Delta Inflation, Optimism Bias, and Uncertainty in Clinical Trials

Djulbegovic B, Kumar A, Glasziou P, Miladinovic B, Chalmers I. Trial unpredictability yields predictable therapy gains, Nature, vol. 500, no. 7463, pp. 395–396, Aug. 2013, https://doi.org/10.1038/500395a

Chalmers I, Matthews R. What are the implications of optimism bias in clinical research? Lancet. Feb. 2006;367(9509):449–50. https://doi.org/10.1016/S0140-6736(06)68153-1.

Bahnam P, et al. Most placebo-controlled trials in inflammatory bowel disease were underpowered because of Overestimated Drug Efficacy Rates: results from a systematic review of induction studies. J Crohn’s Colitis. Oct. 2022;jjac150. https://doi.org/10.1093/ecco-jcc/jjac150.

Seehra J, Stonehouse-Smith D, Cobourne MT, Tsagris M, Pandis N. Are treatment effect assumptions in orthodontic studies overoptimistic? European Journal of Orthodontics, vol. 43, no. 5, pp. 583–587, Oct. 2021, https://doi.org/10.1093/ejo/cjab018

Aberegg SK, Richards DR, O’Brien JM. Delta inflation: a bias in the design of randomized controlled trials in critical care medicine. Crit Care, 14, 2010.

Latronico N, Metelli M, Turin M, Piva S, Rasulo FA, Minelli C. Quality of reporting of randomized controlled trials published in Intensive Care Medicine from 2001 to 2010, Intensive Care Med, vol. 39, no. 8, pp. 1386–1395, Aug. 2013, https://doi.org/10.1007/s00134-013-2947-3

Harhay MO, et al. Outcomes and statistical power in adult critical care randomized trials. Am J Respir Crit Care Med. Jun. 2014;189(12):1469–78. https://doi.org/10.1164/rccm.201401-0056CP.

Abrams D et al. Dec., Powering Bias and Clinically Important Treatment Effects in Randomized Trials of Critical Illness*, Critical Care Medicine, vol. 48, no. 12, pp. 1710–1719, 2020, https://doi.org/10.1097/CCM.0000000000004568

Djulbegovic B, et al. Optimism bias leads to inconclusive results—an empirical study. J Clin Epidemiol. Jun. 2011;64(6):583–93. https://doi.org/10.1016/j.jclinepi.2010.09.007.

Gan HK, You B, Pond GR, Chen EX. Assumptions of Expected Benefits in Randomized Phase III Trials Evaluating Systemic Treatments for Cancer, JNCI Journal of the National Cancer Institute, vol. 104, no. 8, pp. 590–598, Apr. 2012, https://doi.org/10.1093/jnci/djs141

Zakeri K, Noticewala S, Vitzthum L, Sojourner E, Shen H, Mell L. ‘Optimism bias’ in contemporary national clinical trial network phase III trials: are we improving? Annals of Oncology, vol. 29, no. 10, pp. 2135–2139, Oct. 2018, https://doi.org/10.1093/annonc/mdy340

Al-Showbaki L, Almugbel FA, Alqaisi HA, Amir E, Chen EX. Optimism Bias in the Design of Phase III Randomized Control Trials Evaluating PD-1/PD-L1 Targeting Monoclonal Antibodies, The Oncologist, vol. 27, no. 6, pp. 487–492, Jun. 2022, https://doi.org/10.1093/oncolo/oyac031

Wong H. Minimum important difference is minimally important in sample size calculations. Trials. Jan. 2023;24(1):34. https://doi.org/10.1186/s13063-023-07092-8.

Hey SP. Ethics and epistemology of accurate prediction in clinical research, J Med Ethics, vol. 41, no. 7, pp. 559–562, Jul. 2015, https://doi.org/10.1136/medethics-2013-101868

Kraemer HC, Mintz J, Noda A, Tinklenberg J, Yesavage JA. Caution regarding the Use of Pilot studies to Guide Power calculations for study proposals. Arch Gen Psychiatry. May 2006;63(5):484. https://doi.org/10.1001/archpsyc.63.5.484.

Djulbegovic B. Articulating and Responding to Uncertainties in Clinical Research, J. of Med. & Philosophy, vol. 32, no. 2, pp. 79–98, Mar. 2007, https://doi.org/10.1080/03605310701255719

Alexander JH. Equipoise in clinical trials: enough uncertainty in whose opinion? Circulation. Mar. 2022;145(13):943–5. https://doi.org/10.1161/CIRCULATIONAHA.121.057201.

Hey SP, Kimmelman J. Do we know whether researchers and reviewers are estimating risk and benefit accurately? Are researchers and reviewers estimating risk and benefit accurately? Bioethics. Oct. 2016;30(8):609–17. https://doi.org/10.1111/bioe.12260.

Parker RA, Cook JA. The importance of clinical importance when determining the target difference in sample size calculations. Trials. Aug. 2023;24(1):495. https://doi.org/10.1186/s13063-023-07532-5.

Hey SP, Kimmelman J. Ethics, Error, and initial trials of efficacy. Sci Transl Med. May 2013;5(184). https://doi.org/10.1126/scitranslmed.3005684.

Hey SP, London AJ, Weijer C, Rid A, Miller F. Is the concept of clinical equipoise still relevant to research? BMJ, p. j5787, Dec. 2017, https://doi.org/10.1136/bmj.j5787

Lehmann EL. The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two? Journal of the American Statistical Association, vol. 88, no. 424, pp. 1242–1249, Dec. 1993, https://doi.org/10.1080/01621459.1993.10476404

ICH, E 9 Statistical Principles for Clinical Trials. 1998. Accessed: Jun. 07, 2023. [Online]. Available: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-9-statistical-principles-clinical-trials-step-5_en.pdf

Schulz KF, Grimes DA. Sample size calculations in randomised trials: mandatory and mystical, The Lancet, vol. 365, no. 9467, pp. 1348–1353, Apr. 2005, https://doi.org/10.1016/S0140-6736(05)61034-3

Chuang-Stein C, Kirby S. The shrinking or disappearing observed treatment effect. Pharm Stat. 2014;13(5):277–80. https://doi.org/10.1002/pst.1633.

Article  PubMed  Google Scholar 

Erdmann S, Kirchner M, Götte H, Kieser M. Optimal designs for phase II/III drug development programs including methods for discounting of phase II results. BMC Med Res Methodol. Oct. 2020;20(1):253. https://doi.org/10.1186/s12874-020-01093-w.

Rothwell JC, Julious SA, Cooper CL. Adjusting for bias in the mean for primary and secondary outcomes when trials are in sequence. Pharm Stat. 2022;21(2):460–75. https://doi.org/10.1002/pst.2180.

Article  PubMed  Google Scholar 

Wiklund SJ, Burman C-F. Selection bias, investment decisions and treatment effect distributions. Pharm Stat. 2021;20:1168–82. https://doi.org/10.1002/pst.2132.

Article  PubMed  PubMed Central  Google Scholar 

Spiegelhalter DJ, Freedman LS, Parmar MKB. Bayesian approaches to Randomized trials. J Royal Stat Soc Ser (Statistics Society). 1994;157(3):357. https://doi.org/10.2307/2983527.

Article  Google Scholar 

Spiegelhalter DJ. Incorporating bayesian ideas into health-care evaluation. Statist Sci. Feb. 2004;19(1). https://doi.org/10.1214/088342304000000080.

Berry DA. Bayesian clinical trials. Nat Rev Drug Discov. Jan. 2006;5(1):27–36. https://doi.org/10.1038/nrd1927.

Hampson LV, et al. A New Comprehensive Approach to assess the probability of success of Development Programs before pivotal trials. Clin Pharma Ther. May 2022;111(5):1050–60. https://doi.org/10.1002/cpt.2488.

O’Hagan A, Stevens JW, Campbell MJ. Assurance in clinical trial design, Pharmaceut. Statist., vol. 4, no. 3, pp. 187–201, Jul. 2005, https://doi.org/10.1002/pst.175

Carroll KJ. Decision Making from Phase II to Phase III and the Probability of Success: Reassured by ‘Assurance’? Journal of Biopharmaceutical Statistics, vol. 23, no. 5, pp. 1188–1200, Sep. 2013, https://doi.org/10.1080/10543406.2013.813527

Chen D-G, Ho S, editors. From statistical power to statistical assurance: It’s time for a paradigm change in clinical trial design, Communications in Statistics - Simulation and Computation, vol. 46, no. 10, pp. 7957–7971, 2016, https://doi.org/10.1080/03610918.2016.1259476

Crisp A, Miller S, Thompson D, Best N. Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development, Pharmaceutical Statistics, vol. 17, no. 4, pp. 317–328, Jul. 2018, https://doi.org/10.1002/pst.1856

Wang Y, Fu H, Kulkarni P, Kaiser C. Evaluating and utilizing probability of study success in clinical development, Clinical Trials, vol. 10, no. 3, pp. 407–413, Jun. 2013, https://doi.org/10.1177/1740774513478229

Zierhut M, Bycott P, Gibbs M, Smith B, Vicini P. Ignorance is not bliss: statistical power is not probability of trial success. Clin Pharmacol Ther. Apr. 2016;99(4):356–9. https://doi.org/10.1002/cpt.257.

Dallow N, Best N, Montague TH. Better decision making in drug development through adoption of formal prior elicitation. Pharm Stat. Jul. 2018;17(4):301–16. https://doi.org/10.1002/pst.1854.

Brown BW, Herson J, Neely Atkinson E, Elizabeth Rozell M. Projection from previous studies: A Bayesian and frequentist compromise, Controlled Clinical Trials, vol. 8, no. 1, pp. 29–44, Mar. 1987, https://doi.org/10.1016/0197-2456(87)90023-7

Ciarleglio MM, Arendt CD, Makuch RW, Peduzzi PN. Selection of the treatment effect for sample size determination in a superiority clinical trial using a hybrid classical and Bayesian procedure, Contemporary Clinical Trials, vol. 41, pp. 160–171, Mar. 2015, https://doi.org/10.1016/j.cct.2015.01.002

Walley RJ, Smith CL, Gale JD, Woodward P. Advantages of a wholly bayesian approach to assessing efficacy in early drug development: a case study. Pharmaceut Statist. May 2015;14(3):205–15. https://doi.org/10.1002/pst.1675.

Hampson LV et al. Mar., Improving the assessment of the probability of success in late stage drug development, Pharmaceutical Statistics, vol. 21, no. 2, pp. 439–459, 2022, https://doi.org/10.1002/pst.2179

Temple JR, Robertson JR. Conditional assurance: the answer to the questions that should be asked within drug development, Pharmaceutical Statistics, vol. 20, no. 6, pp. 1102–1111, Nov. 2021, https://doi.org/10.1002/pst.2128

Chuang-Stein C, Yang R. A revisit of sample size decisions in confirmatory trials. Stat Biopharm Res. May 2010;2(2):239–48. https://doi.org/10.1198/sbr.2009.0073.

Thomas D et al. Clinical development success rates and contributing Factors 2011–2020. 2021. Accessed: Dec. 21, 2021. [Online]. Available: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf

Box GEP. Sampling and Bayes’ inference in scientific modelling and robustness. J Royal Stat Soc Ser (General). 1980;143(4):383. https://doi.org/10.2307/2982063.

Article  Google Scholar 

van de Schoot R, et al. Bayesian statistics and modelling. Nat Rev Methods Primers. Jan. 2021;1(1):1. https://doi.org/10.1038/s43586-020-00001-2.

Evans M, Moshonov H. Checking for prior-data conflict. Bayesian Anal. Dec. 2006;1(4). https://doi.org/10.1214/06-BA129.

Nott DJ, Drovandi CC, Mengersen K, Evans M. Approximation of bayesian predictive p-Values with regression ABC. Bayesian Anal. Mar. 2018;13(1). https://doi.org/10.1214/16-BA1033.

Daimon T. Predictive checking for bayesian interim analyses in clinical trials. Contemp Clin Trials. Sep. 2008;29(5):740–50. https://doi.org/10.1016/j.cct.2008.05.005.

Mutsvari T, Tytgat D, Walley R. Addressing potential prior-data conflict when using informative priors in proof‐of‐concept studies. Pharmaceut Statist. Jan. 2016;15(1):28–36. https://doi.org/10.1002/pst.1722.

Ruberg SJ et al. Feb., Application of Bayesian approaches in drug development: starting a virtuous cycle, Nat Rev Drug Discov, 2023, https://doi.org/10.1038/s41573-023-00638-0

Atanasov P et al. Aug., Wisdom of the expert crowd prediction of response for 3 neurology randomized trials, Neurology, vol. 95, no. 5, pp. e488–e498, 2020, https://doi.org/10.1212/WNL.0000000000009819

Benjamin DM, et al. Can oncologists predict the efficacy of treatments in Randomized trials? Oncologist. Jan. 2021;26(1):56–62. https://doi.org/10.1634/theoncologist.2020-0054.

Benjamin DM et al. Feb., Principal investigators over-optimistically forecast scientific and operational outcomes for clinical trials, PLoS ONE, vol. 17, no. 2, p. e0262862, 2022, https://doi.org/10.1371/journal.pone.0262862

Chongwe G, Ali J, Kaye DK, Michelo C, Kass NE. Ethics of Adaptive Designs for Randomized Controlled Trials, Ethics & Human Research, vol. 45, no. 5, pp. 2–14, Sep. 2023, https://doi.org/10.1002/eahr.500178

Vandemeulebroecke M. Group Sequential and Adaptive Designs - A Review of Basic Concepts and Points of Discussion, Biom. J., vol. 50, no. 4, pp. 541–557, Aug. 2008, https://doi.org/10.1002/bimj.200710436

Walter SD, et al. A systematic survey of randomised trials that stopped early for reasons of futility. BMC Med Res Methodol. Dec. 2020;20(1):10. https://doi.org/10.1186/s12874-020-0899-1.

Zhang JJ, Blumenthal GM, He K, Tang S, Cortazar P, Sridhara R. Overestimation of the effect size in group sequential trials. Clin Cancer Res. Sep. 2012;18:4872–6. https://doi.org/10.1158/1078-0432.CCR-11-3118.

Hopewell S, Loudon K, Clarke MJ, Oxman AD, Dickersin K. Publication bias in clinical trials due to statistical significance or direction of trial results. Cochrane Database Syst Rev. Jan. 2009;2009(1):MR. https://doi.org/10.1002/14651858.MR000006.pub3.

Vickers AJ. Underpowering in randomized trials reporting a sample size calculation. J Clin Epidemiol. Aug. 2003;56(8):717–20. https://doi.org/10.1016/S0895-4356(03)00141-0.

Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: A practical guide with examples, Statist. Med., vol. 30, no. 28, pp. 3267–3284, Dec. 2011, https://doi.org/10.1002/sim.4102

Proschan MA. Two-stage sample size re-estimation based on a nuisance parameter: a review. J Biopharm Stat. 2005;15(4):559–74. https://doi.org/10.1081/BIP-200062852.

Article  PubMed  Google Scholar 

留言 (0)

沒有登入
gif