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

Abstract 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 th...

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Autores principales:	Rima Izem, Robert McCarter
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/9253ab0790f24de99be7790223c1c8ff
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spelling	oai:doaj.org-article:9253ab0790f24de99be7790223c1c8ff2021-11-28T12:22:39ZRandomized and non-randomized designs for causal inference with longitudinal data in rare disorders10.1186/s13023-021-02124-51750-1172https://doaj.org/article/9253ab0790f24de99be7790223c1c8ff2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13023-021-02124-5https://doaj.org/toc/1750-1172Abstract 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.Rima IzemRobert McCarterBMCarticleMedicineRENOrphanet Journal of Rare Diseases, Vol 16, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Rima Izem Robert McCarter Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
description	Abstract 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.
format	article
author	Rima Izem Robert McCarter
author_facet	Rima Izem Robert McCarter
author_sort	Rima Izem
title	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_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_sort	randomized and non-randomized designs for causal inference with longitudinal data in rare disorders
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9253ab0790f24de99be7790223c1c8ff
work_keys_str_mv	AT rimaizem randomizedandnonrandomizeddesignsforcausalinferencewithlongitudinaldatainraredisorders AT robertmccarter randomizedandnonrandomizeddesignsforcausalinferencewithlongitudinaldatainraredisorders
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Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders

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