far), 4.5 years, 3.5 years and 7 years (so far), write down the times: 3, 4, 4.5, 3.5, 7.
Put a plus sign (or other mark) next to any times that are right-censored (that is, those that have not had the event happen yet). Your list would look like this: 3, 4+, 4.5, 3.5, 7+.
Determine if more than half the data is censored. To do this, divide the number of subjects with plus signs (censored data) by the total number of subjects. If this is more than 0.5, the median doesn't exist. In the example, 2 subjects out of 5 have censored data. That's less than half, so the median exists.
Sort the survival times from shortest to longest. Using the example, they'd be sorted like this: 3, 3.5, 4, 4.5, 7.
Divide the number of subjects by 2, and round down. In the example 5 ÷ 2 = 2.5 and rounding down gives 2.
Find the first-ordered survival time that is greater than this number. This is the median survival time. In the example, 4 is the first number that is greater than two other numbers; this is the median survival time.
https://sciencing.com/calculate-median- ... 32926.html
====
Median Survival Time is equivalent to Median Overall Survival. The above calculation shows that Median OS can handle the situation that patients are still alive, while not knowing how long they still may live. So it may be that Median OS is a more practical endpoint in this case then OS.
This article explains it an a comprehensible way:
====
How to Calculate Median Survival Time
By Peter Flom; Updated April 24, 2017
Survival time is a term used by statisticians for any kind of time-to-event data, not just survival. For example, it could be time-to-graduation for students or time-to-divorce for married couples. The key thing about variables like this is that they're censored; in other words, you usually don't have complete information. By far the most common type of censoring is "right-censoring." This occurs when the event in question doesn't happen to all the subjects in your sample. For example, if you're tracking students, not all will graduate before your study ends. You won't be able to tell if or when they'll graduate.
List the survival time of all the subjects in your sample. For example, if you have five students (in a real study, you'd have more) and their times to graduation were 3 years, 4 years (so far), 4.5 years, 3.5 years and 7 years (so far), write down the times: 3, 4, 4.5, 3.5, 7.
Put a plus sign (or other mark) next to any times that are right-censored (that is, those that have not had the event happen yet). Your list would look like this: 3, 4+, 4.5, 3.5, 7+.
Determine if more than half the data is censored. To do this, divide the number of subjects with plus signs (censored data) by the total number of subjects. If this is more than 0.5, the median doesn't exist. In the example, 2 subjects out of 5 have censored data. That's less than half, so the median exists.
Sort the survival times from shortest to longest. Using the example, they'd be sorted like this: 3, 3.5, 4, 4.5, 7.
Divide the number of subjects by 2, and round down. In the example 5 ÷ 2 = 2.5 and rounding down gives 2.
Find the first-ordered survival time that is greater than this number. This is the median survival time. In the example, 4 is the first number that is greater than two other numbers; this is the median survival time.
https://sciencing.com/calculate-median- ... 32926.html
====
Median Survival Time is equivalent to Median Overall Survival. The above calculation shows that Median OS can handle the situation that patients are still alive, while not knowing how long they still may live. So it may be that Median OS is a more practical endpoint in this case then OS.
Re: Using OS can be particularly difficult for new therapies in hematologic malignancies
Of interest:
Background: In 2015, The FDA approved the most number of novel drugs in the last 19-years. We propose to evaluate the factors associated with the significant increase in FDA approved oncologic drugs over the last 5 years.
Results: The FDA approved 47 novel cancer drugs from 2011 to 2015, of which, 34 (72.3%) were new drug applications and 13 (27.7%) were biologic license applications. Of these, 35 (64.5%) were orphan and 19 (40.4%) were first-in-class drugs. Based on expedited approval, 36 (76.6%) underwent priority review, 27 (57.5%) fast track, 17 (36.2%) accelerated, and 11 (23.4%) had breakthrough designation. 31 (66%) were for solid and 16 (34%) for liquid cancers. 20 (42.6%) drugs were approved based on clinical response, 16 (34.0%) on progression-free survival and 11 (23.4%) on overall survival as primary endpoints. The median time from initiation of clinical trial to approval was 42 months (range: 17 to 78 months). Trials completed within a shorter time ( < 42 months, median) were more likely to receive fast track designation compared to those requiring a longer time (36.2% vs. 21.3%; p = 0.03).
Conclusions: The majority (65%) of new cancer drugs were approved under the orphan drug designation which has increased over the past 5 years. The median time from clinical trial initiation to FDA approval was 42 months. The increasing trend for novel drug approvals through these expedited programs based on shorter and smaller clinical trials warrants further investigation.
http://ascopubs.org/doi/abs/10.1200/JCO ... ppl.e14111
Background: In 2015, The FDA approved the most number of novel drugs in the last 19-years. We propose to evaluate the factors associated with the significant increase in FDA approved oncologic drugs over the last 5 years.
Results: The FDA approved 47 novel cancer drugs from 2011 to 2015, of which, 34 (72.3%) were new drug applications and 13 (27.7%) were biologic license applications. Of these, 35 (64.5%) were orphan and 19 (40.4%) were first-in-class drugs. Based on expedited approval, 36 (76.6%) underwent priority review, 27 (57.5%) fast track, 17 (36.2%) accelerated, and 11 (23.4%) had breakthrough designation. 31 (66%) were for solid and 16 (34%) for liquid cancers. 20 (42.6%) drugs were approved based on clinical response, 16 (34.0%) on progression-free survival and 11 (23.4%) on overall survival as primary endpoints. The median time from initiation of clinical trial to approval was 42 months (range: 17 to 78 months). Trials completed within a shorter time ( < 42 months, median) were more likely to receive fast track designation compared to those requiring a longer time (36.2% vs. 21.3%; p = 0.03).
Conclusions: The majority (65%) of new cancer drugs were approved under the orphan drug designation which has increased over the past 5 years. The median time from clinical trial initiation to FDA approval was 42 months. The increasing trend for novel drug approvals through these expedited programs based on shorter and smaller clinical trials warrants further investigation.
http://ascopubs.org/doi/abs/10.1200/JCO ... ppl.e14111