Adaptive design of clinical trials : understanding the barriers to adoption

There is great competition for clinical research funding. This is in part due to the National Institute of Health's reduced budget to support such initiatives. It has resulted in a growing trend for clinical research to use adaptive design models to accelerate clinical trials and at the same time reduce overall cost. Although such models have existed for several years, the pace of adoption remains slow, especially for early-stage clinical research. Through a review of relevant literature and interviews with industry experts, this thesis explores the barriers that inhibit the adoption of adaptive design of clinical trials. Reasons uncovered include: a lack of novel funding mechanisms, regulatory uncertainty, logistical difficulties, overly technical communications, a lack of collaboration among stakeholders, and an inability to recruit and retain patients. Then follows a series of possible solutions - some already functioning, others possible - for each of the barriers. This research found that unless efforts are devoted to addressing these underlying barriers, the widespread adoption of adaptive designs for clinical trials will not occur. The thesis concludes with recommendations and suggestions for future research.

Department dc:contributor.department

Sloan School of Management.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Khalid, Ayesha N. (Ayesha Naz)

Advisor dc:contributor.advisor

• Andrew Lo.

• Sloan School of Management.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/90220

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/90220