Treffer: Causal Approaches to Disease Progression Analyses.
Title:
Causal Approaches to Disease Progression Analyses.
Authors:
Gonçalves BP; From the Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom, and., Suzuki E; Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
Source:
Epidemiology (Cambridge, Mass.) [Epidemiology] 2025 Nov 01; Vol. 36 (6), pp. 732-740. Date of Electronic Publication: 2025 Jul 04.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9009644 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5487 (Electronic) Linking ISSN: 10443983 NLM ISO Abbreviation: Epidemiology Subsets: MEDLINE
Imprint Name(s):
Publication: <2000>- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
Original Publication: [Cambridge, MA : Blackwell Scientific Publications ; Chestnut Hill, MA : Epidemiology Resources, c1990-
MeSH Terms:
References:
Hudgens MG, Halloran ME. Causal vaccine effects on binary postinfection outcomes. J Am Stat Assoc. 2006;101:51–64.
Shepherd BE, Redman MW, Ankerst DP. Does finasteride affect the severity of prostate cancer? A causal sensitivity analysis. J Am Stat Assoc. 2008;103:1392–1404.
Hudgens MG, Hoering A, Self SG. On the analysis of viral load endpoints in HIV vaccine trials. Stat Med. 2003;22:2281–2298.
Little RJ, Rubin DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu Rev Public Health. 2000;21:121–145.
Halloran ME, Longini IM, Struchiner CJ. Design and Analysis of Vaccine Studies. Statistics for Biology and Health. Springer. 2010.
Gonçalves BP, Suzuki E. Preventable fraction in the context of disease progression. Epidemiology. 2024;35:801–804.
Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–29.
Suzuki E. Generalized causal measure: the beauty lies in its generality. Epidemiology. 2015;26:490–495.
Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc. 2005;100:322–331.
Suzuki E, Mitsuhashi T, Tsuda T, Yamamoto E. A counterfactual approach to bias and effect modification in terms of response types. BMC Med Res Methodol. 2013;13:101.
Suzuki E, Shinozaki T, Yamamoto E. Causal diagrams: pitfalls and tips. J Epidemiol. 2020;30:153–162.
Rubin DB. Causal inference through potential outcomes and principal stratification: application to studies with “censoring” due to death. Stat Sci. 2006;21:299–309.
Zhang JL, Rubin DB. Estimation of causal effects via principal stratification when some outcomes are truncated by “death”. J Educ Behav Stat. 2003;28:353–368.
Talaat KR, Alaimo C, Martin P, et al. Human challenge study with a Shigella bioconjugate vaccine: analyses of clinical efficacy and correlate of protection. EBioMedicine. 2021;66:103310.
Minassian AM, Silk SE, Barrett JR, et al. Reduced blood-stage malaria growth and immune correlates in humans following RH5 vaccination. Med. 2021;2:701–719.e19.
Rapeport G, Smith E, Gilbert A, Catchpole A, McShane H, Chiu C. SARS-CoV-2 human challenge studies - establishing the model during an evolving pandemic. N Engl J Med. 2021;385:961–964.
Abo YN, Jamrozik E, McCarthy JS, Roestenberg M, Steer AC, Osowicki J. Strategic and scientific contributions of human challenge trials for vaccine development: facts versus fantasy. Lancet Infect Dis. 2023;23:e533–e546.
Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143–155.
Pearl J. Direct and Indirect Effects. In: Proceedings of the Seventeenth Conference on Uncertainy in Articial Intelligence. San Francisco, CA. 2001.
Young JG, Stensrud MJ. Identified versus interesting causal effects in fertility trials and other settings with competing or truncation events. Epidemiology. 2021;32:569–572.
Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020;39:1199–1236.
Mauger EA, Mauger DT, Fish JE, Chinchilli VM, Israel E; Asthma Clinical Trials Network. Summarizing methacholine challenges in clinical research. Control Clin Trials. 2001;22:244S–251S.
Cockcroft DW, Davis BE, Roh Y, Lourens J-A. Effect of ingested H(1) antihistamines on methacholine challenge. J Allergy Clin Immunol. 2015;135:579–580.
Pfaar O, Zieglmayer P. Allergen exposure chambers: implementation in clinical trials in allergen immunotherapy. Clin Transl Allergy. 2020;10:33.
Stensrud MJ, Smith L. Identification of vaccine effects when exposure status is unknown. Epidemiology. 2023;34:216–224.
Chiu YH, Stensrud MJ, Dahabreh IJ, et al. The effect of prenatal treatments on offspring events in the presence of competing events: an application to a randomized trial of fertility therapies. Epidemiology. 2020;31:636–643.
Chiba Y, VanderWeele TJ. A simple method for principal strata effects when the outcome has been truncated due to death. Am J Epidemiol. 2011;173:745–751.
Suzuki E, Yamamoto E, Tsuda T. Identification of operating mediation and mechanism in the sufficient-component cause framework. Eur J Epidemiol. 2011;26:347–357.
VanderWeele TJ, Vansteelandt S. Conceptual issues concerning mediation, interventions and composition. Stat Its Interface. 2009;2:457–468.
Barda N, Dagan N, Cohen C, et al. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. Lancet. 2021;398:2093–2100.
Dagan N, Barda N, Biron-Shental T, et al. Effectiveness of the BNT162b2 mRNA COVID-19 vaccine in pregnancy. Nat Med. 2021;27:1693–1695.
Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol. 2023;8:55–63.
VanderWeele TJ. Controlled direct and mediated effects: definition, identification and bounds. Scand Stat Theory Appl. 2011;38:551–563.
Tchetgen Tchetgen EJ. Identification and estimation of survivor average causal effects. Stat Med. 2014;33:3601–3628.
Wang L, Zhou XH, Richardson TS. Identification and estimation of causal effects with outcomes truncated by death. Biometrika. 2017;104:597–612.
Chiba Y, Taguri M, Uemura Y. On the identification of the survivor average causal effect. J Biomet Biostat. 2011;02:1000e104.
Shepherd BE, Redman MW, Ankerst DP. Does finasteride affect the severity of prostate cancer? A causal sensitivity analysis. J Am Stat Assoc. 2008;103:1392–1404.
Hudgens MG, Hoering A, Self SG. On the analysis of viral load endpoints in HIV vaccine trials. Stat Med. 2003;22:2281–2298.
Little RJ, Rubin DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu Rev Public Health. 2000;21:121–145.
Halloran ME, Longini IM, Struchiner CJ. Design and Analysis of Vaccine Studies. Statistics for Biology and Health. Springer. 2010.
Gonçalves BP, Suzuki E. Preventable fraction in the context of disease progression. Epidemiology. 2024;35:801–804.
Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–29.
Suzuki E. Generalized causal measure: the beauty lies in its generality. Epidemiology. 2015;26:490–495.
Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc. 2005;100:322–331.
Suzuki E, Mitsuhashi T, Tsuda T, Yamamoto E. A counterfactual approach to bias and effect modification in terms of response types. BMC Med Res Methodol. 2013;13:101.
Suzuki E, Shinozaki T, Yamamoto E. Causal diagrams: pitfalls and tips. J Epidemiol. 2020;30:153–162.
Rubin DB. Causal inference through potential outcomes and principal stratification: application to studies with “censoring” due to death. Stat Sci. 2006;21:299–309.
Zhang JL, Rubin DB. Estimation of causal effects via principal stratification when some outcomes are truncated by “death”. J Educ Behav Stat. 2003;28:353–368.
Talaat KR, Alaimo C, Martin P, et al. Human challenge study with a Shigella bioconjugate vaccine: analyses of clinical efficacy and correlate of protection. EBioMedicine. 2021;66:103310.
Minassian AM, Silk SE, Barrett JR, et al. Reduced blood-stage malaria growth and immune correlates in humans following RH5 vaccination. Med. 2021;2:701–719.e19.
Rapeport G, Smith E, Gilbert A, Catchpole A, McShane H, Chiu C. SARS-CoV-2 human challenge studies - establishing the model during an evolving pandemic. N Engl J Med. 2021;385:961–964.
Abo YN, Jamrozik E, McCarthy JS, Roestenberg M, Steer AC, Osowicki J. Strategic and scientific contributions of human challenge trials for vaccine development: facts versus fantasy. Lancet Infect Dis. 2023;23:e533–e546.
Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143–155.
Pearl J. Direct and Indirect Effects. In: Proceedings of the Seventeenth Conference on Uncertainy in Articial Intelligence. San Francisco, CA. 2001.
Young JG, Stensrud MJ. Identified versus interesting causal effects in fertility trials and other settings with competing or truncation events. Epidemiology. 2021;32:569–572.
Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A causal framework for classical statistical estimands in failure-time settings with competing events. Stat Med. 2020;39:1199–1236.
Mauger EA, Mauger DT, Fish JE, Chinchilli VM, Israel E; Asthma Clinical Trials Network. Summarizing methacholine challenges in clinical research. Control Clin Trials. 2001;22:244S–251S.
Cockcroft DW, Davis BE, Roh Y, Lourens J-A. Effect of ingested H(1) antihistamines on methacholine challenge. J Allergy Clin Immunol. 2015;135:579–580.
Pfaar O, Zieglmayer P. Allergen exposure chambers: implementation in clinical trials in allergen immunotherapy. Clin Transl Allergy. 2020;10:33.
Stensrud MJ, Smith L. Identification of vaccine effects when exposure status is unknown. Epidemiology. 2023;34:216–224.
Chiu YH, Stensrud MJ, Dahabreh IJ, et al. The effect of prenatal treatments on offspring events in the presence of competing events: an application to a randomized trial of fertility therapies. Epidemiology. 2020;31:636–643.
Chiba Y, VanderWeele TJ. A simple method for principal strata effects when the outcome has been truncated due to death. Am J Epidemiol. 2011;173:745–751.
Suzuki E, Yamamoto E, Tsuda T. Identification of operating mediation and mechanism in the sufficient-component cause framework. Eur J Epidemiol. 2011;26:347–357.
VanderWeele TJ, Vansteelandt S. Conceptual issues concerning mediation, interventions and composition. Stat Its Interface. 2009;2:457–468.
Barda N, Dagan N, Cohen C, et al. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. Lancet. 2021;398:2093–2100.
Dagan N, Barda N, Biron-Shental T, et al. Effectiveness of the BNT162b2 mRNA COVID-19 vaccine in pregnancy. Nat Med. 2021;27:1693–1695.
Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol. 2023;8:55–63.
VanderWeele TJ. Controlled direct and mediated effects: definition, identification and bounds. Scand Stat Theory Appl. 2011;38:551–563.
Tchetgen Tchetgen EJ. Identification and estimation of survivor average causal effects. Stat Med. 2014;33:3601–3628.
Wang L, Zhou XH, Richardson TS. Identification and estimation of causal effects with outcomes truncated by death. Biometrika. 2017;104:597–612.
Chiba Y, Taguri M, Uemura Y. On the identification of the survivor average causal effect. J Biomet Biostat. 2011;02:1000e104.
Contributed Indexing:
Keywords: Causal inference; Controlled direct effects; Disease progression; Potential outcomes; Principal stratification
Entry Date(s):
Date Created: 20250714 Date Completed: 20250925 Latest Revision: 20251216
Update Code:
20251216
DOI:
10.1097/EDE.0000000000001893
PMID:
40658050
Database:
MEDLINE
Weitere Informationen
Epidemiologic analyses that aim to quantify exposure effects on disease progression are not uncommon. Understanding the implications of these studies, however, is complicated, in part because different causal estimands could, at least in theory, be the target of such analyses. Here, to facilitate interpretation of these studies, we describe different settings in which causal questions related to disease progression can be asked, and consider possible estimands. For clarity, our discussion is structured around settings defined based on two factors: whether the disease occurrence is manipulable or not, and the type of outcome. We describe relevant causal structures and sets of response types, which consist of joint potential outcomes of disease occurrence and disease progression, and argue that settings where interventions to manipulate disease occurrence are not plausible are more common, and that, in this case, principal stratification might be an appropriate framework to conceptualize the analysis. Further, we suggest that the precise definition of the outcome of interest, in particular of what constitutes its permissible levels, might determine whether potential outcomes linked to disease progression are definable in different strata of the population. Our hope is that this paper will encourage additional methodological work on causal analysis of disease progression, as well as serve as a resource for future applied studies.
(Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
Disclosure: The authors report no conflicts of interest.