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Subgroup Analysis in Confirmatory Trials: Consensus and Opportunities
The importance of assessing consistency of treatment effect across subgroups in confirmatory trials is widely accepted. Forest plots and interaction tests are routinely applied to explore potential heterogeneity of treatment effects but are known to lack power to detect important subgroup effects, and lack control of type I error (i.e., increased chance of false positive subgroup findings) [1,2]. Recent updates in regulatory guidance [3] and an Industry working group white paper [4] have shed new light on this critical aspect of regulatory assessment for confirmatory trials. Consensus has emerged on the categorization of subgroup investigations into 1) confirmatory, 2) biologically plausible and 3) hypothesis generating, and the associated statistical rigor in the assessment of treatment effects for subgroups. There are, however, further opportunities for discussion in several areas.
i) Considerations and guidance in categorizing subgroups into biologically plausible versus hypothesis generating for a development program. Does the known prognostic value of the subgroup factor alone necessarily determine that it needs to be assessed as a biologically plausible subgroup?
ii) The need for multiplicity control or statistical presentations that put the observed subgroup results in the context of possible chance findings for biologically plausible subgroup factors. Should this type of subgroup analysis be considered as signal generation, and thus largely negate the necessity of type I error consideration?
iii) The consideration of relative treatment effect versus absolute treatment effect in the assessment of prognostic subgroups, related to the scale of the estimand in question. Is heterogeneous treatment effect in one scale potentially just a representation of homogeneous treatment effect in another scale?
iv) The utility of pre-specified supportive [5] or consistency [6] criterion in regulatory review for those subgroups that fall into the biologically plausible category.
v) What is the role of novel individualized treatment regime and subgroup identification methods in the hypothesis generating subgroup analysis?
The above list is not meant to be exhaustive, but hopefully will help stimulate the discussions and help improve understanding on this crucial topic.
References: [1] Robert Hemmings (2014) An Overview of Statistical and Regulatory Issues in the Planning, Analysis, and Interpretation of Subgroup Analyses in Confirmatory Clinical Trials, Journal of Biopharmaceutical Statistics, 24:1, 4-18.
[2] Brookes, S. T., Whitely, E., Egger, M., Smith, G. D., Mulheran, P. A., & Peters, T. J. (2004). Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test. Journal of clinical epidemiology, 57(3), 229-236.
[3] EMA. Guidance on the investigation of subgroups in confirmatory clinical trials. European Medicines Agency/Committee for Medicinal Products for Human Use; 2019.
[4] Dane, A., Spencer, A., Rosenkranz, G., Lipkovich, I., Parke, T., & PSI/EFSPI Working Group on Subgroup Analysis. (2019). Subgroup analysis and interpretation for phase 3 confirmatory trials: White paper of the EFSPI/PSI working group on subgroup analysis. Pharmaceutical statistics, 18(2), 126-139.
[5] Koch, G. G., & Schwartz, T. A. (2014). An overview of statistical planning to address subgroups in confirmatory clinical trials. Journal of biopharmaceutical statistics, 24(1), 72-93.
[6] Alosh, M., & Huque, M. F. (2013). Multiplicity considerations for subgroup analysis subject to consistency constraint. Biometrical Journal, 55(3), 444-462.
Rare Disease While the statutory substantial evidence requirement for adequate and well controlled investigations for rare diseases is the same as those of common diseases, “Small populations often restrict study design and replication and use of usual inferential statistics”, as noted by Moscicki in his FDA/CDER 2016 update for rare diseases.
There are many cases where it is infeasible to enroll an adequate number of patients for traditional randomized controlled trials. This is further exacerbated with the advance of individualized therapies, such as antisense oligonucleotide (ASO) therapy and N-of-1 trial designs. Compounding these issues, Moscicki also noted that the natural history of rare diseases is often poorly understood with well-defined and validated endpoints, outcome measures/tools and biomarkers often lacking and that rare diseases tend to be progressive, serious, life-limiting and life-threatening. Adequate natural history studies are critical to bridging these knowledge gaps in the disease pathology, and phenotypic and clinical symptomology heterogeneity both across the patient population and within individual patients over the course of the disease. Natural history studies with genetic testing may provide useful information in disease progression, study population, and development of patient specific endpoints for clinical development of new therapeutics.
To provide clarity on the design and conduct of studies in rare diseases, FDA recently issued two guidance documents [1,2] to meet the requirement of the 21st Century Cures Act which was signed into law on Dec. 13, 2016. The first guidance discusses general considerations for rare disease studies and the second guidance provides more detailed discussion on the design and conduct of natural history studies. The Act also created the Complex Innovative Trial Design (CID) Pilot Meeting to support the goal of facilitating and advancing the use of complex adaptive, Bayesian, and other novel clinical trial designs. This program has received and reviewed novel trial designs for rare disease. The panel discussion will provide a forum for a discussion between regulatory and industry, focusing on the challenges and opportunities in application of innovative design for rare disease.
References: [1] FDA. Rare Diseases: Common Issues in Drug Development Guidance for Industry, January 2019. [2] FDA. Rare Diseases: Natural History Studies for Drug Development, March 2019.