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Measurement error in
medical research
Maarten van Smeden, PhD
University Medical Center Utrecht
Julius Center for Health Sciences and Primary Care
The Netherlands
Twitter: @MvanSmeden
Email: M.vanSmeden@umcutrecht.nl
29 Feb 2020
Clinical Studies Application and Education Project Group Meeting (STATISTANBUL 2020)
Turkish Society of Cardiology
Istanbul, Turkey
Sides available at https://www.slideshare.net/MaartenvanSmeden
I have no conflicts of interest to declare
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error
“Errors in reading, calculating or recording a
numerical value. The difference between
observed values of a variable recorded
under similar conditions and some fixed true
value.“
The Cambridge Dictionary of Statistics (4th ed), ISBN: 9780521766999
Twitter: @MaartenvSmedenIstanbul, February 29 2020 img: https://bit.ly/2T9UnRt
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement of systolic blood pressure
Measurement error due to:
• White coat effect1
• Non-adherence to measurement protocol2
• Fallibity of measurement instruments3
• ….
Measurement error varies:
• Number of BP measurements taken4
• Gender4
• Circadian rhythm
• ….
doi: 110.1370/afm.1211; 210.3399/096016407782604965; 310.2147/MDER.S141599; 410.3109/08037051.2014.986952
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example circadian rythm
doi: 10.1111/j.1552-6909.2000.tb02771.x
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Imprecision of medical measurements
doi: 10.1136/bmj.m149
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error: a long list
• Blood pressure
• Dietary intake
• Smoking status
• Air pollution
• BMI
• Physical activity
• Vaccination status
• Social class
• Carotid intima media thickness
• Thyroid hormone levels
• Glucose levels
• Cholesterol levels
• Income
• Family history
• Mental health history
• Education level
• “Intelligence”
• Sedentary hours
• Vitamin use
• Immigration status
• Age at first intercourse
• ICD coding
• Symptoms
• Medication use
• Visceral adipose tissue
• ….
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error mentioned
Journals of epidemiology
Jurek et al. 20061 61% (N = 35)
Brakenhoff et al. 20162 56% (N = 198)
Shaw et al. 20193 80% (N = 65)
doi: 110.1007/s10654-006-9083-0; 210.1016/j.jclinepi.2018.02.02; 310.1016/j.annepidem.2018.09.001
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error mentioned
Journals of general medicine
Brakenhoff et al. 20162: 25% (N = 57)
doi: 210.1016/j.jclinepi.2018.02.02
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error “corrections” applied
Journals of epidemiology
Jurek et al. 20061: 2% (N = 1)
Brakenhoff et al. 20162: 4% (N = 13)
Shaw et al. 20193: 6% (N = 5)
doi: 110.1007/s10654-006-9083-0; 210.1016/j.jclinepi.2018.02.02; 310.1016/j.annepidem.2018.09.001
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error “corrections” applied
Journals of general medicine
Brakenhoff et al. 20162: 0% (N = 0)
doi: 210.1016/j.jclinepi.2018.02.02
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Twitter: @MaartenvSmedenIstanbul, February 29 2020
• Many studies categorized the continuous variables: dichotomania
– Common myth: categorization will reduce impact of
measurement error
• Many studies made incomplete/incorrect claims
– Many stated that estimates could only be attenuated
(‘underestimated’) due to measurement error
– Some claimed no bias due to measurement error in
associations
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Excerpt from a trial protocol
excerpt from: 10.1186/s13063-018-2954-3
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Types of measurement error
Measurement are
consistently wrong in a
particular direction
Classical (Random)
measurement error
Differential
measurement error
Systematic
measurement error
Measurements fluctuate
around their true value
Measurements are
consistently wrong in a
particular direction,
varying per group
Courtesy: Linda Nab
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Classical measurement error
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Classical measurement error
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Tripple whammy of measurement error
• Bias
• Increased imprecision
• Masked functional relations
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Tripple whammy of measurement error
• Bias
• Increased imprecision
• Masked functional relations
Always weaker effects?
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example: classical measurement error
doi: 10.1371/journal.pone.0192298
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example: classical measurement error
Second Manifestations of ARTerial disease (SMART) cohort
doi: 10.1371/journal.pone.0192298
Effect of
interest
Confounder
with error
Outcome
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example: classical measurement error
doi: 10.1371/journal.pone.0192298
% bias in hazard ratio for SBP (multivariable Cox regression model)
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example: classical measurement error
Second Manifestations of ARTerial disease (SMART) cohort
doi: 10.1371/journal.pone.0192298
Effect of
interest
Confounder
with error
Outcome
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Example: classical measurement error
doi: 10.1371/journal.pone.0192298
% bias in hazard ratio for SBP (multivariable Cox regression model)
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Randomized controlled trials
doi: 10.1002/sim.8359
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Randomized controlled trials
Classical (Random)
measurement error
Systematic
measurement error
Differential
measurement error
doi: 10.1002/sim.8359
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Randomized controlled trials
Classical (Random)
measurement error
Systematic
measurement error
Differential
measurement error
• Unbiased treatment effect estimator
• Increased Type-II error
• Nominal Type-I error
• Possibly biased TE estimator
• Type-II error affected
• Type-I generally nominal
• Possibly biased TE estimator
• Type-II error affected
• Type-I not nominal
doi: 10.1002/sim.8359
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Tripple whammy of measurement error
• Bias
• Increased imprecision
• Masked functional relations
Usually the target for measurement error “corrections”
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error corrections
External validation set
Study sample
!∗
External validation set
Standard
measurements
Standard
measurements
+
Validated
measurements
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error corrections
Internal validation set
Study sample
!∗Internal validation set
Standard
measurements
Standard
measurements
+
Validated
measurements
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error corrections
Replicates study
Study sample
!∗
Standard
measurements
replicated
Twitter: @MaartenvSmedenIstanbul, February 29 2020
A few measurement error models
Measurement of the variables with error (X) with/without truth (T)
• Latent variable models
– replicate measures of X to approximate T
• Regression calibration
– X is replaced by a prediction of T
• SIMEX:
– Error is simulated + added to X, extrapolated to estimate T effect of X
• MIME:
– T is multiple imputed for individuals for whom T wasn't observed
!∗
Twitter: @MaartenvSmedenIstanbul, February 29 2020
A few measurement error models
Measurement of the variables with error (X) with/without truth (T)
• Latent variable models
– E.g. R-package lavaan
• Regression calibration
– E.g. R-package mecor (www.github.com/LindaNab/mecor)
• SIMEX:
– E.g. R-package simex
• MIME:
– E.g. R-package mice
!∗
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Without replicates or validation data
https://lindanab.shinyapps.io/SensitivityAnalysis/
Preprint: https://arxiv.org/abs/1912.05800
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error models are not new
doi: 10.2307/1422689
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement error models are not new
doi: 10.2307/2530508
Twitter: @MaartenvSmedenIstanbul, February 29 2020 img: https://support.apple.com/en-us/HT208931
Twitter: @MaartenvSmedenIstanbul, February 29 2020
419,297 participants
doi: 10.1056/NEJMoa1901183
Twitter: @MaartenvSmedenIstanbul, February 29 2020
Measurement: are labels the new oil?
https://twitter.com/DrHughHarvey/status/1230218991026819077
Twitter: @MaartenvSmedenIstanbul, February 29 2020 doi: 10.1093/ije/dyz251
Twitter: @MaartenvSmedenIstanbul, February 29 2020

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Measurement error in medical research

  • 1. Measurement error in medical research Maarten van Smeden, PhD University Medical Center Utrecht Julius Center for Health Sciences and Primary Care The Netherlands Twitter: @MvanSmeden Email: M.vanSmeden@umcutrecht.nl 29 Feb 2020 Clinical Studies Application and Education Project Group Meeting (STATISTANBUL 2020) Turkish Society of Cardiology Istanbul, Turkey Sides available at https://www.slideshare.net/MaartenvanSmeden I have no conflicts of interest to declare
  • 2. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error “Errors in reading, calculating or recording a numerical value. The difference between observed values of a variable recorded under similar conditions and some fixed true value.“ The Cambridge Dictionary of Statistics (4th ed), ISBN: 9780521766999
  • 3. Twitter: @MaartenvSmedenIstanbul, February 29 2020 img: https://bit.ly/2T9UnRt
  • 4. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement of systolic blood pressure Measurement error due to: • White coat effect1 • Non-adherence to measurement protocol2 • Fallibity of measurement instruments3 • …. Measurement error varies: • Number of BP measurements taken4 • Gender4 • Circadian rhythm • …. doi: 110.1370/afm.1211; 210.3399/096016407782604965; 310.2147/MDER.S141599; 410.3109/08037051.2014.986952
  • 5. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example circadian rythm doi: 10.1111/j.1552-6909.2000.tb02771.x
  • 6. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Imprecision of medical measurements doi: 10.1136/bmj.m149
  • 7. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error: a long list • Blood pressure • Dietary intake • Smoking status • Air pollution • BMI • Physical activity • Vaccination status • Social class • Carotid intima media thickness • Thyroid hormone levels • Glucose levels • Cholesterol levels • Income • Family history • Mental health history • Education level • “Intelligence” • Sedentary hours • Vitamin use • Immigration status • Age at first intercourse • ICD coding • Symptoms • Medication use • Visceral adipose tissue • ….
  • 8. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error mentioned Journals of epidemiology Jurek et al. 20061 61% (N = 35) Brakenhoff et al. 20162 56% (N = 198) Shaw et al. 20193 80% (N = 65) doi: 110.1007/s10654-006-9083-0; 210.1016/j.jclinepi.2018.02.02; 310.1016/j.annepidem.2018.09.001
  • 9. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error mentioned Journals of general medicine Brakenhoff et al. 20162: 25% (N = 57) doi: 210.1016/j.jclinepi.2018.02.02
  • 10. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error “corrections” applied Journals of epidemiology Jurek et al. 20061: 2% (N = 1) Brakenhoff et al. 20162: 4% (N = 13) Shaw et al. 20193: 6% (N = 5) doi: 110.1007/s10654-006-9083-0; 210.1016/j.jclinepi.2018.02.02; 310.1016/j.annepidem.2018.09.001
  • 11. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error “corrections” applied Journals of general medicine Brakenhoff et al. 20162: 0% (N = 0) doi: 210.1016/j.jclinepi.2018.02.02
  • 13. Twitter: @MaartenvSmedenIstanbul, February 29 2020 • Many studies categorized the continuous variables: dichotomania – Common myth: categorization will reduce impact of measurement error • Many studies made incomplete/incorrect claims – Many stated that estimates could only be attenuated (‘underestimated’) due to measurement error – Some claimed no bias due to measurement error in associations
  • 14. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Excerpt from a trial protocol excerpt from: 10.1186/s13063-018-2954-3
  • 15. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Types of measurement error Measurement are consistently wrong in a particular direction Classical (Random) measurement error Differential measurement error Systematic measurement error Measurements fluctuate around their true value Measurements are consistently wrong in a particular direction, varying per group Courtesy: Linda Nab
  • 16. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Classical measurement error
  • 17. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Classical measurement error
  • 18. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Tripple whammy of measurement error • Bias • Increased imprecision • Masked functional relations
  • 19. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Tripple whammy of measurement error • Bias • Increased imprecision • Masked functional relations Always weaker effects?
  • 20. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example: classical measurement error doi: 10.1371/journal.pone.0192298
  • 21. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example: classical measurement error Second Manifestations of ARTerial disease (SMART) cohort doi: 10.1371/journal.pone.0192298 Effect of interest Confounder with error Outcome
  • 22. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example: classical measurement error doi: 10.1371/journal.pone.0192298 % bias in hazard ratio for SBP (multivariable Cox regression model)
  • 23. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example: classical measurement error Second Manifestations of ARTerial disease (SMART) cohort doi: 10.1371/journal.pone.0192298 Effect of interest Confounder with error Outcome
  • 24. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Example: classical measurement error doi: 10.1371/journal.pone.0192298 % bias in hazard ratio for SBP (multivariable Cox regression model)
  • 25. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Randomized controlled trials doi: 10.1002/sim.8359
  • 26. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Randomized controlled trials Classical (Random) measurement error Systematic measurement error Differential measurement error doi: 10.1002/sim.8359
  • 27. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Randomized controlled trials Classical (Random) measurement error Systematic measurement error Differential measurement error • Unbiased treatment effect estimator • Increased Type-II error • Nominal Type-I error • Possibly biased TE estimator • Type-II error affected • Type-I generally nominal • Possibly biased TE estimator • Type-II error affected • Type-I not nominal doi: 10.1002/sim.8359
  • 28. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Tripple whammy of measurement error • Bias • Increased imprecision • Masked functional relations Usually the target for measurement error “corrections”
  • 29. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error corrections External validation set Study sample !∗ External validation set Standard measurements Standard measurements + Validated measurements
  • 30. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error corrections Internal validation set Study sample !∗Internal validation set Standard measurements Standard measurements + Validated measurements
  • 31. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error corrections Replicates study Study sample !∗ Standard measurements replicated
  • 32. Twitter: @MaartenvSmedenIstanbul, February 29 2020 A few measurement error models Measurement of the variables with error (X) with/without truth (T) • Latent variable models – replicate measures of X to approximate T • Regression calibration – X is replaced by a prediction of T • SIMEX: – Error is simulated + added to X, extrapolated to estimate T effect of X • MIME: – T is multiple imputed for individuals for whom T wasn't observed !∗
  • 33. Twitter: @MaartenvSmedenIstanbul, February 29 2020 A few measurement error models Measurement of the variables with error (X) with/without truth (T) • Latent variable models – E.g. R-package lavaan • Regression calibration – E.g. R-package mecor (www.github.com/LindaNab/mecor) • SIMEX: – E.g. R-package simex • MIME: – E.g. R-package mice !∗
  • 34. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Without replicates or validation data https://lindanab.shinyapps.io/SensitivityAnalysis/ Preprint: https://arxiv.org/abs/1912.05800
  • 35. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error models are not new doi: 10.2307/1422689
  • 36. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement error models are not new doi: 10.2307/2530508
  • 37. Twitter: @MaartenvSmedenIstanbul, February 29 2020 img: https://support.apple.com/en-us/HT208931
  • 38. Twitter: @MaartenvSmedenIstanbul, February 29 2020 419,297 participants doi: 10.1056/NEJMoa1901183
  • 39. Twitter: @MaartenvSmedenIstanbul, February 29 2020 Measurement: are labels the new oil? https://twitter.com/DrHughHarvey/status/1230218991026819077
  • 40. Twitter: @MaartenvSmedenIstanbul, February 29 2020 doi: 10.1093/ije/dyz251