1
|
Dyson G. An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials. INTERNATIONAL JOURNAL OF CLINICAL BIOSTATISTICS AND BIOMETRICS 2021; 7. [PMID: 34632463 PMCID: PMC8496893 DOI: 10.23937/2469-5831/1510038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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.
Collapse
Affiliation(s)
- Greg Dyson
- Department of Oncology, Karmanos Cancer Institute, Wayne State University, Detroit MI, USA
| |
Collapse
|
2
|
Sameem M, Rani A, Arshad M. Association of rs146292819 Polymorphism in ABCA1 Gene with the Risk of Coronary Artery Disease in Pakistani Population. Biochem Genet 2019; 57:623-637. [DOI: 10.1007/s10528-019-09915-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 03/27/2019] [Indexed: 11/30/2022]
|
3
|
Tan PL, Garrett ME, Willer JR, Campochiaro PA, Campochiaro B, Zack DJ, Ashley-Koch AE, Katsanis N. Systematic Functional Testing of Rare Variants: Contributions of CFI to Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci 2017; 58:1570-1576. [PMID: 28282489 PMCID: PMC6022411 DOI: 10.1167/iovs.16-20867] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Purpose Genome-wide association (GWAS) and sequencing studies for AMD have highlighted the importance of coding variants at loci that encode components of the complement pathway. However, assessing the contribution of such alleles to AMD, especially when they are rare, remains coarse, in part because of the persistent challenge in establishing their functional relevance. Others and we have shown previously that rare alleles in complement factor I (CFI) can be tested functionally using a surrogate in vivo assay of retinal vascularization in zebrafish embryos. Here, we have implemented and scaled these tools to assess the overall contribution of rare alleles in CFI to AMD. Methods We performed targeted sequencing of CFI in 731 AMD patients, followed by replication in a second patient cohort of 511 older healthy individuals. Systematic functional testing of all alleles and post-hoc statistical analysis of functional variants was also performed. Results We discovered 20 rare coding nonsynonymous variants, including the previously reported G119R allele. In vivo testing led to the identification of nine variants that alter CFI; six of which are associated with hypoactive complement factor I (FI). Post-hoc analysis in ethnically matched, population controls showed six of these to be present exclusively in cases. Conclusions Taken together, our data argue that multiple rare and ultra-rare alleles in CFI contribute to AMD pathogenesis; they improve the precision of the assessment of the contribution of CFI to AMD; and they offer a rational route to establishing both causality and direction of allele effect for genes associated with this disorder.
Collapse
Affiliation(s)
- Perciliz L Tan
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, United States 2Department of Cell Biology, Duke University Medical Center, Durham, North Carolina, United States
| | - Melanie E Garrett
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, United States
| | - Jason R Willer
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, United States
| | - Peter A Campochiaro
- Departments of Ophthalmology, Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
| | - Betsy Campochiaro
- Departments of Ophthalmology, Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
| | - Donald J Zack
- Departments of Ophthalmology, Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, United States 4Center for Stem Cells and Ocular Regenerative Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States 5Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
| | - Allison E Ashley-Koch
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, United States 6Departments of Medicine, Molecular Genetics and Microbiology, Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, United States 2Department of Cell Biology, Duke University Medical Center, Durham, North Carolina, United States
| |
Collapse
|