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Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders

In the United States, approximately 7000 rare diseases affect 30 million patients, and only 10% of these diseases have existing therapies. Sound study design and causal inference methods are essential to demonstrate the therapeutic efficacy, safety, and effectiveness of new therapies. In the rare di...

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Autores principales: Izem, Rima, McCarter, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609847/
https://www.ncbi.nlm.nih.gov/pubmed/34814939
http://dx.doi.org/10.1186/s13023-021-02124-5
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author Izem, Rima
McCarter, Robert
author_facet Izem, Rima
McCarter, Robert
author_sort Izem, Rima
collection PubMed
description In the United States, approximately 7000 rare diseases affect 30 million patients, and only 10% of these diseases have existing therapies. Sound study design and causal inference methods are essential to demonstrate the therapeutic efficacy, safety, and effectiveness of new therapies. In the rare diseases setting, several factors challenge the use of typical parallel control designs: the small patient population size, genotypic and phenotypic diversity, and the complexity and incomplete understanding of the disorder’s progression. Repeated measures, when spaced appropriately relative to disease progression and exploited in design and analysis, can increase study power and reduce variability in treatment effect estimation. This paper reviews these longitudinal designs and draws the parallel between some new and existing randomized studies in rare diseases and their less well-known controlled observational study designs. We show that self-controlled randomized crossover and N-of-1 designs have similar considerations as the observational case series and case-crossover designs. Also, randomized sequential designs have similar considerations to longitudinal cohort studies using sequential matching or weighting to control confounding. We discuss design and analysis considerations for valid causal inference and illustrate them with examples of analyses in multiple rare disorders, including urea cycle disorder and cystic fibrosis.
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spelling pubmed-86098472021-11-29 Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders Izem, Rima McCarter, Robert Orphanet J Rare Dis Review In the United States, approximately 7000 rare diseases affect 30 million patients, and only 10% of these diseases have existing therapies. Sound study design and causal inference methods are essential to demonstrate the therapeutic efficacy, safety, and effectiveness of new therapies. In the rare diseases setting, several factors challenge the use of typical parallel control designs: the small patient population size, genotypic and phenotypic diversity, and the complexity and incomplete understanding of the disorder’s progression. Repeated measures, when spaced appropriately relative to disease progression and exploited in design and analysis, can increase study power and reduce variability in treatment effect estimation. This paper reviews these longitudinal designs and draws the parallel between some new and existing randomized studies in rare diseases and their less well-known controlled observational study designs. We show that self-controlled randomized crossover and N-of-1 designs have similar considerations as the observational case series and case-crossover designs. Also, randomized sequential designs have similar considerations to longitudinal cohort studies using sequential matching or weighting to control confounding. We discuss design and analysis considerations for valid causal inference and illustrate them with examples of analyses in multiple rare disorders, including urea cycle disorder and cystic fibrosis. BioMed Central 2021-11-23 /pmc/articles/PMC8609847/ /pubmed/34814939 http://dx.doi.org/10.1186/s13023-021-02124-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Izem, Rima
McCarter, Robert
Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title_full Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title_fullStr Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title_full_unstemmed Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title_short Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
title_sort randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609847/
https://www.ncbi.nlm.nih.gov/pubmed/34814939
http://dx.doi.org/10.1186/s13023-021-02124-5
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