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We conducted a random-effects Bayesian network meta-analysis (NMA) of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Progression-free survival (PFS) was compared between 11 modern regimens with different immunochemotherapy and maintenance strategies. G-Benda-G resulted in with the best PFS, with an HR of 0.41 compared to R-Benda, a surface under the cumulative ranking curve (SUCRA) of 0.97, a probability of being the best treatment (PbBT) of 72%, and a posterior ranking distribution (PoRa) of 1 (95% BCI 1\u20133). This was followed by R-Benda-R4 (HR\u2009=\u20090.49, PbBT\u2009=\u200925%, PoRa\u2009=\u20092) and R-Benda-R (HR\u2009=\u20090.60, PbBT\u2009=\u20093%, PoRa\u2009=\u20093). R-CHOP-R (HR\u2009=\u20090.96) and R-Len-R (HR\u2009=\u20090.97) had similar efficacy to R-Benda. Bendamustine was a better chemotherapy backbone than CHOP either with maintenance (R-Benda-R vs R-CHOP-R, HR\u2009=\u20090.62; G-Benda-G vs G-CHOP-G, HR\u2009=\u20090.55) or without maintenance therapy (R-Benda vs R-CHOP, HR\u2009=\u20090.68). Rituximab maintenance improved PFS following R-CHOP (R-CHOP-R vs R-CHOP, HR\u2009=\u20090.65) or R-Benda (R-Benda-R vs R-Benda, HR\u2009=\u20090.60; R-Benda-R4 vs R-Benda, HR\u2009=\u20090.49). In the absence of multi-arm RCTs that include all common regimens, this NMA provides an important and useful guide to inform treatment decisions.<\/jats:p>","DOI":"10.1038\/s41408-021-00598-x","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T12:03:26Z","timestamp":1641384206000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials"],"prefix":"10.1038","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-8341","authenticated-orcid":false,"given":"Yucai","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouhao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1867-0526","authenticated-orcid":false,"given":"Matthew J.","family":"Maurer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas M.","family":"Habermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4215-6500","authenticated-orcid":false,"given":"Thomas E.","family":"Witzig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9748-5486","authenticated-orcid":false,"given":"Michael L.","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-9393","authenticated-orcid":false,"given":"Grzegorz S.","family":"Nowakowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"598_CR1","doi-asserted-by":"publisher","first-page":"443","DOI":"10.3322\/caac.21357","volume":"66","author":"LR Teras","year":"2016","unstructured":"Teras LR, DeSantis CE, Cerhan JR, Morton LM, Jemal A, Flowers CR. 2016 US lymphoid malignancy statistics by World Health Organization subtypes. 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XQ currently is a principal biostatistician at Incyte. MJM: Research funding: BMS, Genentech, Nanostring; Advisory board: MorphoSys, Kite Pharma, Pfizer. TMH: Funding (to institution): Advisory Board (compensation to institution): Lilly, MorphoSys, Incyte; Data Monitoring Committee (compensation to institution): Seagen, Tess Therapeutics. TEW: Research funding: Karyopharm Therapeutics, Abbvie\/Genentech, Seattle Genetics, Celgene\/BMS, Incyte, Epizyme, Cellectar, Tessa Therapeutics, Portola Pharmaceuticals, MorphoSys, ADC Therapeutics; Consultansy or Advisory board: Celgene\/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics. MLW: Research Funding: Acerta Pharma, AstraZeneca, BeiGene, BioInvent, Celgene, Innocare, Janssen, Juno, Kite Pharma, Lilly, Loxo Oncology, Molecular Templates, Oncternal, Pharmacyclics, VelosBio; Consultancy or Advisory board: AstraZeneca, Bayer, BeiGene, CSTone, DTRM Biopharma, Epizyme, Genentech, InnoCare, Janssen, Juno, Kite Pharma, Loxo Oncology, Miltenyi Biomedicine GmbH, Oncternal, Pharmacyclics, VelosBio; Honoraria: Acerta Pharma, AstraZeneca, BeiGene, Epizyme, Janssen, Kite Pharma, Miltenyi Biomedicine GmbH, Newbridge Pharmaceuticals, OMI. GSN: Research funding: Celgene, NanoString Technologies, MorphoSys; Consultansy or Advisory board: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite\/Gilead.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}