Interim analysis: A rational approach of decision making in clinical trial

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Abstract

Interim analysis of especially sizeable trials keeps the decision process free of conflict of interest while considering cost, resources, and meaningfulness of the project. Whenever necessary, such interim analysis can also call for potential termination or appropriate modification in sample size, study design, and even an early declaration of success. Given the extraordinary size and complexity today, this rational approach helps to analyze and predict the outcomes of a clinical trial that incorporate what is learned during the course of a study or a clinical development program. Such approach can also fill the gap by directing the resources toward relevant and optimized clinical trials between unmet medical needs and interventions being tested currently rather than fulfilling only business and profit goals.

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Most cited references 21

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Multiplicity in randomised trials II: subgroup and interim analyses.

Kenneth F Schulz, David A Grimes (2015)

Subgroup analyses can pose serious multiplicity concerns. By testing enough subgroups, a false-positive result will probably emerge by chance alone. Investigators might undertake many analyses but only report the significant effects, distorting the medical literature. In general, we discourage subgroup analyses. However, if they are necessary, researchers should do statistical tests of interaction, rather than analyse every separate subgroup. Investigators cannot avoid interim analyses when data monitoring is indicated. However, repeatedly testing at every interim raises multiplicity concerns, and not accounting for multiplicity escalates the false-positive error. Statistical stopping methods must be used. The O'Brien-Fleming and Peto group sequential stopping methods are easily implemented and preserve the intended alpha level and power. Both adopt stringent criteria (low nominal p values) during the interim analyses. Implementing a trial under these stopping rules resembles a conventional trial, with the exception that it can be terminated early should a treatment prove greatly superior. Investigators and readers, however, need to grasp that the estimated treatment effects are prone to exaggeration, a random high, with early stopping.

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When (not) to stop a clinical trial for benefit.

Nicola S. Pocock (2005)

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The Design and Analysis of Sequential Clinical Trials.

J. Whitehead (1997)

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Author and article information

Journal

Journal ID (nlm-ta): J Adv Pharm Technol Res

Journal ID (iso-abbrev): J Adv Pharm Technol Res

Journal ID (publisher-id): JAPTR

Title: Journal of Advanced Pharmaceutical Technology & Research

Publisher: Medknow Publications & Media Pvt Ltd (India )

ISSN (Print): 2231-4040

ISSN (Electronic): 0976-2094

Publication date (Print): Oct-Dec 2016

Volume: 7

Issue: 4

Pages: 118-122

Affiliations

[1]PhD Scholar, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India

[1 ]Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, Gujarat, India

Author notes

Address for correspondence: Dr. Bhaswat S. Chakraborty, Cadila Pharmaceuticals Limited, Ahmedabad, Gujarat, India. E-mail: drb.chakraborty@ 123456cadilapharma.co.in

Article

Publisher ID: JAPTR-7-118

DOI: 10.4103/2231-4040.191414

PMC ID: 5052936

PubMed ID: 27833889

SO-VID: bee1d5e8-7073-42a1-a1a2-8e93e436fae2

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

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