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A Novel Approach to Developing Comprehensive Profile of Adverse Event (AE) Data from Clinical Trials

A Novel Approach to Developing Comprehensive Profile of Adverse Event (AE) Data from Clinical Trials

Sharayu Paranjpe

Cytel Statistical Software and Services Pvt. Ltd

The funding will be provided by Cytel Statistical Software & Services Pvt. Ltd. ?“Cytel”?, an Indian company with whom I am a consultant on contract. The team members 2 to 7 are full time employees of Cytel.

I have requested for data on four trials. The sponsor companies are Eli Lilly and Company, Hoffmann-La Roche, Boehringer Ingelheim and Glaxo-Smith-Kline. There is no conflict of interest. But as a matter of disclosure, I would like to state that all four sponsor companies are customers of Cytel's software products (East and StatExact) The sponsor company Hoffmann-La Roche is an existing client of Cytel for statistical and programming services. That relationship will not influence the conduct of the project or the outcome that we will document. None of the team members (including me) are part of the team working for Roche and we do not have direct/ indirect access to client data.

20 September 2017

Summary and analysis of AE data is crucial for judging suitability of a drug and for comparison between drugs. AE data from clinical trials is typically reported mostly as counts and percentages. In line with recommendation from ICH3 Guideline, only a few events of special interest are further analyzed and used for comparison of treatments. Clearly this is incomplete use of AE information. This happens because of the complex nature of data. First of all, variety of AEs is immense. Some are minor while others are severe. Some are clearly drug related while others are not. Some are instantaneous while others linger for some duration. Secondly the Same AE may occur in multiple subjects and a single subject may experience multiple AEs. There may be repeat episodes of same AE to same patient. There may be associations between occurrence/non-occurrence of two or more AEs. Total number of AEs experienced by group of patients in a trial is generally very large. All these issues make AE data complicated to comprehend as a whole. It would be very useful to devise statistical methods to extract more information from the complex AE data. The problem could use some 'out of the box' thinking. One way of getting hold of such ideas is to borrow from a different domain of application of statistics. Towards this my collaboration with ecologists during my tenure as University teacher has an advantage. I believe that some statistical methods widely used in analysis of ecological data can be fruitful in analysis of AE. The biodiversity measurement issue in ecology has complexity level of data comparable to AE data. My idea is to apply some of these methods to AE data to build a comprehensive picture.
Objectives: The main objective is to treat all AEs as a single entity and explore patterns in AE occurrence as a whole instead of one AE type in isolation. The emergent AE profile will help in comparison between drugs. I believe that with this approach some new features of the AE profile will emerge. Some of the indices available in ecological literature for measurement of species diversity can be directly used for AE data. Some modifications, if needed will be attempted. The interpretation of these indices and possible comparison of treatments will also be part of the objective. AE accumulation curve will be generated. A suitable model will be fitted. This will help estimating ‘Possible number of maximum AEs we may observe' and what fraction we have recorded thus far.
Key question is whether some new insights emerge from the proposed analysis.
In particular, it is of interest to see whether inferences drawn by the trial team can be supplemented/strengthened further. The work is intended as a prototype. If results are encouraging, biostatistics community will need to be informed. The results will be published in a peer reviewed journal.

[{ "PostingID": 1309, "Title": "ROCHE-BO17708", "Description": "A randomized, double-blind study of the effect of first line treatment with Avastin (bevacizumab) in combination with docetaxel on progression-free survival and disease response in patients with HER2 negative metastatic breast cancer" },{ "PostingID": 1664, "Title": "GSK-GLP114179", "Description": "A Randomized, Open-Label, Parallel-Group, Multicenter Study to Determine the Efficacy and Safety of Albiglutide as Compared With Liraglutide in Subjects With Type 2 Diabetes Mellitus" },{ "PostingID": 2510, "Title": "BI-1245.23", "Description": "Efficacy and Safety Study With Empagliflozin (BI 10773) vs. Placebo as add-on to Metformin or Metformin Plus Sulfonylurea Over 24 Weeks in Patients With Type 2 Diabetes" },{ "PostingID": 3179, "Title": "LILLY-B9E-MC-JHQG", "Description": "Phase III Study of Gemcitabine Plus Paclitaxel Versus Paclitaxel in Patients With Unresectable, Locally Recurrent or Metastatic Breast Cancer" }]

Statistical Analysis Plan

Sharayu Paranjpe & Anil Gore. Adversity Index for Clinical Trials: An Inclusive Approach for Analysis of Safety Data.