Sample Size Estimation for a Non-inferiority Pain Management Trial
Anadya Prakash Tripathi1, *, Rama Shanker1
Identifiers and Pagination:Year: 2023
E-location ID: e187638632301260
Publisher ID: e187638632301260
Article History:Received Date: 03/08/2022
Revision Received Date: 15/01/2023
Acceptance Date: 16/01/2023
Electronic publication date: 23/02/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Measuring pain and pain relief are the primary concerns in pain management. Sample size estimation in pain management with non-inferiority (NI) study design and assessment of specific-NI margin endpoints may be challenging as pain and its improvement are measured and reported on different endpoints.
Multiple endpoints were reported frequently to measure pain and pain improvement. The sum of pain intensity difference (SPID[0-t]) at a specific time is the recommended endpoint for the measurement of pain by the United States Food and Drug Administration. Statistical information on SPID and other endpoints reported in multiple works in the literature (preferably from placebo-controlled trials) was collected and compared to identify a suitable NI margin. A difference of 20% was considered the default NI margin for evaluation, and the sample size was calculated for each endpoint.
The sample size based on the FDA-recommended primary endpoint SPID was found to be larger. This may be a concern for overall clinical operation and the availability of patients for recruitment in time. The sample size obtained for the minimal clinically important difference (MCID) endpoint was feasible and justifiable from an operational and clinical standpoint.
Evaluation and assessment of multiple endpoints before designing an NI study enable rapid decision-making on endpoint selection and increase operational efficiency.