An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials

Overview

Journal Int J Clin Biostat Biom

Publisher ClinMed International Library

Date 2021 Oct 11

PMID 34632463

Authors

Greg Dyson

Affiliations

Soon will be listed here.

Abstract

Background: Phase III superiority clinical trials have negative results (new treatment is not statistically better than standard of care) due to a number of factors, including patient and disease heterogeneity. However, even a treatment regime that fails to show population-level clinical improvement will have a subgroup of patients that attain a measurable clinical benefit.

Objective: The goal of this paper is to modify the Patient Rule-Induction Method to identify statistically significant subgroups, defined by clinical and/or demographic factors, of the clinical trial population where the experimental treatment performs better than the standard of care and better than observed in the entire clinical trial sample.

Results: We illustrate this method using part A of the SUCCESS clinical trial, which showed no overall difference between treatment arms: HR (95% CI) = 0.97 (0.78, 1.20). Using PRIM, we identified one subgroup defined by the mutational profile in BRCA1 which resulted in a significant benefit for adding Gemcitabine to the standard treatment: HR (95% CI) = 0.59 (0.40, 0.87).

Conclusion: This result demonstrates that useful information can be extracted from existing databases that could provide insight into why a phase III trial failed and assist in the design of future clinical trials involving the experimental treatment.

References

Lipkovich I, Dmitrienko A, Denne J, Enas G . Subgroup identification based on differential effect search--a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med. 2011; 30(21):2601-21. DOI: 10.1002/sim.4289. View

LeBlanc M, Jacobson J, Crowley J . Partitioning and peeling for constructing prognostic groups. Stat Methods Med Res. 2002; 11(3):247-74. DOI: 10.1191/0962280202sm286ra. View

Dyson G, Frikke-Schmidt R, Nordestgaard B, Tybjaerg-Hansen A, Sing C . An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease. Genet Epidemiol. 2007; 31(6):515-27. PMC: 2045699. DOI: 10.1002/gepi.20225. View

Berger J, Wang X, Shen L . A Bayesian approach to subgroup identification. J Biopharm Stat. 2014; 24(1):110-29. DOI: 10.1080/10543406.2013.856026. View

Fogel D . Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun. 2018; 11:156-164. PMC: 6092479. DOI: 10.1016/j.conctc.2018.08.001. View

Chen G, Zhong H, Belousov A, Devanarayan V . A PRIM approach to predictive-signature development for patient stratification. Stat Med. 2014; 34(2):317-42. PMC: 4285951. DOI: 10.1002/sim.6343. View

Wang S, Dmitrienko A . Guest editors' note: Special issue on subgroup analysis in clinical trials. J Biopharm Stat. 2014; 24(1):1-3. DOI: 10.1080/10543406.2014.858958. View

Foster J, Taylor J, Ruberg S . Subgroup identification from randomized clinical trial data. Stat Med. 2011; 30(24):2867-80. PMC: 3880775. DOI: 10.1002/sim.4322. View

Liu X, Minin V, Huang Y, Seligson D, Horvath S . Statistical methods for analyzing tissue microarray data. J Biopharm Stat. 2004; 14(3):671-85. DOI: 10.1081/BIP-200025657. View

10.

Dyson G, Sing C . Efficient identification of context dependent subgroups of risk from genome-wide association studies. Stat Appl Genet Mol Biol. 2014; 13(2):217-26. PMC: 4171947. DOI: 10.1515/sagmb-2013-0062. View

11.

Frikke-Schmidt R, Tybjaerg-Hansen A, Dyson G, Haase C, Benn M, Nordestgaard B . Subgroups at high risk for ischaemic heart disease:identification and validation in 67 000 individuals from the general population. Int J Epidemiol. 2014; 44(1):117-28. PMC: 4339759. DOI: 10.1093/ije/dyu215. View

12.

Stram D . Software for tag single nucleotide polymorphism selection. Hum Genomics. 2005; 2(2):144-51. PMC: 3525260. DOI: 10.1186/1479-7364-2-2-144. View

13.

Schnell P, Tang Q, Offen W, Carlin B . A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects. Biometrics. 2016; 72(4):1026-1036. PMC: 5102829. DOI: 10.1111/biom.12522. View

14.

Hammer S, Katzenstein D, Hughes M, Gundacker H, Schooley R, Haubrich R . A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. AIDS Clinical Trials Group Study 175 Study Team. N Engl J Med. 1996; 335(15):1081-90. DOI: 10.1056/NEJM199610103351501. View

15.

Lipkovich I, Dmitrienko A . Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES. J Biopharm Stat. 2014; 24(1):130-53. DOI: 10.1080/10543406.2013.856024. View

16.

OSullivan Coyne G, Takebe N, Chen A . Defining precision: The precision medicine initiative trials NCI-MPACT and NCI-MATCH. Curr Probl Cancer. 2017; 41(3):182-193. DOI: 10.1016/j.currproblcancer.2017.02.001. View

17.

Nelson M, Kardia S, Ferrell R, Sing C . A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res. 2001; 11(3):458-70. PMC: 311041. DOI: 10.1101/gr.172901. View

18.

Dyson G, Frikke-Schmidt R, Nordestgaard B, Tybjaerg-Hansen A, Sing C . Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease. Genet Epidemiol. 2008; 33(4):317-24. PMC: 2676926. DOI: 10.1002/gepi.20383. View

19.

Su X, Zhou T, Yan X, Fan J, Yang S . Interaction trees with censored survival data. Int J Biostat. 2010; 4(1):Article 2. PMC: 2835451. DOI: 10.2202/1557-4679.1071. View

20.

Liu Y, Ma X, Zhang D, Geng L, Wang X, Zheng W . Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification. J Biopharm Stat. 2019; 29(6):1082-1102. PMC: 6742587. DOI: 10.1080/10543406.2019.1584204. View