A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.
Saved in:
| Title: | A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data. |
|---|---|
| Authors: | Pripp AH; Oslo Centre of Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway. apripp@ous-hf.no.; Faculty of Health Science, OsloMet - Oslo Metropolitan University, Oslo, Norway. apripp@ous-hf.no., Łosińska K; Division of Rheumatology and Immunology, University Hospital, Krakow, Poland.; Division of Rheumatology, Department of Internal Medicine, Sørlandet Hospital, Kristiansand, Norway., Korkosz M; Division of Rheumatology and Immunology, University Hospital, Krakow, Poland.; Department of Rheumatology and Immunology, Jagiellonian University Medical College, Krakow, Poland., Haugeberg G; Division of Rheumatology, Department of Internal Medicine, Sørlandet Hospital, Kristiansand, Norway.; Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. |
| Source: | Rheumatology international [Rheumatol Int] 2024 Jul; Vol. 44 (7), pp. 1265-1274. Date of Electronic Publication: 2024 Apr 24. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 8206885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1437-160X (Electronic) Linking ISSN: 01728172 NLM ISO Abbreviation: Rheumatol Int Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Berlin ; New York, NY] : Springer International, c1981- |
| MeSH Terms: | Rheumatic Diseases*/therapy, Observational Studies as Topic/methods ; Humans ; Treatment Outcome ; Propensity Score ; Regression Analysis ; Data Interpretation, Statistical |
| Abstract: | Objective: Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research. Methods: We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods. Results: Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed. Conclusion: The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology. (© 2024. The Author(s).) |
| References: | Bothwell LE, Greene JA, Podolsky SH, Jones DS (2016) Assessing the gold standard – lessons from the history of RCTs. N Engl J Med 374(22):2175–2181. https://doi.org/10.1056/NEJMms1604593. (PMID: 10.1056/NEJMms160459327248626) Michael SP, Ashley Harrison R, Eric MR (2020) The quality of randomized controlled trials in high-impact rheumatology journals, 1998–2018. J Rhuematol 47(9):1446. https://doi.org/10.3899/jrheum.191306. (PMID: 10.3899/jrheum.191306) von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP (2007) Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335(7624):806–808. https://doi.org/10.1136/bmj.39335.541782.AD. (PMID: 10.1136/bmj.39335.541782.AD) Dreyer NA, Bryant A, Velentgas P (2016) The GRACE checklist: a validated assessment tool for high qauality observational studies of comparative effectiveness. J Manag Care Spec Pharm 22(10):1107–1113. https://doi.org/10.18553/jmcp.2016.22.10.1107. (PMID: 10.18553/jmcp.2016.22.10.110727668559) Courvoisier DS, Lauper K, Kedra J, de Wit M, Fautrel B, Frisell T, Hyrich KL, Iannone F, Machado PM, Ornbjerg LM, Rotar Z, Santos MJ, Stamm TA, Stones SR, Strangfeld A, Bergstra SA, Landewe RBM, Finckh A (2022) EULAR points to consider when analysing and reporting comparative effectiveness research using observational data in rheumatology. Ann Rheum Dis 81(6):780–785. https://doi.org/10.1136/annrheumdis-2021-221307. (PMID: 10.1136/annrheumdis-2021-22130735058229) Haugeberg G, Bakland G, Rodevand E, Hansen IJW, Diamantopoulos A, Pripp AH (2023) Effectiveness and persistence in SB4- and reference etanercept-treated rheumatoid arthritis patients in ordinary clinical practice in Norway. Arthritis Care Res (Hoboken). https://doi.org/10.1002/acr.25092. (PMID: 10.1002/acr.2509236722450) Łosińska K, Pripp AH, Bakland G, Fevang BT, Brekke LK, Wierod A, Korkosz M, Haugeberg G (2024) Comparative effectiveness and persistence of SB4 and reference etanercept in patients with psoriatic arthritis in Norway. Arthritis Care Res (Hoboken) Accepted for publication. Hernán MA, Wang W, Leaf DE (2022) Target trial emulation: a framework for causal inference from observational data. JAMA 328(24):2446–2447. https://doi.org/10.1001/jama.2022.21383. (PMID: 10.1001/jama.2022.2138336508210) Cook RJ, Lawless JF (2024) Statistical and scientific considerations concerning the interpretation, replicability, and transportability of research findings. J Rheumatol 51(2):117–129. https://doi.org/10.3899/jrheum.2023-0499. (PMID: 10.3899/jrheum.2023-049937967911) Sturmer T, Wang T, Golightly YM, Keil A, Lund JL, Jonsson Funk M (2020) Methodological considerations when analysing and interpreting real-world data. Rheumatology (Oxford) 59(1):14–25. https://doi.org/10.1093/rheumatology/kez320. (PMID: 10.1093/rheumatology/kez32031834408) Goetghebeur E, le Cessie S, De Stavola B, Moodie EE, Waernbaum I (2020) Formulating causal questions and principled statistical answers. Stat Med 39(30):4922–4948. https://doi.org/10.1002/sim.8741. (PMID: 10.1002/sim.8741329645267756489) Pearl J (1995) Causal diagrams for empirical research. Biometrika 82(4):669–688. https://doi.org/10.1093/biomet/82.4.669. (PMID: 10.1093/biomet/82.4.669) Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11(5):550–560. https://doi.org/10.1097/00001648-200009000-00011. (PMID: 10.1097/00001648-200009000-0001110955408) Smith MJ, Mansournia MA, Maringe C, Zivich PN, Cole SR, Leyrat C, Belot A, Rachet B, Luque-Fernandez MA (2022) Introduction to computational causal inference using reproducible Stata, R, and Python code: a tutorial. Stat Med 41(2):407–432. https://doi.org/10.1002/sim.9234. (PMID: 10.1002/sim.923434713468) Hill AB (1965) The environment and disease: association or causation? Proc R Soc Med 58(5):295–300. https://doi.org/10.1177/003591576505800503. (PMID: 10.1177/003591576505800503142838791898525) Shimonovich M, Pearce A, Thomson H, Keyes K, Katikireddi SV (2021) Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking. Eur J Epidemiol 36(9):873–887. https://doi.org/10.1007/s10654-020-00703-7. (PMID: 10.1007/s10654-020-00703-733324996) Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66(5):688–701. https://doi.org/10.1037/h0037350. (PMID: 10.1037/h0037350) Rubin DB (2005) Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc 100(469):322–331. https://doi.org/10.1198/016214504000001880. (PMID: 10.1198/016214504000001880) Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945–960. https://doi.org/10.1080/01621459.1986.10478354. (PMID: 10.1080/01621459.1986.10478354) Kim H (2019) Propensity score analysis in non-randomized experimental designs: an overview and a tutorial using R software. In: Hein S, Weeland J (eds) Randomized Controlled Trials, pp 65–89. Wang HW, Fang YX, He WL, Chen RZ, Chen S (2022) Clinical trials with external control: beyond propensity score matching. Stat Biosci 14(2):304–317. https://doi.org/10.1007/s12561-022-09341-x. (PMID: 10.1007/s12561-022-09341-x) Jiang M, Li Y, Jiang C, Zhao L, Zhang X, Lipsky PE (2021) Machine learning in rheumatic diseases. Clin Rev Allergy Immunol 60(1):96–110. https://doi.org/10.1007/s12016-020-08805-6. (PMID: 10.1007/s12016-020-08805-632681407) Schuster NA, Twisk JWR, Ter Riet G, Heymans MW, Rijnhart JJM (2021) Noncollapsibility and its role in quantifying confounding bias in logistic regression. BMC Med Res Methodol 21(1):136. https://doi.org/10.1186/s12874-021-01316-8. (PMID: 10.1186/s12874-021-01316-8342256538259440) Chesnaye NC, Stel VS, Tripepi G, Dekker FW, Fu EL, Zoccali C, Jager KJ (2022) An introduction to inverse probability of treatment weighting in observational research. Clin Kidney J 15(1):14–20. https://doi.org/10.1093/ckj/sfab158. (PMID: 10.1093/ckj/sfab15835035932) van Straalen JW, de Roock S, Giancane G, Consolaro A, Rygg M, Nordal EB, Rubio-Perez N, Jelusic M, De Inocencio J, Vojinovic J, Wulffraat NM, Bruijning-Verhagen PCJ, Ruperto N, Swart JF, Paediat Rheumatology Int Trials O (2022) Real-world comparison of the effects of etanercept and adalimumab on well-being in non-systemic juvenile idiopathic arthritis: a propensity score matched cohort study. Pediatr Rheumatol 20(1). https://doi.org/10.1186/s12969-022-00763-x. Seror R, Lafourcade A, De Rycke Y, Pinto S, Castaneda J, Fautrel B, Mariette X, Tubach F (2022) Risk of malignancy in rheumatoid arthritis patients initiating biologics: an historical propensity score matched cohort study within the French nationwide healthcare database. RMD open 8(2). https://doi.org/10.1136/rmdopen-2021-002139. Kuster S, Jordan S, Elhai M, Held U, Steigmiller K, Bruni C, Cacciapaglia F, Vettori S, Siegert E, Rednic S, Codullo V, Airo P, Braun-Moscovici Y, Hunzelmann N, Salvador MJ, Riccieri V, Gheorghiu AM, Sancho JJA, Romanowska-Prochnicka K, Castellvi I, Kotter I, Truchetet ME, Lopez-Longo F, Novikov PI, Giollo A, Shirai Y, Belloli L, Zanatta E, Hachulla E, Smith V, Denton C, Ionescu RM, Schmeiser T, Distler JHW, Gabrielli A, Hoffmann-Vold AM, Kuwana M, Allanore Y, Distler O, Collaborators E (2022) Effectiveness and safety of tocilizumab in patients with systemic sclerosis: a propensity score matched controlled observational study of the EUSTAR cohort. RMD open 8(2). https://doi.org/10.1136/rmdopen-2022-002477. Kato S, Nakamoto H, Matsubayashi Y, Taniguchi Y, Doi T, Yoshida Y, Higashikawa A, Takeshita Y, Fukushima M, Ono T, Hara N, Okazaki R, Iwai H, Oshina M, Sugita S, Hirai S, Masuda K, Tanaka S, Oshima Y, Univ Tokyo Spine G (2022) Postoperative outcomes after degenerative lumbar spine surgery in rheumatoid arthritis patients -a propensity score-matched analysis. BMC Musculoskelet Disord 23(1). https://doi.org/10.1186/s12891-022-05326-5. Austin PC (2011) Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 10(2):150–161. https://doi.org/10.1002/pst.433. (PMID: 10.1002/pst.43320925139) Kuss O, Blettner M, Borgermann J (2016) Propensity score: an alternative method of analyzing treatment effects. Dtsch Arztebl Int 113(35–36):597–603. https://doi.org/10.3238/arztebl.2016.0597. (PMID: 10.3238/arztebl.2016.0597276584735963493) Beaujean AA (2018) Simulating data for clinical research: a tutorial. J Psychoeduc Assess 36(1):7–20. https://doi.org/10.1177/0734282917690302. (PMID: 10.1177/0734282917690302) Austin PC (2014) A comparison of 12 algorithms for matching on the propensity score. Stat Med 33(6):1057–1069. https://doi.org/10.1002/sim.6004. (PMID: 10.1002/sim.600424123228) |
| Contributed Indexing: | Keywords: Observational studies as topic; Propensity score; Random Allocation; Regression analysis; Rheumatology; Selection Bias |
| Entry Date(s): | Date Created: 20240424 Date Completed: 20240614 Latest Revision: 20241204 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11178628 |
| DOI: | 10.1007/s00296-024-05597-2 |
| PMID: | 38656609 |
| Database: | MEDLINE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | Objective: Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research.<br />Methods: We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods.<br />Results: Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed.<br />Conclusion: The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology.<br /> (© 2024. The Author(s).) |
|---|---|
| ISSN: | 1437-160X |
| DOI: | 10.1007/s00296-024-05597-2 |