Interim analysis

In clinical trials and other scientific studies, an interim analysis is an analysis of data that is conducted before data collection has been completed. Clinical trials are unusual in that enrollment of subjects is a continual process staggered in time. If a treatment can be proven to be clearly beneficial or harmful compared to the concurrent control, or to be obviously futile, based on a pre-defined analysis of an incomplete data set while the study is on-going, the investigators may stop the study early.

Statistical methods

The design of many clinical trials includes some strategy for early stopping if an interim analysis reveals large differences between treatment groups, or shows obvious futility such that there is no chance that continuing to the end would show a clinically meaningful effect. In addition to saving time and resources, such a design feature can reduce study participants' exposure to an inferior or useless treatment. However, when repeated significance testing on accumulating data is done, some adjustment of the usual hypothesis testing procedure must be made to maintain an overall significance level.[1][2] The methods described by Pocock[3][4] and O'Brien & Fleming,[5] among others,[6][7][8] are popular implementations of group sequential testing for clinical trials.[9][10][11] Sometimes interim analyses are equally spaced in terms of calendar time or the information available from the data, but this assumption can be relaxed to allow for unplanned or unequally spaced analyses.

Example

The second Multicenter Automatic Defibrillator Implantation Trial (MADIT II) was conducted to help better identify patients with coronary heart disease who would benefit from an ICD. MADIT II is the latest in a series of trials involving the use of ICDs to improve management and clinical treatment of arrhythmia patients. The Antiarrhythmics versus Implantable Defibrillators (AVID) Trial compared ICDs with antiarrhythmic-drug therapy (amiodarone or sotalol, predominantly the former) in patients who had survived life-threatening ventricular arrhythmias. After inclusion of 1,232 patients, the MADIT II study was terminated when interim analysis showed significant (31%) reduction in all-cause death in patients assigned to ICD therapy.[12]

References
  1. Armitage, P.; McPherson, C.K.; Rowe, B.C. (1969). "Repeated Significance Tests on Accumulating Data". Journal of the Royal Statistical Society, Series A. 132 (2): 235–244. doi:10.2307/2343787. JSTOR 2343787.
  2. McPherson, C.K.; Armitage, P. (1971). "Repeated Significance Tests on Accumulating Data". Journal of the Royal Statistical Society, Series A. 134 (1): 15–26. doi:10.2307/2343971. JSTOR 2343971.
  3. Pocock, S.J. (1977). "Group sequential methods in the design and analysis of clinical trials". Biometrika. 64 (2): 191–199. doi:10.2307/2335684. JSTOR 2335684.
  4. Pocock, S.J. (1982). "Interim Analyses for Randomized Clinical trials: The Group Sequential Approach". Biometrics. 38 (1): 153–162. doi:10.2307/2530298. JSTOR 2530298. PMID 7082757.
  5. O’Brien, P.C.; Fleming, T.R. (1979). "A Multiple Testing Procedure for Clinical Trials". Biometrics. 35 (3): 549–556. doi:10.2307/2530245. JSTOR 2530245. PMID 497341.
  6. Lan, K.G.; DeMets, D.L. (1983). "Discrete sequential boundaries for clinical trials". Biometrika. 70 (3): 659–663. doi:10.1093/biomet/70.3.659. JSTOR 2336502.
  7. Tang, Z (2015). "Optimal futility interim design: a predictive probability of success approach with time to event end point". Journal of Biopharmaceutical Statistics. 25 (6): 1312–1319. doi:10.1080/10543406.2014.983646. PMID 25379701. S2CID 1375275.
  8. Tang, Z (2017). "Defensive Efficacy Interim Design: dynamic benefit/risk ratio view using probability of success". Journal of Biopharmaceutical Statistics, 2016. 27 (4): 683–690. doi:10.1080/10543406.2016.1198370. PMID 27295497. S2CID 46723221.
  9. Jennison, Christopher; Turnbull, Bruce C. (1999). Group Sequential Methods with Applications to Clinical Trials. Boca Raton, Florida: Chapman & Hall/CRC. ISBN 978-0-8493-0316-6.
  10. Chin, Richard (2012). Adaptive and Flexible Clinical Trials. Boca Raton, Florida: Chapman & Hall/CRC. ISBN 978-1-4398-3832-7.
  11. Chow, Shein-Chow; Chang, Mark (2012). Adaptive Design Methods in Clinical Trials (2 ed.). Boca Raton, Florida: Chapman & Hall/CRC. ISBN 978-1-4398-3987-4.
  12. Moss AJ, Zareba W, Hall WJ, Klein H, Wilber DJ, Cannom DS, Daubert JP, Higgins SL, Brown MW, Andrews ML (2002). "Prophylactic Implantation of a Defibrillator in Patients with Myocardial Infarction and Reduced Ejection Fraction". New England Journal of Medicine. 346 (12): 877–83. doi:10.1056/NEJMoa013474. PMID 11907286.
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