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Schoenen S, Verbeeck J, Koletzko L, Brambilla I, Kuchenbuch M, Dirani M, Zimmermann G, Dette H, Hilgers RD, Molenberghs G, Nabbout R. Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations. Orphanet J Rare Dis 2024; 19:96. [PMID: 38431612 PMCID: PMC10909280 DOI: 10.1186/s13023-024-03103-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 02/23/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches. METHODS In very small populations, methodological challenges exacerbate. iSTORE's ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence. RESULTS The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias. CONCLUSION Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.
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Affiliation(s)
- Stefanie Schoenen
- Institute of Medical Statistics, RWTH Aachen University, Pauwelsstrasse 19, 52074, Aachen, Germany
| | - Johan Verbeeck
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
| | - Lukas Koletzko
- Institute of Statistics, Ruhr-University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
| | - Isabella Brambilla
- Dravet Italia Onlus - European Patient Advocacy Group (ePAG) EpiCARE, 37100, Verona, Italy
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, Research Center for Pediatric Epilepsies, University of Verona, Via S. Francesco, 22, 37129, Verona, Italy
| | - Mathieu Kuchenbuch
- Institut des Maladies Gènètiques Imagine-Necker Enfants malades Hospital, 24 Boulevard du Montparnasse, 75015, Paris, France
- Necker Enfants malades Hospital, 149 Rue de Sèvre, 75015, Paris, France
| | - Maya Dirani
- Institut des Maladies Gènètiques Imagine-Necker Enfants malades Hospital, 24 Boulevard du Montparnasse, 75015, Paris, France
- Necker Enfants malades Hospital, 149 Rue de Sèvre, 75015, Paris, France
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, 5020, Salzburg, Austria
| | - Holger Dette
- Institute of Statistics, Ruhr-University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
| | - Ralf-Dieter Hilgers
- Institute of Medical Statistics, RWTH Aachen University, Pauwelsstrasse 19, 52074, Aachen, Germany.
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- I-BioStat, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
| | - Rima Nabbout
- Institut des Maladies Gènètiques Imagine-Necker Enfants malades Hospital, 24 Boulevard du Montparnasse, 75015, Paris, France
- Necker Enfants malades Hospital, 149 Rue de Sèvre, 75015, Paris, France
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Geroldinger M, Verbeeck J, Hooker AC, Thiel KE, Molenberghs G, Nyberg J, Bauer J, Laimer M, Wally V, Bathke AC, Zimmermann G. Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. Orphanet J Rare Dis 2023; 18:391. [PMID: 38115074 PMCID: PMC10729462 DOI: 10.1186/s13023-023-02990-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/19/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.
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Affiliation(s)
- Martin Geroldinger
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria.
- Department of Neurology, Christian Doppler Medical Centre, Full Member of European Reference Network on Rare and Complex Epilepsies EpiCARE, Paracelsus Medical University, Ignaz-Harrer Straße 79, Salzburg, 5020, Austria.
| | - Johan Verbeeck
- I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
| | - Andrew C Hooker
- Department of Pharmacy, Uppsala University, 751 24, Uppsala, Sweden
| | - Konstantin E Thiel
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria
| | - Geert Molenberghs
- I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
- I-BioStat, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
| | - Joakim Nyberg
- Department of Pharmacy, Uppsala University, 751 24, Uppsala, Sweden
| | - Johann Bauer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, 5020, Austria
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Martin Laimer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, 5020, Austria
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Verena Wally
- EB House Austria, Research Program for Molecular Therapy of Genodermatoses, Department of Dermatology and Allergology, University Hospital of the Paracelsus Medical University Salzburg, Salzburg, 5020, Austria
| | - Arne C Bathke
- Intelligent Data Analytics (IDA) Lab Salzburg, Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, 5020, Austria
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Strubergasse 21, Salzburg, 5020, Austria
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Verbeeck J, Geroldinger M, Thiel K, Hooker AC, Ueckert S, Karlsson M, Bathke AC, Bauer JW, Molenberghs G, Zimmermann G. How to analyze continuous and discrete repeated measures in small-sample cross-over trials? Biometrics 2023; 79:3998-4011. [PMID: 37587671 DOI: 10.1111/biom.13920] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/26/2023] [Indexed: 08/18/2023]
Abstract
To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.
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Affiliation(s)
- Johan Verbeeck
- Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
| | - Martin Geroldinger
- Team Biostatistics and Big Medical Data, Intelligent Data Analytics (IDA) Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Research and Innovation Management, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Konstantin Thiel
- Team Biostatistics and Big Medical Data, Intelligent Data Analytics (IDA) Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Research and Innovation Management, Paracelsus Medical University Salzburg, Salzburg, Austria
| | | | | | - Mats Karlsson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Arne Cornelius Bathke
- Intelligent Data Analytics (IDA) Lab Salzburg, Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, Austria
| | - Johann Wolfgang Bauer
- Department of Dermatology and Allergology, Paracelsus Medical University, Salzburg, Austria
| | - Geert Molenberghs
- Data Science Institute (DSI), Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Belgium
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), KULeuven, Leuven, Belgium
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, Intelligent Data Analytics (IDA) Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Research and Innovation Management, Paracelsus Medical University Salzburg, Salzburg, Austria
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