Abstract
Modern randomized controlled trials often involve multiple periods of data collection separated by interim analyses, where the accumulated data is analyzed and findings are used to make adjustments to the ongoing trial. Various endpoints can be used to influence these decisions, including primary or surrogate outcome data, safety data, administrative data, and/or new external information. Example uses of interim analyses include deciding if there is evidence that a trial should be stopped early for safety, efficacy, or futility or if the treatment allocation ratios should be modified to optimize trial efficiency and better align the risk-benefit ratio. Additionally, a decision could be made to lengthen or shorten a trial based on observed information. To avoid unwanted bias, studies known as adaptive design clinical trials pre-specify these decision rules in the study protocol. Extensive simulation studies are often required during study planning and protocol development in order to characterize operating characteristics and validate testing procedures and parameter estimation. Over time, researchers have gained a better understanding of the strengths and limitations of employing interim analyses in their clinical studies. In particular, with proper planning and conduct, adaptive designs incorporating interim analyses can provide great benefits in flexibility and efficiency. However, an increase in infrastructure for development and planning is needed to successfully implement adaptive designs and interim analyses and allow their potential advantages to be achieved in clinical research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson J, High R (2011) Alternatives to the standard Fleming, Harrington, and O’Brien futility boundary. Clin Trials 8(3):270–276. https://doi.org/10.1177/1740774511401636
Armitage P, McPherson C et al (1969) Repeated significance tests on accumulating data. J R Stat Soc Ser A 132(2):235–244. https://doi.org/10.2307/2343787
Bassler D, Briel M et al (2010) Stopping randomized trials early for benefit and estimation of treatment effects: systematic review and meta-regression analysis. JAMA 303(12):1180–1187. https://doi.org/10.1001/jama.2010.310
Bauer P, Kohne K (1994) Evaluation of experiments with adaptive interim analyses. Biometrics 50(4):1029–1041. https://doi.org/10.2307/2533441
Berry S, Carlin B et al (2010) Bayesian adaptive methods for clinical trials. CRC Press, Boca Raton
Berry S, Broglio K et al (2013) Bayesian hierarchical modeling of patient subpopulations: efficient designs of Phase II oncology clinical trials. Clin Trials 10(5):720–734. https://doi.org/10.1177/1740774513497539
Beta-Blocker Heart Attack Study Group (1981) The beta-blocker heart attack trial. JAMA 246(18):2073–2074
Bhatt D, Mehta C (2016) Adaptive designs for clinical trials. N Engl J Med 375(1):65–74. https://doi.org/10.1056/NEJMra1510061
Brakenhoff T, Roes K et al (2018) Bayesian sample size re-estimation using power priors. Stat Methods Med Res. https://doi.org/10.1177/0962280218772315
Brannath W, Koenig F et al (2007) Multiplicity and flexibility in clinical trials. Pharm Stat J Appl Stat Pharm Ind 6(3):205–216. https://doi.org/10.1002/pst.302
Burman C, Sonesson C (2006) Are flexible designs sound? Biometrics 62(3):664–669. https://doi.org/10.1111/j.1541-0420.2006.00626.x
Chaitman B, Pepine C et al (2004) Effects of ranolazine with atenolol, amlodipine, or diltiazem on exercise tolerance and angina frequency in patients with severe chronic angina: a randomized controlled trial. JAMA 291(3):309–316. https://doi.org/10.1001/jama.291.3.309
Chen Y, Gesser R et al (2015) A seamless phase IIb/III adaptive outcome trial: design rationale and implementation challenges. Clin Trials 12(1):84–90. https://doi.org/10.1177/1740774514552110
Choko A, Corbett E et al (2019) HIV self-testing alone or with additional interventions, including financial incentives, and linkage to care or prevention among male partners of antenatal care clinic attendees in Malawi: an adaptive multi-arm, multi-stage cluster randomized trial. PLoS Med 16(1). https://doi.org/10.1371/journal.pmed.1002719
Coffey C, Levin B et al (2012) Overview, hurdles, and future work in adaptive designs: perspectives from a National Institutes of Health-funded workshop. Clin Trials 9(6):671–680. https://doi.org/10.1177/1740774512461859
Cui L, Hung H et al (1999) Modification of sample size in group sequential clinical trials. Biometrics 55(3):853–857. https://doi.org/10.1111/j.0006-341X.1999.00853.x
Ellenberg S, Fleming T et al (eds) (2003) Data monitoring committees in clinical trials: a practical perspective. Wiley, Chichester
European Medicines Agency (2007) Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. Retrieved from http://www.ema.europa.eu
Fleming T, Harrington D et al (1984) Designs for group sequential tests. Control Clin Trials 5(4):349–361. https://doi.org/10.1016/S0197-2456(84)80014-8
Food and Drug Administration (2018) Adaptive designs for clinical trials of drugs and biologics: guidance for industry. Retrieved from https://www.fda.gov
Friede T, Kieser M (2011) Blinded sample size recalculation for clinical trials with normal data and baseline adjusted analysis. Pharm Stat 10(1):8–13. https://doi.org/10.1002/pst.398
Garrett-Mayer E (2006) The continual reassessment method for dose-finding studies: a tutorial. Clin Trials 3(1):57–71. https://doi.org/10.1191/1740774506cn134oa
Gould A, Shih W (1992) Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance. Commun Stat Theory Methods 21(10):2833–2853. https://doi.org/10.1080/03610929208830947
Heart Special Project Committee (1988) Organization, review and administration of cooperative studies (Greenberg report): a report from the Heart Special Project Committee to the National Advisory Council, May 1967. Control Clin Trials 9:137–148
Ho T, Pearlman E et al (2012) Efficacy and tolerability of rizatriptan in pediatric migraineurs: results from a randomized, double-blind, placebo-controlled trial using a novel adaptive enrichment design. Cephalalgia 32(10):750–765. https://doi.org/10.1177/0333102412451358
Hommel G (2001) Adaptive modifications of hypotheses after an interim analysis. Biom J 43(5):581–589. https://doi.org/10.1002/1521-4036(200109)43:5<581::AID-BIMJ581>3.0.CO;2-J
Hung H, O’Neill R et al (2006) A regulatory view on adaptive/flexible clinical trial design. Biometr J 48(4):565–573. https://doi.org/10.1002/bimj.200610229
Jennison C, Turnbull B (2006) Efficient group sequential designs when there are several effect sizes under consideration. Stat Med 25(6):917–932. https://doi.org/10.1002/sim.2251
Jolly S, Gao P et al (2018) Risks of overinterpreting interim data: lessons from the TOTAL trial (thrombectomy with PCI versus PCI alone in patients with STEMI). Circulation 137(2):206–209. https://doi.org/10.1161/CIRCULATIONAHA.117.030656
Kairalla J, Coffey C et al (2012) Adaptive trial designs: a review of barriers and opportunities. Trials 13(1):145. https://doi.org/10.1186/1745-6215-13-145
Kimani P, Todd S et al (2015) Estimation after subpopulation selection in adaptive seamless trials. Stat Med 34(18):2581–2601. https://doi.org/10.1002/sim.6506
Krams M, Lees K et al (2003) Acute stroke therapy by inhibition of neutrophils (ASTIN). Stroke 34(11):2543–2548. https://doi.org/10.1161/01.STR.0000092527.33910.89
Lachin J (2005) A review of methods for futility stopping based on conditional power. Stat Med 24(18):2747–2764. https://doi.org/10.1002/sim.2151
Lan K, DeMets D (1983) Discrete sequential boundaries for clinical trials. Biometrika 70:659–663. https://doi.org/10.1093/biomet/70.3.659
Leonardi S, Mahaffey K et al (2012) Rationale and design of the Cangrelor versus standard therapy to achieve optimal Management of Platelet Inhibition PHOENIX trial. Am Heart J 163(5):768–776. https://doi.org/10.1016/j.ahj.2012.02.018
Mehta C, Pocock S (2011) Adaptive increase in sample size when interim results are promising: a practical guide with examples. Stat Med 30(28):3267–3284. https://doi.org/10.1002/sim.4102
Moher D, Hopewell S et al (2010) CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol 63(8):e1–e37. Retrieved from www.consort-statement.org
O’Brien P, Fleming T (1979) A multiple testing procedure for clinical trials. Biometrics 35(3):549–556. https://doi.org/10.2307/2530245
Pallmann P, Bedding A et al (2018) Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 16(1):29. https://doi.org/10.1186/s12916-018-1017-7
Piccart-Gebhart M, Procter M et al (2005) Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 353(16):1659–1672. https://doi.org/10.1056/NEJMoa052306
Pocock S (1977) Group sequential methods in the design and analysis of clinical trials. Biometrika 64(2):191–199. https://doi.org/10.1093/biomet/64.2.191
Pritchett Y, Jemiai Y et al (2011) The use of group sequential, information-based sample size re-estimation in the design of the PRIMO study of chronic kidney disease. Clin Trials 8(2):165–174. https://doi.org/10.1177/1740774511399128
Proschan M (2009) Sample size re-estimation in clinical trials. Biometr J 51(2):348–357. https://doi.org/10.1002/bimj.200800266
Proschan M, Hunsberger S (1995) Designed extension of studies based on conditional power. Biometrics 51(4):1315–1324. https://doi.org/10.1016/0197-2456(95)91243-6
Savitz J, Teague T et al (2018) Treatment of bipolar depression with minocycline and/or aspirin: an adaptive, 2× 2 double-blind, randomized, placebo-controlled, phase IIA clinical trial. Transl Psychiatry 8(1):27. https://doi.org/10.1038/s41398-017-0073-7
Shimura M (2019) Reducing overestimation of the treatment effect by interim analysis when designing clinical trials. J Clin Pharm Ther 44(2):243–248. https://doi.org/10.1111/jcpt.12777
Stallard N, Todd S (2010) Seamless phase II/III designs. Stat Methods Med Res 20(6):626–634. https://doi.org/10.1177/0962280210379035
Tsiatis A (2006) Information-based monitoring of clinical trials. Stat Med 25(19):3236–3244. https://doi.org/10.1002/sim.2625
Tsiatis A, Mehta C (2003) On the inefficiency of the adaptive design for monitoring clinical trials. Biometrika 90(2):367–378. https://doi.org/10.1093/biomet/90.2.367
White W, Cannon C et al (2013) Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med 369(14):1327–1335. https://doi.org/10.1056/NEJMoa1305889
Wittes J, Brittain E (1990) The role of internal pilot studies in increasing the efficacy of clinical trials. Stat Med 9(1–2):65–72. https://doi.org/10.1002/sim.4780090113
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Kairalla, J.A., Zahigian, R., Wu, S.S. (2022). Interim Analysis in Clinical Trials. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_84
Download citation
DOI: https://doi.org/10.1007/978-3-319-52636-2_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52635-5
Online ISBN: 978-3-319-52636-2
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering