1932

Abstract

The self-controlled case series method is an epidemiological study design in which individuals act as their own control. The method offers advantages but has several limitations. This article outlines the self-controlled case series method and reviews methodological developments that address some of these limitations.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-030718-105108
2019-03-07
2024-06-20
Loading full text...

Full text loading...

/deliver/fulltext/statistics/6/1/annurev-statistics-030718-105108.html?itemId=/content/journals/10.1146/annurev-statistics-030718-105108&mimeType=html&fmt=ahah

Literature Cited

  1. Andrews NJ. 2002. Statistical assessment of the association between vaccination and rare adverse events post-licensure. Vaccine 30:S49–53
    [Google Scholar]
  2. Armstrong BG, Gasparrini A, Tobias A. 2014. Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis. BMC Med. Res. Methodol. 14:122
    [Google Scholar]
  3. Baker MA, Lieu TA, Li L, Hua W, Qiang Y et al. 2015. A vaccine safety study design selection framework for the postlicensure rapid immunization safety monitoring program. Am. J. Epidemiol. 181:608–18
    [Google Scholar]
  4. Becker NG, Li Z, Kelman CW 2004. The effect of transient exposures on the risk of an acute illness with low hazard rate. Biostatistics 5:2239–48
    [Google Scholar]
  5. Connolly-Andersen AM, Hammargren E, Whitaker H, Eliasson M, Holmgren L et al. 2014. Increased risk of acute myocardial infarction and stroke during hemorrhagic fever with renal syndrome: a self-controlled case series study. Circulation 129:121295–302
    [Google Scholar]
  6. Escolano S, Hill C, Tubert-Bitter P 2013. A new self-controlled case series method for analyzing spontaneous reports of adverse events after vaccination. Am. J. Epidemiol. 178:91496–504
    [Google Scholar]
  7. Farrington CP 1995. Relative incidence estimation from case series for vaccine safety evaluation. Biometrics 51:1228–35
    [Google Scholar]
  8. Farrington CP, Anaya-Izquierdo K, Whitaker HJ, Hocine MN, Douglas I, Smeeth L 2011. Self-controlled case series analysis with event-dependent observation periods. J. Am. Stat. Assoc. 106:494417–26
    [Google Scholar]
  9. Farrington CP, Hocine MN 2010. Within-individual dependence in self-controlled case series models for recurrent events. J. R. Stat. Soc. C 59:3457–75
    [Google Scholar]
  10. Farrington CP, Whitaker HJ 2006. Semiparametric analysis of case series data (with discussion). J. R. Stat. Soc. C 55:5553–94
    [Google Scholar]
  11. Farrington CP, Whitaker HJ, Ghebremichael-Weldeselassie Y 2018. Self-Controlled Case Series Studies: A Modelling Guide with R Boca Raton, FL: Chapman and Hall/CRC
    [Google Scholar]
  12. Farrington CP, Whitaker HJ, Hocine MN 2009. Case series analysis for censored, perturbed, or curtailed post-event exposures. Biostatistics 10:13–16
    [Google Scholar]
  13. Ghebremichael-Weldeselassie Y, Whitaker HJ, Douglas IJ, Smeeth L, Farrington CP. 2017a. Self-controlled case series model with multiple event types. Comput. Stat. Data Anal. 113:64–72
    [Google Scholar]
  14. Ghebremichael-Weldeselassie Y, Whitaker HJ, Farrington CP. 2014. Self-controlled case series method with smooth age effect. Stat. Med. 33:639–49
    [Google Scholar]
  15. Ghebremichael-Weldeselassie Y, Whitaker HJ, Farrington CP 2016. Flexible modelling of vaccine effect in self-controlled case series models. Biom. J. 58:3607–22
    [Google Scholar]
  16. Ghebremichael-Weldeselassie Y, Whitaker HJ, Farrington CP. 2017b. Spline-based self-controlled case series model. Stat. Med. 36:3022–38
    [Google Scholar]
  17. Ghebremichael-Weldeselassie Y, Whitaker H, Farrington P 2017c. SCCS: the self-controlled case series method. Statistical software, R package version 1.0 https://CRAN.R-project.org/package=SCCS
    [Google Scholar]
  18. Hocine M, Guillemot D, Tubert-Bitter P, Moreau T. 2005. Testing independence between two Poisson-generated multinomial variables in case-series and cohort studies. Stat. Med. 24:4035–44
    [Google Scholar]
  19. Hocine MN, Musonda P, Andrews NJ, Farrington CP 2009. Sequential case series analysis for pharmacovigilance. J. R. Stat. Soc. A 172:1213–36
    [Google Scholar]
  20. Janes H, Sheppard L, Lumley T. 2005. Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias. Epidemiology 16:717–26
    [Google Scholar]
  21. Kuhnert R, Hecker H, Poethko-Müller C, Schlaud M, Vennemann M et al. 2011. A modified self-controlled case series method to examine association between multidose vaccinations and death. Stat. Med. 30:666–77
    [Google Scholar]
  22. Kulldorff M, Davis RL, Kolczak M, Lewis E, Lieu T, Platt R. 2011. A maximized sequential probability ratio test for drug and vaccine safety surveillance. Sequential Anal. 30:58–78
    [Google Scholar]
  23. Lee KJ, Carlin JB. 2014. Fractional polynomial adjustment for time-varying covariates in a self-controlled case series analysis. Stat. Med. 33:105–16
    [Google Scholar]
  24. Li L, Kulldorf M, Russek-Cohen E, Tse Kawai A, Hua W. 2015. Quantifying the impact of time-varying baseline risk adjustment in the self-controlled risk interval design. Pharmacoepidemiol. Drug Saf. 24:1304–12
    [Google Scholar]
  25. Lu Y, Zeger SL. 2007. On the equivalence of case-crossover and time series methods in environmental epidemiology. Biostatistics 8:337–44
    [Google Scholar]
  26. Lumley T, Levy D. 2000. Bias in the case-crossover design: implications for studies of air pollution. Environmetrics 11:689–704
    [Google Scholar]
  27. Maclure M. 1991. The case-crossover design: a method for studying transient effects on the risk of acute events. Am. J. Epidemiol. 133:144–53
    [Google Scholar]
  28. Miller E, Waight P, Farrington P, Andrews N, Stowe J, Taylor B. 2001. Idiopathic thrombocytopenic purpura and MMR vaccine. Arch. Dis. Child. 84:227–29
    [Google Scholar]
  29. Moghaddass R, Rudin C, Madigan D. 2016. The factorized self-controlled case series method: an approach for estimating the effects of many drugs on many outcomes. J. Mach. Learn. Res. 17:1–24
    [Google Scholar]
  30. Mohammed SM, Dalrymple LS, Şentürk D, Nguyen DV. 2013a. Design considerations for case series models with exposure onset measurement error. Stat. Med. 32:772–86
    [Google Scholar]
  31. Mohammed SM, Dalrymple LS, Şentürk D, Nguyen DV. 2013b. Naive hypothesis testing for case series analysis with time-varying exposure onset measurement error: inference for infection-cardiovascular risk in patients on dialysis. Biometrics 69:520–29
    [Google Scholar]
  32. Mohammed SM, Şentürk D, Dalrymple LS, Nguyen DV 2012. Measurement error case series models with application to infection-cardiovascular risk in older patients on dialysis. J. Am. Stat. Assoc. 107:5001310–23
    [Google Scholar]
  33. Musonda P, Farrington CP, Whitaker HJ. 2006. Sample sizes for self-controlled case series. Stat. Med. 25:2618–31. Erratum. 2008. Stat. Med. 27:4854–4855
    [Google Scholar]
  34. Musonda P, Hocine MN, Andrews NJ, Tubert-Bitter P, Farrington CP. 2008a. Monitoring vaccine safety using case series cumulative sum charts. Vaccine 26:5358–67
    [Google Scholar]
  35. Musonda P, Hocine MN, Whitaker HJ, Farrington CP 2008b. Self-controlled case series analyses: small sample performance. Comput. Stat. Data Anal. 52:41942–57
    [Google Scholar]
  36. Navidi W. 1998. Bidirectional case-crossover designs for exposures with time trends. Biometrics 54:596–605
    [Google Scholar]
  37. Petersen I, Douglas I, Whitaker H. 2016. Self-controlled case series methods—an alternative to standard epidemiological study designs. Br. Med. J. 354:i4515
    [Google Scholar]
  38. Quantin C, Benzenine E, Velten M, Huet F, Farrington CP, Tubert-Bitter P 2013. Self-controlled case series and misclassification bias induced by case selection from administrative databases: application to febrile convulsions in pediatric vaccine pharmacoepidemiology. Am. J. Epidemiol. 178:121731–39
    [Google Scholar]
  39. Ramsay JO 1988. Monotone regression splines in action. Stat. Sci. 3:4425–41
    [Google Scholar]
  40. Schuemie MJ, Ryan P, Shaddox T, Suchard MA 2018. SelfControlledCaseSeries: self-controlled case series. Statistical Software, R package Version 1.3.1 https://github.com/OHDSI/SelfControlledCaseSeries
    [Google Scholar]
  41. Schuemie MJ, Trifirò G, Coloma PM, Ryan PB, Madigan D 2016. Detecting adverse drug reactions following long-term drug exposure in longitudinal observational data: the exposure-adjusted self-controlled case series. Stat. Methods Med. Res. 25:62577–92
    [Google Scholar]
  42. Shaddox TR, Ryan PB, Schuemie MJ, Madigan D, Suchard MA 2016. Hierarchical models for multiple, rare outcomes using massive observational healthcare databases. Stat. Anal. Data Min. 9:4260–68
    [Google Scholar]
  43. Simpson SE 2013. A positive event dependence model for self-controlled case series with applications in postmarketing surveillance. Biometrics 69:1128–36
    [Google Scholar]
  44. Simpson SE, Madigan D, Zorych I, Schuemie MJ, Ryan PB, Suchard MA 2013. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics 69:4893–902
    [Google Scholar]
  45. Whitaker HJ, Farrington CP, Spiessens B, Musonda P. 2006. Tutorial in biostatistics: the self-controlled case series method. Stat. Med. 25:1768–97
    [Google Scholar]
  46. Whitaker HJ, Ghebremichael-Weldeselassie Y, Douglas IJ, Smeeth L, Farrington CP 2018a. Investigating the assumptions of the self-controlled case series method. Stat. Med. 37:4643–58
    [Google Scholar]
  47. Whitaker HJ, Hocine MN, Farrington CP 2007. On case-crossover methods for environmental time series data. Environmetrics 18:2157–71
    [Google Scholar]
  48. Whitaker HJ, Steer CD, Farrington CP. 2018b. Self-controlled case series studies: Just how rare does a rare non-recurrent outcome need to be?. Biom. J. 60:1110–20
    [Google Scholar]
  49. Xu S, Hambidge SJ, McClure D, Daley MF, Glanz JM 2013. A scan statistic for identifying optimal risk windows in vaccine safety studies using self-controlled case series design. Stat. Med. 32:193290–99
    [Google Scholar]
  50. Xu S, Newcomer S, Nelson J, Qian L, McClure D et al. 2014. Signal detection of adverse events with imperfect confirmation rates in vaccine safety studies using self-controlled case series design. Biom. J. 56:3513–25
    [Google Scholar]
  51. Xu S, Zhang L, Nelson JC, Zeng C, Mullooly J et al. 2011. Identifying optimal risk windows for self-controlled case series studies of vaccine efficacy. Stat. Med. 30:7742–52
    [Google Scholar]
  52. Zeng C, Newcomer SR, Glanz JM, Stroup JA, Daley MF et al. 2013. Bias correction of risk estimates in vaccine safety studies with rare adverse events using a self-controlled case series design. Am. J. Epidemiol. 178:121750–59
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-030718-105108
Loading
/content/journals/10.1146/annurev-statistics-030718-105108
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error