Evolution of treatment regimens in multiple myeloma: a social network analysis.

<h4>Background</h4>Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple...

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Autores principales:	Helen Mahony, Athanasios Tsalatsanis, Ambuj Kumar, Benjamin Djulbegovic
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/826ce55a8e96412699a28b0f1c65a588
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spelling	oai:doaj.org-article:826ce55a8e96412699a28b0f1c65a5882021-11-25T06:04:56ZEvolution of treatment regimens in multiple myeloma: a social network analysis.1932-620310.1371/journal.pone.0104555https://doaj.org/article/826ce55a8e96412699a28b0f1c65a5882014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25119186/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.<h4>Methods</h4>We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.<h4>Results</h4>135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four "steps" within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.<h4>Conclusion</h4>Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.Helen MahonyAthanasios TsalatsanisAmbuj KumarBenjamin DjulbegovicPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e104555 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Helen Mahony Athanasios Tsalatsanis Ambuj Kumar Benjamin Djulbegovic Evolution of treatment regimens in multiple myeloma: a social network analysis.
description	<h4>Background</h4>Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.<h4>Methods</h4>We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.<h4>Results</h4>135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four "steps" within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.<h4>Conclusion</h4>Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.
format	article
author	Helen Mahony Athanasios Tsalatsanis Ambuj Kumar Benjamin Djulbegovic
author_facet	Helen Mahony Athanasios Tsalatsanis Ambuj Kumar Benjamin Djulbegovic
author_sort	Helen Mahony
title	Evolution of treatment regimens in multiple myeloma: a social network analysis.
title_short	Evolution of treatment regimens in multiple myeloma: a social network analysis.
title_full	Evolution of treatment regimens in multiple myeloma: a social network analysis.
title_fullStr	Evolution of treatment regimens in multiple myeloma: a social network analysis.
title_full_unstemmed	Evolution of treatment regimens in multiple myeloma: a social network analysis.
title_sort	evolution of treatment regimens in multiple myeloma: a social network analysis.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/826ce55a8e96412699a28b0f1c65a588
work_keys_str_mv	AT helenmahony evolutionoftreatmentregimensinmultiplemyelomaasocialnetworkanalysis AT athanasiostsalatsanis evolutionoftreatmentregimensinmultiplemyelomaasocialnetworkanalysis AT ambujkumar evolutionoftreatmentregimensinmultiplemyelomaasocialnetworkanalysis AT benjamindjulbegovic evolutionoftreatmentregimensinmultiplemyelomaasocialnetworkanalysis
_version_	1718414232148508672

Evolution of treatment regimens in multiple myeloma: a social network analysis.

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