Ignoranus-dumbutt, AF didn't orchestrate a PUMP & DUMP, he exposed the insider PUMPU & DUMP. Whistleblowers should be honored and they are protected by statute.
Ahn and the insiders were caught red handed orchestrating a Pump & Dump.
It should be an easy call.
There was no interest from Big Pharma.
Blockbuster drugs aren't acquired for $5M, just cash burning failures.
"Applying a statistical test of significance (hypothesis testing) to the same data the pattern was learned from is wrong. One way to construct hypotheses while avoiding data dredging is to conduct randomized out-of-sample tests. The researcher collects a data set, then randomly partitions it into two subsets, A and B. Only one subset—say, subset A—is examined for creating hypotheses. Once a hypothesis is formulated, it must be tested on subset B, which was not used to construct the hypothesis. Only where B also supports such a hypothesis is it reasonable to believe the hypothesis might be valid."
"Here is a simple example. Throwing a coin five times, with a result of 2 heads and 3 tails, might lead one to hypothesize that the coin favors tails by 3/5 to 2/5. If this hypothesis is then tested on the existing data set, it is confirmed, but the confirmation is meaningless. The proper procedure would have been to form in advance a hypothesis of what the tails probability is, and then throw the coin various times to see if the hypothesis is rejected or not. If three tails and two heads are observed, another hypothesis, that the tails probability is 3/5, could be formed, but it could only be tested by a new set of coin tosses. It is important to realize that the statistical significance under the incorrect procedure is completely spurious – significance tests do not protect against data dredging."
"The FDA employs an average-patient standard when reviewing drugs: it
approves a drug only if is safe and effective for the average patient in a clinical trial. It is
common, however, for patients to respond differently to a drug. Therefore, the average-patient
standard can reject a drug that benefits certain patient subgroups (false negative) and even
approval a drug that harms other patient subgroups (false positives). These errors increase the
cost of drug development – and thus health care – by wasting research on unproductive or
unapproved drugs. The reason why the FDA sticks with an average patient standard is concern
about opportunism by drug companies. With enough data dredging, a drug company can always
find some subgroup of patients that appears to benefit from its drug, even if it truly does not."
"the size of patient populations in earlier trials are much smaller, which can cause safety signals to be missed and efficacy signals harder to replicate in larger samples."
"Achieving success in the development of a cancer drug continues to be challenging. Given the increasing costs (1) and the small number of drugs that gain regulatory approval (2), it is crucial to understand these failures. In this issue of the Journal, Gan et al. (3) reviewed 235 recently published phase III randomized clinical trials (RCTs). They report that 62% of the trials did not achieve results with statistical significance. Trying to explain the high failure rate, they note the actual magnitude of benefit achieved in a clinical trial (designated B) is nearly always less than what was predicted at the time the trial was designed (designated δ) and conclude, “investigators consistently make overly-optimistic assumptions regarding treatment benefits when designing RCTs.”