1
|
Rahman R, Ventz S, McDunn J, Louv B, Reyes-Rivera I, Polley MYC, Merchant F, Abrey LE, Allen JE, Aguilar LK, Aguilar-Cordova E, Arons D, Tanner K, Bagley S, Khasraw M, Cloughesy T, Wen PY, Alexander BM, Trippa L. Leveraging external data in the design and analysis of clinical trials in neuro-oncology. Lancet Oncol 2021; 22:e456-e465. [PMID: 34592195 PMCID: PMC8893120 DOI: 10.1016/s1470-2045(21)00488-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 01/20/2023]
Abstract
Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.
Collapse
Affiliation(s)
- Rifaquat Rahman
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, MA, USA.
| | - Steffen Ventz
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Jon McDunn
- Project Data Sphere, Morrisville, NC, USA
| | - Bill Louv
- Project Data Sphere, Morrisville, NC, USA
| | | | - Mei-Yin C Polley
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | | | | | | | | | | | - David Arons
- National Brain Tumor Society, Newton, MA, USA
| | - Kirk Tanner
- National Brain Tumor Society, Newton, MA, USA
| | - Stephen Bagley
- Division of Hematology and Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mustafa Khasraw
- Department of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, NC, USA
| | - Timothy Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Brian M Alexander
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, MA, USA; Foundation Medicine, Cambridge, MA, USA
| | - Lorenzo Trippa
- Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T H Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
2
|
Lee UJ, Tzeng S, Chen YC, Chen JJ. Prognostic and predictive signatures for treatment decisions. Biomark Med 2018; 12:849-859. [PMID: 30022678 DOI: 10.2217/bmm-2017-0320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM We develop a subgroup selection procedure using both prognostic and predictive biomarkers to identify four patient subpopulations: low- and high-risk responders, and low- and high-risk nonresponders. METHODS We utilize three regression models to identify three sets of biomarkers: S, prognostic biomarkers; T, predictive biomarkers; and U, prognostic and predictive biomarkers. The prognostic signature C(S) combines with a predictive signature, either C(T) or C(U), to develop two procedures C(S,T) and C(S,U) for identification of four subgroups. RESULTS Simulation experiment showed that proposed models for identifying the biomarker sets S and U performed well, as did the procedure C(S,U) for subgroup identification. CONCLUSION The proposed model provides more comprehensive characterization of patient subpopulations, and better accuracy in patient treatment assignment.
Collapse
Affiliation(s)
- Un Jung Lee
- Division of Biochemical Toxicology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA
| | - ShengLi Tzeng
- Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 11529, Taiwan
| | - Yu-Chuan Chen
- Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72029, USA
| | - James J Chen
- Department of Biostatistics, University of Arkansas for Medical Science, Little Rock, AR 72205, USA
| |
Collapse
|
3
|
Lu TP, Chen JJ. Subgroup identification for treatment selection in biomarker adaptive design. BMC Med Res Methodol 2015; 15:105. [PMID: 26646831 PMCID: PMC4673750 DOI: 10.1186/s12874-015-0098-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 12/01/2015] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects. METHODS The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets. RESULTS The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error. CONCLUSION Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.
Collapse
Affiliation(s)
- Tzu-Pin Lu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. .,Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
| | - James J Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. .,Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.
| |
Collapse
|
4
|
Lu TP, Chen JJ. Identification of drug-induced toxicity biomarkers for treatment determination. Pharm Stat 2015; 14:284-93. [PMID: 25914330 DOI: 10.1002/pst.1684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Revised: 11/18/2014] [Accepted: 03/30/2015] [Indexed: 12/28/2022]
Abstract
Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.
Collapse
Affiliation(s)
- Tzu-Pin Lu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.,Department of Public Health Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - James J Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA
| |
Collapse
|
5
|
Gromo G, Mann J, Fitzgerald JD. Cardiovascular drug discovery: a perspective from a research-based pharmaceutical company. Cold Spring Harb Perspect Med 2014; 4:4/6/a014092. [PMID: 24890831 DOI: 10.1101/cshperspect.a014092] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The theme of this review is to summarize the evolving processes in cardiovascular drug discovery and development within a large pharmaceutical company. Emphasis is placed on the contrast between the academic and industrial research operating environments, which can influence the effectiveness of research collaboration between the two constituencies, but which plays such an important role in drug innovation. The strategic challenges that research directors face are also emphasized. The need for improved therapy in many cardiovascular indications remains high, but the feasibility in making progress, despite the advances in molecular biology and genomics, is also assessed.
Collapse
Affiliation(s)
- G Gromo
- F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland
| | - J Mann
- Translational Medicine, Cardiovascular and Metabolism, F. Hoffmann-La Roche AG, CH-4070 Basel, Switzerland
| | | |
Collapse
|
6
|
Chen JJ, Lin WJ, Chen HC. Pharmacogenomic biomarkers for personalized medicine. Pharmacogenomics 2014; 14:969-80. [PMID: 23746190 DOI: 10.2217/pgs.13.75] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Pharmacogenomics examines how the benefits and adverse effects of a drug vary among patients in a target population by analyzing genomic profiles of individual patients. Personalized medicine prescribes specific therapeutics that best suit an individual patient. Much current research focuses on developing genomic biomarkers to identify patients, to identify which patients would benefit from a treatment, have an adverse response, or no response at all, prior to treatment according to relevant differences in risk factors, disease types and/or responses to therapy. This review describes the use of the two personalized medicine biomarkers, prognostic and predictive, to classify patients into subgroups for treatment recommendation.
Collapse
Affiliation(s)
- James J Chen
- Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US FDA, 3900 NCTR Road, HFT-20, Jefferson, AR 72079, USA.
| | | | | |
Collapse
|
7
|
López V, Triguero I, Carmona CJ, García S, Herrera F. Addressing imbalanced classification with instance generation techniques: IPADE-ID. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.01.050] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|