Pith. sign in

REVIEW 3 minor 137 references

Reviewed by Pith at T0; open to challenge.

T0 means a machine referee read the full paper against a public rubric. The mark states how deep the mechanical check went, never who wrote it. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

Methods exist to address measurement error in both outcomes and multiple covariates in routine biomedical data analyses.

2026-06-30 08:41 UTC pith:DDL5EVFS

load-bearing objection A clear tutorial on existing measurement error methods with supplied code and a running example, but no new techniques or results.

arxiv 2606.28810 v1 pith:DDL5EVFS submitted 2026-06-27 stat.AP

Methods to address measurement error in both Outcome and Covariates

Pamela A. Shaw , Bryan E. Shepherd This is my paper
classification stat.AP
keywords measurement erroroutcome errorcovariate errorroutine dataelectronic health recordsbiomedical researchstatistical correction methodstutorial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Routine data sources such as electronic health records frequently contain measurement errors in the outcome and in several covariates at once. This tutorial reviews available statistical methods that can correct for those errors simultaneously. The authors demonstrate the methods side by side on a single running example and supply the data and code so readers can reproduce every comparison. They close by comparing the practical trade-offs among the approaches and identifying gaps that still require new work.

Core claim

For analyses of routine data, methods that address errors in the outcome and multiple covariates are needed, and available methods can be illustrated with examples.

What carries the argument

A running example that applies and compares several correction methods for measurement error in the outcome and in multiple covariates simultaneously.

Load-bearing premise

That the reviewed correction methods remain applicable and effective when measurement error affects both the outcome and several covariates in real biomedical routine data.

What would settle it

A dataset with known true values in which none of the reviewed methods reduces bias or improves inference relative to naive analysis that ignores the errors.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Analyses of electronic health records and large cohort studies can reduce bias by applying methods that correct outcome and covariate errors together.
  • Clinical decision-making based on routine data can avoid being misled when these correction techniques are used.
  • Researchers can directly compare and select among the reviewed methods using the supplied example data and code.
  • Future methodological work should focus on the gaps identified in the tutorial for this combined error setting.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same correction framework could be tested on data from other fields that rely on noisy administrative records, such as education or economics.
  • Extending the running example to include time-to-event outcomes would show whether the reviewed methods generalize to survival analysis.
  • Simulation studies that vary the magnitude and correlation structure of the errors would quantify when each method performs best.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 3 minor

Summary. The manuscript is a tutorial that reviews available methods for correcting measurement error when it affects both the outcome and multiple covariates. It motivates the need in the context of biomedical routine data (e.g., electronic health records), illustrates the methods via a running example that permits direct comparison, supplies the data and analytic code for reproducibility, and closes with a discussion of the approaches plus areas for future work.

Significance. If the illustrations accurately reflect the cited methods, the tutorial supplies a practical, accessible resource for applied statisticians working with error-prone observational data. The explicit provision of data and code is a clear strength that supports reproducibility and allows readers to verify and adapt the examples. The focus on simultaneous correction for outcome and covariate error addresses a setting that is common yet often underserved by single-error tutorials.

minor comments (3)
  1. [Abstract] Abstract: the statement that 'methods that address errors in the outcome and multiple covariates are needed' would be strengthened by naming the specific families of methods (e.g., regression calibration extensions, SIMEX, Bayesian approaches) that will be illustrated, so readers can immediately gauge coverage.
  2. Running example section: confirm that the measurement-error model assumptions (classical vs. Berkson, independence across variables) are stated explicitly for each method so that the comparison is transparent.
  3. Discussion: the areas of future work should be tied back to concrete limitations observed in the running example (e.g., performance with high-dimensional covariates or non-linear relationships) rather than left at a general level.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the tutorial, its focus on simultaneous correction for outcome and covariate measurement error, and the emphasis on reproducibility through provided data and code. The recommendation for minor revision is noted. No major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity; tutorial review of existing methods

full rationale

The manuscript is a tutorial reviewing and illustrating prior methods for measurement error correction in outcomes and covariates using examples and supplied code. It advances no new derivations, predictions, theorems, or empirical claims whose validity depends on self-referential steps. All statements are descriptive of existing literature, with no load-bearing equations, fitted inputs renamed as predictions, or self-citation chains that reduce the central content to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a review and tutorial paper; it introduces no new free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5659 in / 884 out tokens · 22300 ms · 2026-06-30T08:41:39.982492+00:00 · methodology

0 comments
read the original abstract

Biomedical research is increasingly relying on readily available routine data, such as electronic health records. Routinely collected data, as well as datasets from large cohorts, are often prone to measurement error which, if not addressed in analyses, can bias study results and ultimately mislead clinical decision-making and potentially harm patients. For this setting, methods that address errors in the outcome and multiple covariates are needed. In this tutorial, we will review available methods to address for errors in both outcomes and covariates. We will illustrate methods with use of a running example in order to compare the methods directly. Both the data and analytic code are provided for the user so that they may easily reproduce results in each example. We conclude the tutorial with a discussion of the different approaches and highlight areas of future work needed for this setting.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

137 extracted references · 2 canonical work pages · 1 internal anchor

  1. [1]

    International Journal of Epidemiology , volume=

    Good practices for quantitative bias analysis , author=. International Journal of Epidemiology , volume=. 2014 , publisher=

    2014

  2. [2]

    International Journal of Epidemiology , volume=

    A systematic review of quantitative bias analysis applied to epidemiological research , author=. International Journal of Epidemiology , volume=. 2021 , publisher=

    2021

  3. [3]

    International Journal of Epidemiology , volume=

    Basic methods for sensitivity analysis of biases , author=. International Journal of Epidemiology , volume=. 1996 , publisher=

    1996

  4. [4]

    Spatial measurement error and correction by spatial

    Alexeeff, Stacey E and Carroll, Raymond J and Coull, Brent , journal=. Spatial measurement error and correction by spatial. 2016 , publisher=

    2016

  5. [5]

    Journal of the Royal Statistical Society: Series A (Statistics in Society) , year=

    Two-phase sampling designs for data validation in settings with covariate measurement error and continuous outcome , author=. Journal of the Royal Statistical Society: Series A (Statistics in Society) , year=

  6. [6]

    Statistics in Medicine , year=

    Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model , author=. Statistics in Medicine , year=

  7. [7]

    Journal of Statistical Theory and Practice , volume=

    Bias Correction Methods for Misclassified Covariates in the Cox Model: comparison of five correction methods by simulation and data analysis , author=. Journal of Statistical Theory and Practice , volume=. 2013 , publisher=

    2013

  8. [8]

    Statistical Methods for Medical Research , volume=

    Bootstrap inference for multiple imputation under uncongeniality and misspecification , author=. Statistical Methods for Medical Research , volume=

  9. [9]

    Low-fat dietary pattern and risk of colorectal cancer: the

    Beresford, Shirley AA and Johnson, Karen C and Ritenbaugh, Cheryl and Lasser, Norman L and Snetselaar, Linda G and Black, Henry R and Anderson, Garnet L and Assaf, Annlouise R and Bassford, Tamsen and Bowen, Deborah and others , journal=. Low-fat dietary pattern and risk of colorectal cancer: the. 2006 , publisher=

    2006

  10. [10]

    International Statistical Review/Revue Internationale de Statistique , pages=

    On the variances of asymptotically normal estimators from complex surveys , author=. International Statistical Review/Revue Internationale de Statistique , pages=. 1983 , publisher=

    1983

  11. [11]

    Statistics in Medicine , volume=

    An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures , author=. Statistics in Medicine , volume=. 2021 , publisher=

    2021

  12. [12]

    Journal of Clinical Epidemiology , volume=

    Measurement error is often neglected in medical literature: a systematic review , author=. Journal of Clinical Epidemiology , volume=. 2018 , publisher=

    2018

  13. [13]

    Journal of the Royal Statistical Society: Series C (Applied Statistics) , volume=

    Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis , author=. Journal of the Royal Statistical Society: Series C (Applied Statistics) , volume=. 1999 , publisher=

    1999

  14. [14]

    Optimum Allocation for Adaptive Multi-Wave Sampling in

    Yang, JB and Shepherd, BE and Lumley, T and Shaw, PA , journal=. Optimum Allocation for Adaptive Multi-Wave Sampling in

  15. [15]

    MS Dissertation, University of Essex , year=

    Two-phase sampling designs for multiple outcomes of interest , author=. MS Dissertation, University of Essex , year=

  16. [16]

    Applied Statistics , volume=

    Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis , author=. Applied Statistics , volume=

  17. [17]

    Scandinavian Journal of Statistics , volume=

    Weighted likelihood for semiparametric models and two-phase stratified samples, with application to Cox regression , author=. Scandinavian Journal of Statistics , volume=. 2007 , publisher=

    2007

  18. [18]

    Improved

    Breslow, NE and Lumley, T and Ballantyne, CM and Chambless, LE and Kulich, M , journal=. Improved. 2009 , publisher=

    2009

  19. [19]

    2010 , publisher=

    Measurement Error: Models, Methods, and Applications , author=. 2010 , publisher=

    2010

  20. [20]

    2006 , publisher=

    Measurement Error in Nonlinear Models: A Modern Perspective , author=. 2006 , publisher=

    2006

  21. [21]

    , author=

    Statistical design of calibration studies. , author=. The American Journal of Clinical Nutrition , volume=. 1997 , publisher=

    1997

  22. [22]

    Biometrics , pages=

    Design aspects of calibration studies in nutrition, with analysis of missing data in linear measurement error models , author=. Biometrics , pages=. 1997 , publisher=

    1997

  23. [23]

    Journal of the American Medical Informatics Association , volume=

    Automated identification of implausible values in growth data from pediatric electronic health records , author=. Journal of the American Medical Informatics Association , volume=. 2017 , publisher=

    2017

  24. [24]

    PLOS ONE , volume=

    Measuring the quality of observational study data in an international HIV research network , author=. PLOS ONE , volume=

  25. [25]

    Journal of Clinical and Translational Science , volume=

    Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa , author=. Journal of Clinical and Translational Science , volume=

  26. [26]

    Journal of the American Statistical Association , volume=

    Simulation-extrapolation estimation in parametric measurement error models , author=. Journal of the American Statistical Association , volume=. 1994 , publisher=

    1994

  27. [27]

    Statistics in Medicine , volume=

    Optimal multiwave sampling for regression modeling in two-phase designs , author=. Statistics in Medicine , volume=. 2020 , publisher=

    2020

  28. [28]

    Statistics in Medicine , volume=

    Optimal sampling for design-based estimators of regression models , author=. Statistics in Medicine , volume=. 2022 , publisher=

    2022

  29. [29]

    International Journal of Epidemiology , volume =

    Cole, Stephen R and Chu, Haitao and Greenland, Sander , title =. International Journal of Epidemiology , volume =

  30. [30]

    Journal of the American Statistical Association , volume=

    Calibration estimators in survey sampling , author=. Journal of the American Statistical Association , volume=. 1992 , publisher=

    1992

  31. [31]

    Journal of the American Statistical Association , volume=

    Generalized raking procedures in survey sampling , author=. Journal of the American Statistical Association , volume=. 1993 , publisher=

    1993

  32. [32]

    Biometrics , volume=

    An audit strategy for progression-free survival , author=. Biometrics , volume=

  33. [33]

    American Journal of Epidemiology , volume=

    Does nondifferential misclassification of exposure always bias a true effect toward the null value? , author=. American Journal of Epidemiology , volume=. 1990 , publisher=

    1990

  34. [34]

    Controlled Clinical Trials , volume=

    Power and sample size calculations for studies involving linear regression , author=. Controlled Clinical Trials , volume=. 1998 , publisher=

    1998

  35. [35]

    American Journal of Epidemiology , volume=

    Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data , author=. American Journal of Epidemiology , volume=. 2013 , publisher=

    2013

  36. [36]

    1993 , publisher=

    An introduction to the bootstrap , author=. 1993 , publisher=

    1993

  37. [37]

    American Journal of Epidemiology , volume=

    Regression calibration in studies with correlated variables measured with error , author=. American Journal of Epidemiology , volume=. 2001 , publisher=

    2001

  38. [38]

    Annals of Applied Statistics , volume=

    Accounting for dependent errors in predictors and time-to-event outcomes using electronic health records, validation samples and multiple imputation , author=. Annals of Applied Statistics , volume=. 2020 , publisher=

    2020

  39. [39]

    American Journal of Epidemiology , volume=

    Multiple imputation variance estimation in studies with missing or misclassified inclusion criteria , author=. American Journal of Epidemiology , volume=

  40. [40]

    Statistics in Medicine , volume=

    A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right-censored survival data , author=. Statistics in Medicine , volume=. 2018 , publisher=

    2018

  41. [41]

    Biometrics , volume=

    Measurement error in covariates in the marginal hazards model for multivariate failure time data , author=. Biometrics , volume=. 2004 , publisher=

    2004

  42. [42]

    BMC Medical Research Methodology , volume=

    A double SIMEX approach for bivariate random-effects meta-analysis of diagnostic accuracy studies , author=. BMC Medical Research Methodology , volume=. 2017 , publisher=

    2017

  43. [43]

    2003 , publisher=

    Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments , author=. 2003 , publisher=

    2003

  44. [44]

    Semiparametric time to event models in the presence of error-prone, self-reported outcomes: With application to the

    Gu, Xiangdong and Ma, Yunsheng and Balasubramanian, Raji , journal=. Semiparametric time to event models in the presence of error-prone, self-reported outcomes: With application to the. 2015 , publisher=

    2015

  45. [45]

    Scandinavian Journal of Statistics , volume=

    Combining inverse probability weighting and multiple imputation to improve robustness of estimation , author=. Scandinavian Journal of Statistics , volume=. 2016 , publisher=

    2016

  46. [46]

    Statistical Methods in Medical Research , volume=

    Two-phase analysis and study design for survival models with error-prone exposures , author=. Statistical Methods in Medical Research , volume=. 2021 , publisher=

    2021

  47. [47]

    Statistics in Medicine , volume=

    Combining multiple imputation with raking of weights: An efficient and robust approach in the setting of nearly true models , author=. Statistics in Medicine , volume=. 2021 , publisher=

    2021

  48. [48]

    JASA , year=

    Functional data analysis for sparse longitudinal data , author=. JASA , year=

  49. [49]

    2005 , publisher=

    Functional data analysis , author=. 2005 , publisher=

    2005

  50. [50]

    Statistics in Medicine , volume=

    Accelerated failure time models with covariates subject to measurement error , author=. Statistics in Medicine , volume=. 2007 , publisher=

    2007

  51. [51]

    Pharmaceutical Statistics , volume=

    Attenuation of treatment effect due to measurement variability in assessment of progression-free survival , author=. Pharmaceutical Statistics , volume=. 2012 , publisher=

    2012

  52. [52]

    Journal of the American Statistical Association , volume=

    A generalization of sampling without replacement from a finite universe , author=. Journal of the American Statistical Association , volume=. 1952 , publisher=

    1952

  53. [53]

    Biometrics , pages=

    Estimating the parameters in the Cox model when covariate variables are measured with error , author=. Biometrics , pages=. 1998 , publisher=

    1998

  54. [54]

    Journal of the American Statistical Association , volume=

    Cox regression with accurate covariates unascertainable: A nonparametric-correction approach , author=. Journal of the American Statistical Association , volume=. 2000 , publisher=

    2000

  55. [55]

    Statistica Sinica , pages=

    Errors-in-covariates effect on estimating functions: Additivity in limit and nonparametric correction , author=. Statistica Sinica , pages=. 2006 , publisher=

    2006

  56. [56]

    Clinical Trials , pages=

    Analysis of progression-free survival data using a discrete time survival model that incorporates measurements with and without diagnostic error , author=. Clinical Trials , pages=. 2010 , publisher=

    2010

  57. [57]

    Journal of Exposure Sciences and Environmental Epidemiology , volume=

    Reproducibility of urinary bisphenol A concentrations measured during pregnancy in the Generation R Study , author=. Journal of Exposure Sciences and Environmental Epidemiology , volume=

  58. [58]

    American Journal of Epidemiology , volume=

    Sample size requirements for calibration studies of dietary intake measurements in prospective cohort investigations , author=. American Journal of Epidemiology , volume=. 1995 , publisher=

    1995

  59. [59]

    Statistics in Medicine , volume=

    The role of frailty models and accelerated failure time models in describing heterogeneity due to omitted covariates , author=. Statistics in Medicine , volume=

  60. [60]

    Statistics in Medicine , volume=

    Keogh, Ruth H and Shaw, Pamela A and Gustafson, Paul and Carroll, Raymond J and Deffner, Veronika and Dodd, Kevin W and K. Statistics in Medicine , volume=. 2020 , publisher=

    2020

  61. [61]

    Clinical Trials , year=

    Measurement error in the timing of events: effect on survival analyses in randomized clinical trials , author=. Clinical Trials , year=

  62. [62]

    A general method for dealing with misclassification in regression: the misclassification

    K. A general method for dealing with misclassification in regression: the misclassification. Biometrics , volume=. 2006 , publisher=

    2006

  63. [63]

    Journal of the American Statistical Association , volume=

    Improving the efficiency of relative-risk estimation in case-cohort studies , author=. Journal of the American Statistical Association , volume=. 2004 , publisher=

    2004

  64. [64]

    Statistics in Medicine , volume=

    Effects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable , author=. Statistics in Medicine , volume=. 1988 , publisher=

    1988

  65. [65]

    The Newsletter of the R Project Volume 6/4, October 2006 , pages=

    A short Introduction to the SIMEX and MCSIMEX , author=. The Newsletter of the R Project Volume 6/4, October 2006 , pages=

    2006

  66. [66]

    Journal of Infectious Diseases , volume=

    Race and sex differences in antiretroviral therapy use and mortality among HIV-infected persons in care , author=. Journal of Infectious Diseases , volume=. 2009 , publisher=

    2009

  67. [67]

    Sociological Methods & Research , volume=

    A comparison of three popular methods for handling missing data: complete-case analysis, inverse probability weighting, and multiple imputation , author=. Sociological Methods & Research , volume=. 2024 , publisher=

    2024

  68. [68]

    Biometrika , volume=

    Covariate measurement errors in frailty models for clustered survival data , author=. Biometrika , volume=. 2000 , publisher=

    2000

  69. [69]

    Biometrics , volume=

    Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research cohort , author=. Biometrics , volume=

  70. [70]

    Robustness of semiparametric efficiency in nearly-true models for two-phase samples

    Robustness of semiparametric efficiency in nearly-true models for two-phase samples , author=. arXiv Preprint arXiv:1707.05924 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  71. [71]

    International Statistical Review , volume=

    Connections between survey calibration estimators and semiparametric models for incomplete data , author=. International Statistical Review , volume=. 2011 , publisher=

    2011

  72. [72]

    2011 , publisher=

    Complex surveys: a guide to analysis using R , author=. 2011 , publisher=

    2011

  73. [73]

    Epidemiology , year=

    Validation data-based adjustments for outcome misclassification in logistic regression: an illustration , author=. Epidemiology , year=

  74. [74]

    American Journal of Epidemiology , volume=

    Logistic regression when the outcome is measured with uncertainty , author=. American Journal of Epidemiology , volume=. 1997 , publisher=

    1997

  75. [75]

    Statistics in Medicine , volume=

    Incorporating validation subsets into discrete proportional hazards models for mismeasured outcomes , author=. Statistics in Medicine , volume=

  76. [76]

    , author=

    Adjusting for measurement error in the Cox proportional hazards regression model. , author=. Journal of Cancer Epidemiology and Prevention , volume=

  77. [77]

    Statistics in Medicine , volume=

    Adaptive sampling in two-phase designs: a biomarker study for progression in arthritis , author=. Statistics in Medicine , volume=. 2015 , publisher=

    2015

  78. [78]

    Biometrics , volume=

    Discrete proportional hazards models for mismeasured outcomes , author=. Biometrics , volume=. 2003 , publisher=

    2003

  79. [79]

    Statistical Science , volume=

    Multiple imputation inferences with uncongenial sources of input , author=. Statistical Science , volume=

  80. [80]

    Statistics in Medicine , volume=

    Using multiple frames in health surveys , author=. Statistics in Medicine , volume=. 2009 , publisher=

    2009

Showing first 80 references.