Related Experiment Video
Updated: Jul 7, 2025

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
Published on: November 30, 2022
Understanding the value of biobank attributes to researchers using a conjoint experiment
Deepshikha Batheja1, Srishti Goel2, Warren Fransman3
1One Health Trust, Obeya Pulse, First Floor, 7/1, Halasur Road, Bengaluru, Karnataka, 560102, India. deepshikha@onehealthtrust.org.
Abstract:
Biobanks are important in biomedical and public health research, and future healthcare research relies on their strength and capacity. However, there are financial challenges related to the operation of commercial biobanks and concerns around the commercialization of biobanks. Non-commercial biobanks depend on grant funding to operate and could be valuable to researchers if they can enable access to quality specimens at lower costs. The objective of this study is to estimate the value of specific biobank attributes. We used a rating-based conjoint experiment approach to study how researchers valued handling fee, access, quality, characterization, breadth of consent, access to key endemics, and time taken to fulfil requests. We found that researchers placed the greatest relative importance on the quality of specimens (26%), followed by the characterization of specimens (21%). Researchers with prior experience purchasing biological samples also valued access to key endemic in-country sites (11.6%) and low handling fees (5.5%) in biobanks.
Related Concept Videos
Biostatistics: Overview
Discrete variables are...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Data Collection by Experiments
An example of the experimental method is a public...
Experimental Designs
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Factorial Design

