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Ma J, Johnson EA, McCrory B. Predicting risk factors for pediatric mortality in clinical trial research: A retrospective, cross-sectional study using a Healthcare Cost and Utilization Project database. J Clin Transl Sci 2023; 7:e211. [PMID: 37900356 PMCID: PMC10603364 DOI: 10.1017/cts.2023.634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Incorporating real-world data using "big data" analysis in healthcare are useful to extract specific information for healthcare delivery system improvement. All-cause mortality is an essential measure to enhance patient safety in clinical trial research, especially for underrepresented pediatric participants. Objective This study aimed to determine the associations between pediatric mortality and patient-specific factors using the Healthcare Cost and Utilization Project (HCUP) database. Methods Data from the 2019 the HCUP Kids' Inpatient Database (KID) were used to conduct a logistic regression analysis to determine associations between pediatric patients' the chance of survival and their demographic and socioeconomic background, discharge records, and hospital information. Results Total number of diagnoses (OR = 0.84), total number of procedures (OR = 0.86), length of stay (OR = 1.04), age intervals greater than 1 year (OR > 1.0), transfer into the hospital from a different acute care (OR = 0.34), major diagnoses of multiple significant trauma (OR = 0.03) or hepatobiliary system and pancreas (OR = 0.10), region of hospital - west and midwest (OR > 1.0), and medium or larger hospital bed size (OR > 1.0) were all significantly associated with the chance of survival for patients participating in pediatric clinical trials (p < 0.05). Conclusion Real-world clinical trial data analysis showed the potential improvement area including reallocating trial resources to promote trial quality and safe participation for pediatric patients. Pediatric trials need tools that are developed using user-centered design approaches to satisfy the unique needs and requirements of pediatric patients and their caregivers. Safe intrahospital transfer procedures and active dissemination of successful trial best practices are crucial to trial management, adherence, quality, and safety.
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Affiliation(s)
- Jiahui Ma
- Montana State University, Norm Asbjornson College of Engineering, Bozeman, MT, USA
| | - Elizabeth A. Johnson
- Montana State University, Mark & Robyn Jones College of Nursing, Bozeman, MT, USA
| | - Bernadette McCrory
- Montana State University, Norm Asbjornson College of Engineering, Bozeman, MT, USA
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Quintana M, Saville BR, Vestrucci M, Detry MA, Chibnik L, Shefner J, Berry JD, Chase M, Andrews J, Sherman AV, Yu H, Drake K, Cudkowicz M, Paganoni S, Macklin EA. Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis. Ann Neurol 2023; 94:547-560. [PMID: 37245090 DOI: 10.1002/ana.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547-560.
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Affiliation(s)
| | - Benjamin R Saville
- Berry Consultants, Austin, Texas, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Lori Chibnik
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy Shefner
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James D Berry
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Marianne Chase
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jinsy Andrews
- Neurological Institute of New York, Columbia University, New York, New York, USA
| | - Alexander V Sherman
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong Yu
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kristin Drake
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Merit Cudkowicz
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabrina Paganoni
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Eric A Macklin
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
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Minichmayr IK, Ravenstijn P, van der Graaf PH, Vamvakas S. Therapeutic Innovations in Neuroscience: What's New on the Horizon? Clin Pharmacol Ther 2022; 111:715-717. [PMID: 35313013 DOI: 10.1002/cpt.2547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 01/28/2022] [Indexed: 11/11/2022]
Affiliation(s)
| | | | - Piet H van der Graaf
- Certara, Canterbury, UK.,Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Spiros Vamvakas
- Human Division, European Medicines Agency, Amsterdam, The Netherlands
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