Skip to main content
Top
Published in: Lifetime Data Analysis 1/2024

12-08-2023

Sensitivity Analysis for Observational Studies with Recurrent Events

Authors: Jeffrey Zhang, Dylan S. Small

Published in: Lifetime Data Analysis | Issue 1/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We conduct an observational study of the effect of sickle cell trait Haemoglobin AS (HbAS) on the hazard rate of malaria fevers in children. Assuming no unmeasured confounding, there is strong evidence that HbAS reduces the rate of malarial fevers. Since this is an observational study, however, the no unmeasured confounding assumption is strong. A sensitivity analysis considers how robust a conclusion is to a potential unmeasured confounder. We propose a new sensitivity analysis method for recurrent event data and apply it to the malaria study. We find that for the causal conclusion that HbAS is protective against malarial fevers to be overturned, the hypothesized unmeasured confounder must be as influential as all but one of the measured confounders.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Literature
go back to reference Aalen OO, Cook RJ, K KR, (2015) Does cox analysis of a randomized survival study yield a causal treatment effect? Lifetime Data Anal 21(4):579–93 Aalen OO, Cook RJ, K KR, (2015) Does cox analysis of a randomized survival study yield a causal treatment effect? Lifetime Data Anal 21(4):579–93
go back to reference Amorim LD, Cai J (2014) Modelling recurrent events: a tutorial for analysis in epidemiology. Int J Epidemiol 44(1):324–333, https://doi.org/10.1093/ije/dyu222, https://academic.oup.com/ije/article-pdf/44/1/324/14152617/dyu222.pdf Amorim LD, Cai J (2014) Modelling recurrent events: a tutorial for analysis in epidemiology. Int J Epidemiol 44(1):324–333, https://​doi.​org/​10.​1093/​ije/​dyu222, https://​academic.​oup.​com/​ije/​article-pdf/​44/​1/​324/​14152617/​dyu222.​pdf
go back to reference Azen R, Budescu DV (2003) The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods 8(2):129–148CrossRef Azen R, Budescu DV (2003) The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods 8(2):129–148CrossRef
go back to reference Azen R, Traxel N (2009) Using dominance analysis to determine predictor importance in logistic regression. J Educ Behav Stat 34(3):319–47CrossRef Azen R, Traxel N (2009) Using dominance analysis to determine predictor importance in logistic regression. J Educ Behav Stat 34(3):319–47CrossRef
go back to reference Carnegie NB, Harada M, Hill J (2016) Assessing sensitivity to unmeasured confounding using a simulated potential confounder. J Res Educ Eff 9:395–420 Carnegie NB, Harada M, Hill J (2016) Assessing sensitivity to unmeasured confounding using a simulated potential confounder. J Res Educ Eff 9:395–420
go back to reference Cinelli C, Hazlett C (2020) Making sense of sensitivity: extending omitted variable bias. J R Stat Soc B 82:39–67MathSciNetCrossRef Cinelli C, Hazlett C (2020) Making sense of sensitivity: extending omitted variable bias. J R Stat Soc B 82:39–67MathSciNetCrossRef
go back to reference Cornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder EL (1959) Smoking and lung cancer. J Natl Cancer Inst 22:173–203 Cornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder EL (1959) Smoking and lung cancer. J Natl Cancer Inst 22:173–203
go back to reference Dempster AP, Laird NM, Rubin DR (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B (Methodological) 39(1):1–22MathSciNet Dempster AP, Laird NM, Rubin DR (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B (Methodological) 39(1):1–22MathSciNet
go back to reference Ding P, VanderWeele TJ (2016) Sensitivity analysis without assumptions. Epidemiology pp 368–377 Ding P, VanderWeele TJ (2016) Sensitivity analysis without assumptions. Epidemiology pp 368–377
go back to reference Dorie V, Harada M, Carnegie NB, Hill J (2016) A flexible, interpretable framework for assessing sensitivity to unmeasured confounding. Stat Med 35:3453–3470MathSciNetCrossRef Dorie V, Harada M, Carnegie NB, Hill J (2016) A flexible, interpretable framework for assessing sensitivity to unmeasured confounding. Stat Med 35:3453–3470MathSciNetCrossRef
go back to reference Franks A, D’Amour A, Feller A (2019) Flexible sensitivity analysis for observational studies without observable implications. J Am Stat Ass 115:1730–1746MathSciNetCrossRef Franks A, D’Amour A, Feller A (2019) Flexible sensitivity analysis for observational studies without observable implications. J Am Stat Ass 115:1730–1746MathSciNetCrossRef
go back to reference Gastwirth J, Krieger AM, Rosenbaum PR (1998) Dual and simultaneous sensitivity analysis for matched pairs. Biometrika 85:907–920CrossRef Gastwirth J, Krieger AM, Rosenbaum PR (1998) Dual and simultaneous sensitivity analysis for matched pairs. Biometrika 85:907–920CrossRef
go back to reference Huang R, Dulai P, Xu R (2020) Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes. Stat Med 39:3397–3411MathSciNetCrossRef Huang R, Dulai P, Xu R (2020) Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes. Stat Med 39:3397–3411MathSciNetCrossRef
go back to reference Ichino A, Mealli F, Nannicini T (2008) From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity? J Appl Econom 23(3):305–327MathSciNetCrossRef Ichino A, Mealli F, Nannicini T (2008) From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity? J Appl Econom 23(3):305–327MathSciNetCrossRef
go back to reference Imbens G (2003) Sensitivity to exogeneity assumptions in program evaluation. Am Econ Rev 93:126–132CrossRef Imbens G (2003) Sensitivity to exogeneity assumptions in program evaluation. Am Econ Rev 93:126–132CrossRef
go back to reference Kang H, Kreuels B, May J, Small DS (2016) Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting. Ann Appl Stat 10(1):335–364MathSciNetCrossRef Kang H, Kreuels B, May J, Small DS (2016) Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting. Ann Appl Stat 10(1):335–364MathSciNetCrossRef
go back to reference Karmakar B, French B, Small DS (2019) Integrating the evidence from evidence factors in observational studies. Biometrika 106(2):353–367, https://doi.org/10.1093/biomet/asz003, https://academic.oup.com/biomet/article-pdf/106/2/353/28575468/asz003.pdf Karmakar B, French B, Small DS (2019) Integrating the evidence from evidence factors in observational studies. Biometrika 106(2):353–367, https://​doi.​org/​10.​1093/​biomet/​asz003, https://​academic.​oup.​com/​biomet/​article-pdf/​106/​2/​353/​28575468/​asz003.​pdf
go back to reference Kreuels B, Ehrhardt S, Kreuzberg C, Adjei S, Kobbe R, Burchard GD, Ehmen C, Ayim M, Adjei O, May J (2009) Sickle cell trait (hbas) and stunting in children below two years of age in an area of high malaria transmission. Malar J 8(16) Kreuels B, Ehrhardt S, Kreuzberg C, Adjei S, Kobbe R, Burchard GD, Ehmen C, Ayim M, Adjei O, May J (2009) Sickle cell trait (hbas) and stunting in children below two years of age in an area of high malaria transmission. Malar J 8(16)
go back to reference Lin D, Psaty BM, Kronmal RA (1998) Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54:948–963CrossRef Lin D, Psaty BM, Kronmal RA (1998) Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54:948–963CrossRef
go back to reference Louis TA (1982) Finding the observed information matrix when using the em algorithm. J R Stat Soc Ser B (Methodological) 44(2):226–233MathSciNet Louis TA (1982) Finding the observed information matrix when using the em algorithm. J R Stat Soc Ser B (Methodological) 44(2):226–233MathSciNet
go back to reference Martinussen T, Vansteelandt S (2013) On collapsibility and confounding bias in cox and aalen regression models. Lifetime Data Anal 19(3):279–96MathSciNetCrossRef Martinussen T, Vansteelandt S (2013) On collapsibility and confounding bias in cox and aalen regression models. Lifetime Data Anal 19(3):279–96MathSciNetCrossRef
go back to reference McCandless LC, Gustafson P, Levy A (2007) Bayesian sensitivity analysis for unmeasured confounding in observational studies. Stat Med 26:2331–2347MathSciNetCrossRef McCandless LC, Gustafson P, Levy A (2007) Bayesian sensitivity analysis for unmeasured confounding in observational studies. Stat Med 26:2331–2347MathSciNetCrossRef
go back to reference Taylor SM, Parobek CM, Fairhurst RM (2012) Haemoglobinopathies and the clinical epidemiology of malaria: a systematic review and meta-analysis. Lancet Infect Dis 12(6):457–468CrossRef Taylor SM, Parobek CM, Fairhurst RM (2012) Haemoglobinopathies and the clinical epidemiology of malaria: a systematic review and meta-analysis. Lancet Infect Dis 12(6):457–468CrossRef
go back to reference Vaida F, Xu R (2000) Proportional hazards model with random effects. Stat Med 19(24):3309–3324CrossRef Vaida F, Xu R (2000) Proportional hazards model with random effects. Stat Med 19(24):3309–3324CrossRef
go back to reference Zhang B, Small DS (2020) A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in children. J R Stat Soc Ser C R Stat Soc 69(5):1285–1305MathSciNetCrossRef Zhang B, Small DS (2020) A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in children. J R Stat Soc Ser C R Stat Soc 69(5):1285–1305MathSciNetCrossRef
Metadata
Title
Sensitivity Analysis for Observational Studies with Recurrent Events
Authors
Jeffrey Zhang
Dylan S. Small
Publication date
12-08-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 1/2024
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09607-6

Other articles of this Issue 1/2024

Lifetime Data Analysis 1/2024 Go to the issue

PREFACE

Preface