Hypothesis-driven mediation analysis for compositional data: an application to gut microbiome

References

• Gloor GB, Macklaim JM, Pawlowsky-Glahn V, et al. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8(NOV):1–6.
   PubMedGoogle Scholar
• Silverman JD, Washburne AD, Mukherjee S, et al. A phylogenetic transform enhances analysis of compositional microbiota data. eLife. 2017;6:1–20. doi: 10.7554/eLife.21887
   Web of Science ®Google Scholar
• Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. Modeling and analysis of compositional data. Chichester: John Wiley & Sons, Ltd; 2015.
   Google Scholar
• Pawlowsky-Glahn V, Buccianti A. Compositional data analysis – theory and applications; 2011.
   Google Scholar
• Graffelman J. “A parametric test for HWE based on isometric logratio coordi- nates”; 2011. p. 1–5.
   Google Scholar
• Gordon-Rodriguez E, Quinn TP, Cunningham JP. “Learning sparse log-ratios for high-throughput sequencing data,” bioRxiv, p. 2021.02.11.430695; 2021.
   Google Scholar
• Zhang H, Chen J, Li Z, et al. Testing for mediation effect with application to human microbiome data. Stat Biosci. 2021;13(2):313–328. doi: 10.1007/s12561-019-09253-3.
   PubMed Web of Science ®Google Scholar
• Pearl J. Interpretation and identification of causal mediation. Psychol Methods. 2014;19(4):459–481. doi: 10.1037/a0036434
   PubMed Web of Science ®Google Scholar
• VanderWeele TJ. Mediation analysis: a Practitioner's guide. Annu Rev Public Health. 2016;37(1):17–32. doi: 10.1146/publhealth.2016.37.issue-1
   PubMedGoogle Scholar
• Sohn MB, Li H. Compositional mediation analysis for microbiome studies. Ann Appl Stat. 2019;13(1):661–681. doi: 10.1214/18-AOAS1210
   Web of Science ®Google Scholar
• Wang C, Hu J, Blaser MJ, et al. Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data. Bioinformatics. 2020;36(2):347–355. doi: 10.1093/bioinformatics/btz565
   PubMed Web of Science ®Google Scholar
• Fu J, Koslovsky M, Neophytou A, et al. A Bayesian joint model for compositional mediation effect selection in microbiome data. Stat Med. 2023;42(17):2999–3015. doi: 10.1002/sim.v42.17
   PubMed Web of Science ®Google Scholar
• Blum MG, Valeri L, François O, et al. Challenges raised by mediation analysis in a high-dimension setting. Environ Health Perspect. 2020;128(5):1–8. doi: 10.1289/EHP6240
   Web of Science ®Google Scholar
• VanderWeele TJ, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Method. 2014;2(1):95–115. doi: 10.1515/em-2012-0010
   PubMedGoogle Scholar
• Daniel R, De Stavola B, Cousens S, et al. Causal mediation analysis with multiple mediators. Biometrics. 2015;71(1):1:14. doi: 10.1111/biom.12248
   PubMed Web of Science ®Google Scholar
• Imai K, Yamamoto T. Identification and sensitivity analysis for multiple causal mechanisms: revisiting evidence from framing experiments. Polit Anal. 2013;21(2):141–161. doi: 10.1093/pan/mps040
   Web of Science ®Google Scholar
• Wang W, Nelson S, Albert JM. Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula. Stat Med. 2013;32(24):4211–4228. doi: 10.1002/sim.v32.24
   PubMed Web of Science ®Google Scholar
• Kim C, Daniels M, Hogan J, et al. Bayesian methods for multiple mediators: relating principal stratification and causal mediation in the analysis of power plant emission controls. Ann Appl Stat. 2019;13(3):1927–1956. doi: 10.1214/19-AOAS1260
   PubMed Web of Science ®Google Scholar
• Taguri M, Featherstone J, Cheng J. Causal mediation analysis with multiple causally non-ordered mediators. Stat Methods Med Res. 2018;27(1):3–19. doi: 10.1177/0962280215615899
   PubMed Web of Science ®Google Scholar
• Filzmoser TMP, Hron K. Applied compositional data analysis. Cham, Switzerland: Springer Nature Switzerland AG; 2018.
   Google Scholar
• Barabesi L, Sartorelli P. Compositional data analysis; 2020.
   Google Scholar
• Pearl J. Direct and indirect effects. Proc Seventeenth Conf Uncertainy Artif Intell. 2001;411–20.
   Google Scholar
• Barber TM, Kabisch S, Pfeiffer AF, et al. The health benefits of dietary fibre. Nutrients. 2020;12(10):1–17. doi: 10.3390/nu12103209
   Web of Science ®Google Scholar
• Gurung M, Li Z, You H, et al. “Role of gut microbiota in type 2 diabetes pathophysiology”; 2020.
   Google Scholar
• Chambers ES, Preston T, Frost G, et al. Role of gut microbiota-generated short-chain fatty acids in metabolic and cardiovascular health. Curr Nutr Rep. 2018;7(4):198–206. doi: 10.1007/s13668-018-0248-8
   PubMedGoogle Scholar
• Pahkala K, Laitinen TT, Niinikoski H, et al. Effects of 20-year infancy-onset dietary counselling on cardiometabolic risk factors in the special Turku coronary risk factor intervention project (STRIP): 6-year post-intervention follow-up. Lancet Child Adolesc Health. 2020;4(5):359–369. doi: 10.1016/S2352-4642(20)30059-6
   PubMedGoogle Scholar
• Ronnemaa T, Lagstrom H, Jula A, et al. Cohort profile: the STRIP study (special Turku coronary risk factor intervention project), an infancy-onset dietary and life-style intervention trial. Int J Epidemiol. 2008;38(3):650–655.
   PubMed Web of Science ®Google Scholar
• Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: high-resolution sample inference from illumina amplicon data. Nat Methods. 2016;13(7):581–583. doi: 10.1038/nmeth.3869
   PubMed Web of Science ®Google Scholar
• Hakala P, Marniemi J, Knuts LR, et al. Calculated vs analysed nutrient composition of weight reduction diets. Food Chem. 1996;57(1):71–75. doi: 10.1016/0308-8146(96)00077-5
   Web of Science ®Google Scholar
• Ravitsemusneuvottelukunta V. Terveyttä ruoasta – Suomalaiset ravitsemussuositukset 2014 (Finnish Nutrition Recommendations 2014) Valtion ravitsemusneuvottelukunta: Tampere; 2014.
   Google Scholar
• VanderWeele T. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010;21(4):540–51. doi: 10.1097/EDE.0b013e3181df191c
   PubMed Web of Science ®Google Scholar
• VanderWeele T, Arah O. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology. 2011;22(1):42–52. doi: 10.1097/EDE.0b013e3181f74493
   PubMed Web of Science ®Google Scholar
• Hombach KMS. Non-coding RNAs: classification, biology and functioning. Adv Exp Med Biol. 2016;937:3–17. doi: 10.1007/978-3-319-42059-2
   PubMedGoogle Scholar