unmconf : an R package for Bayesian regression with unmeasured confounders.
Bayesian methods
Epidemiology
R package
Sensitivity analysis
Unmeasured confounding
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
07 Sep 2024
07 Sep 2024
Historique:
received:
19
02
2024
accepted:
27
08
2024
medline:
8
9
2024
pubmed:
8
9
2024
entrez:
7
9
2024
Statut:
epublish
Résumé
The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.
Identifiants
pubmed: 39244581
doi: 10.1186/s12874-024-02322-2
pii: 10.1186/s12874-024-02322-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
195Informations de copyright
© 2024. The Author(s).
Références
Cochran WG. Controlling bias in observational studies: a review. Cambridge University Press; 2006. pp. 30–58. https://doi.org/10.1017/cbo9780511810725.005 .
Rosenbaum PR, Rubin DB. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score. J Am Stat Assoc. 1984;79(387):516–24. https://doi.org/10.1080/01621459.1984.10478078 .
doi: 10.1080/01621459.1984.10478078
Steenland K. Monte Carlo Sensitivity Analysis and Bayesian Analysis of Smoking as an Unmeasured Confounder in a Study of Silica and Lung Cancer. Am J Epidemiol. 2004;160(4):384–92. https://doi.org/10.1093/aje/kwh211 .
doi: 10.1093/aje/kwh211
pubmed: 15286024
Arah OA. Bias Analysis for Uncontrolled Confounding in the Health Sciences. Annu Rev Public Health. 2017;38:23–38. https://doi.org/10.1146/annurev-publhealth-032315-021644 .
doi: 10.1146/annurev-publhealth-032315-021644
pubmed: 28125388
Fewell Z, Davey Smith G, Sterne JAC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646–55. https://doi.org/10.1093/aje/kwm165 .
doi: 10.1093/aje/kwm165
pubmed: 17615092
Groenwold RHH, Sterne JAC, Lawlor DA, Moons KGM, Hoes AW, Tilling K. Sensitivity analysis for the effects of multiple unmeasured confounders. Ann Epidemiol. 2016;26(9):605–11. https://doi.org/10.1016/j.annepidem.2016.07.009 .
doi: 10.1016/j.annepidem.2016.07.009
pubmed: 27576907
Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15(5):291–303. https://doi.org/10.1002/pds.1200 .
doi: 10.1002/pds.1200
pubmed: 16447304
Uddin MJ, Groenwold RHH, Ali MS, de Boer A, Roes KCB, Chowdhury MAB, et al. Methods to control for unmeasured confounding in pharmacoepidemiology: an overview. Int J Clin Pharm. 2016. https://doi.org/10.1007/s11096-016-0299-0 .
doi: 10.1007/s11096-016-0299-0
pubmed: 27091131
Greenland S. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. Int J Epidemiol. 2009;38(6):1662–1673. https://doi.org/10.1093/ije/dyp278 .
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43(6):1969–85. https://doi.org/10.1093/ije/dyu149 .
doi: 10.1093/ije/dyu149
pubmed: 25080530
McCandless LC, Gustafson P, Levy A. Bayesian sensitivity analysis for unmeasured confounding in observational studies. Stat Med. 2007;26(11):2331–47. https://doi.org/10.1002/sim.2711 .
doi: 10.1002/sim.2711
pubmed: 16998821
Gustafson P, McCandless LC, Levy AR, Richardson S. Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders. Biometrics. 2010;66(4):1129–37. https://doi.org/10.1111/j.1541-0420.2009.01377.x .
doi: 10.1111/j.1541-0420.2009.01377.x
pubmed: 20070294
Kawabata E, Tilling K, Groenwold R, Hughes R. Quantitative bias analysis in practice: Review of software for regression with unmeasured confounding. 2022. https://doi.org/10.1101/2022.02.15.22270975 .
doi: 10.1101/2022.02.15.22270975
Carnegie NB, Harada M, Hill JL. Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder. J Res Educ Eff. 2016;9(3):395–420. https://doi.org/10.1080/19345747.2015.1078862 .
doi: 10.1080/19345747.2015.1078862
Dorie V, Harada M, Carnegie NB, Hill J. A flexible, interpretable framework for assessing sensitivity to unmeasured confounding. Stat Med. 2016;35(20):3453–70. https://doi.org/10.1002/sim.6973 .
doi: 10.1002/sim.6973
pubmed: 27139250
pmcid: 5084780
Blackwell, M. A Selection Bias Approach to Sensitivity Analysis for Causal Effects. Political Analysis. Cambridge University Press; 2014:22(2):169–82. https://doi.org/10.1093/pan/mpt006 . Accessed 17 Jan 2024.
Cinelli C, Ferwerda J, Hazlett C. Sensemakr: Sensitivity Analysis Tools for OLS in R and Stata. SSRN Electron J. 2020. https://doi.org/10.2139/ssrn.3588978 .
doi: 10.2139/ssrn.3588978
VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017;167(4):268. https://doi.org/10.7326/m16-2607 .
doi: 10.7326/m16-2607
pubmed: 28693043
Xu R, Frank KA, Maroulis SJ, Rosenberg JM. konfound: Command to quantify robustness of causal inferences. Stata J Promot Commun Stat Stata. 2019;19(3):523–50. https://doi.org/10.1177/1536867x19874223 .
doi: 10.1177/1536867x19874223
Fox MP, MacLehose RF, Lash TL. Best Practices for Quantitative Bias Analysis. In: Applying Quantitative Bias Analysis to Epidemiologic Data. Cham: Springer International Publishing; 2021. pp. 441–452. Series Title: Statistics for Biology and Health. https://doi.org/10.1007/978-3-030-82673-4_13 .
Fox MP, MacLehose RF, Lash TL. SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders. Int J Epidemiol. 2023. https://doi.org/10.1093/ije/dyad053 .
doi: 10.1093/ije/dyad053
pubmed: 37141446
pmcid: 10555728
Faries D, Peng X, Pawaskar M, Price K, Stamey JD, Seaman JW. Evaluating the Impact of Unmeasured Confounding with Internal Validation Data: An Example Cost Evaluation in Type 2 Diabetes. Value Health. 2013;16(2):259–66. https://doi.org/10.1016/j.jval.2012.10.012 .
doi: 10.1016/j.jval.2012.10.012
pubmed: 23538177
Stamey JD, Beavers DP, Faries D, Price KL, Seaman JW. Bayesian modeling of cost-effectiveness studies with unmeasured confounding: a simulation study. Pharm Stat. 2013;13(1):94–100. https://doi.org/10.1002/pst.1604 .
doi: 10.1002/pst.1604
pubmed: 24446072
Lin DY, Psaty BM, Kronmal RA. Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies. Biometrics. 1998;54(3):948. https://doi.org/10.2307/2533848 .
doi: 10.2307/2533848
pubmed: 9750244
Zhang X, Faries DE, Boytsov N, Stamey JD, Seaman JW. A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis. Pharmacoepidemiol Drug Saf. 2016;25(9):982–92. https://doi.org/10.1002/pds.4053 .
doi: 10.1002/pds.4053
pubmed: 27396534
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis. Chapman and Hall/CRC; 2013. https://doi.org/10.1201/b16018 .
Bedrick EJ, Christensen R, Johnson W. A New Perspective on Priors for Generalized Linear Models. J Am Stat Assoc. 1996;91(436):1450–60. https://doi.org/10.1080/01621459.1996.10476713 .
doi: 10.1080/01621459.1996.10476713
Christensen R, Johnson W, Branscum A, Hanson TE. Bayesian Ideas and Data Analysis. Boca Raton: CRC Press; 2010. https://doi.org/10.1201/9781439894798 .
Mood C. Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It. Eur Sociol Rev. 2010;26(1):67–82. https://doi.org/10.1093/esr/jcp006 .
doi: 10.1093/esr/jcp006
Schuster NA, Twisk JWR, Ter Riet G, Heymans MW, Rijnhart JJM. Noncollapsibility and its role in quantifying confounding bias in logistic regression. BMC Med Res Methodol. 2021;21(1):136. https://doi.org/10.1186/s12874-021-01316-8 .
doi: 10.1186/s12874-021-01316-8
pubmed: 34225653
pmcid: 8259440
Pang M, Kaufman JS, Platt RW. Studying noncollapsibility of the odds ratio with marginal structural and logistic regression models. Stat Methods Med Res. 2016;25(5):1925–37. https://doi.org/10.1177/0962280213505804 .
doi: 10.1177/0962280213505804
pubmed: 24108272
Janes H, Dominici F, Zeger S. On quantifying the magnitude of confounding. Biostatistics. 2010;11(3):572–82. https://doi.org/10.1093/biostatistics/kxq007 .
doi: 10.1093/biostatistics/kxq007
pubmed: 20203259
pmcid: 2883302