Anavex Life Sciences Corp (AVXL)

[Investor2014]

- Yes U.K. and Australian approved an endpoint change not the FDA. There were no AVATAR trial sites in the U.S. so no need to ask the FDA.
- The endpoint change would almost certainly have been only the Anchoring of RSBQ to CGI-I, NOT AUC!
- AUC makes no sense in a trial with just two time samples of data as is the case in all 3 Anavex Rett trials with baseline, midpoint and EOT only. Hence, Jin Kun or others have made sure that Missling does not again seeks to 'rescue' a readout using AUC!
- That the RSBQ Primary and CGI-I Secondary outcome measures were exactly identical is possible with a very low probability. The two scales and structure is different with RSBQ answered by parent/care-giver and CGI-I by a trained physician. If RSBQ and the Pro CGI-I scores were often or even occasionally identical or very close what would be the point of Anchoring RSBQ to CGI-I? I do not buy this without proof that these two scores happened to be identical!

Anavex in their AVATAR readout slide 8, referred to this FDA Guidance: Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims

This guidance document contains no mention of Area Under the Curve (AUC), but provides guidance on Anchoring. In the case of AVATAR this means Anchoring RSBQ to the PRO instrument CGI-I physician subjective scoring of Rett symptoms.

Anavex have offered no references to any guidance for the use of AUC. Imo that was there own invention that rightly caused a lot market consternation as it simply looks like a last minute oh schitt! - the trial outcome was not stat sig. It did not help that Missling refused to release the baseline to EOT results! It did not help either that his explanation for not using AUC in the EXCELLENCE trial was that it has a large n and therefore does not need AUC - complete BS!

The empiric evidence for any responder definition is derived using anchor-based methods. Anchor-based methods explore the associations between the targeted concept of the PRO instrument and the concept measured by the anchors. To be useful, the anchors chosen should be easier to interpret than the PRO measure itself. For example, the number of incontinence episodes collected in incontinence diaries has been used to determine a responder definition for PRO instruments assessing the annoyance of incontinence. A 50 percent reduction in incontinence episodes might be proposed as the anchor for defining a responder on the PRO instrument. Confirmation of this anchor approach in early clinical trials can provide the basis for the proposed responder definition in the confirmatory trials.

Another anchor-based approach to defining responders makes use of patient ratings of change administered at different periods of time or upon exit from a clinical trial. These numerical ratings range from worse to the same and better. The difference in the PRO score for persons who rate their condition the same and better or worse can be used to define responders to treatment. Patient ratings of change are less useful as anchors when patients are not blinded to treatment assignment.

Another set of approaches to defining a responder are distribution-based methods that use, for example, the between-person standard deviation or the standard error of measurement to define a meaningful change on a scale. Distribution-based methods can be used to categorize these changes as small, moderate, and large and often can be combined with anchor-based estimates to provide confidence in the responder definition. Distribution-based methods for determining clinical significance of particular score changes should be considered as supportive and are not appropriate as the sole basis for determining a responder definition.

Alternatively, it is possible to present the entire distribution of responses for treatment and control group, avoiding the need to pick a responder criterion. Whether the individual responses are meaningful represents a judgment, but that problem is present with almost all endpoints except survival. Such cumulative distribution displays show a continuous plot of the percent change from baseline on the X-axis and the percent of patients experiencing that change on the Y-axis. This display type may be preferable to attempting to provide categorical definitions of responders. A variety of responder definitions can be identified along the cumulative distribution of response curve.

Guidance on interpretation considerations for a clinical trial’s SAP is found in section V.E., Interpretation of Clinical Trial Results.

I would love to see the SAP for AVATAR, EXCELLENCE and the P2b/3 AD trial. Imo this should be public information and issued certainly with trial readout as it would answer many of the questions some of us here have!