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Research

Shaw-Shepherd Research Collaborative

The primary focus of our research is methods development for a robust and efficient analysis of data from two phase studies. Below is a selection of recent papers from our group, which is loosely organized into those papers more focussed on estimation and those more focussed on efficient study design. Since estimation and design are integrally related, several papers may address both of these issues. A few members of our lab have also been involved in reviews, tutorials and book chapters, which seek to educate the broader research community of statisticians, epidemiologists, and public health scientists on methods and study designs that are appropriate in studies with data prone to misclassification or measurement error. Some of these works also draw attention to misunderstandings and less than ideal analytical approaches that are commonly appearing in the applied literature and offer practical alternatives to achieve more reliable inference. Dr. Shaw is an active member of the STRATOS initiative Working Group TG4. STRATOS is an international collaborative of researchers focused on addressing shortcomings that commonly appear in analyses of observational studies in the applied literature. Several review papers were done in collaboration with the STRATOS TG4 working group.

  • Raking

    Oh E, Shepherd BE, Lumley T, Shaw PA: Improved Generalized Raking Estimators to Address Dependent Covariate and Failure-Time Outcome Error. Biometrical Journal 2021 Jun; 63(5):1006-27.

    Oh EJ, Shepherd BE, Lumley T, Shaw PA. Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error. Statistics in Medicine 2021 Feb 10;40(3):631-49.

  • Semi-parametric Maximum Likelihood (SMLE)

    Lotspeich SC, Shepherd BE, Amorim GGC, Shaw PA, Tau R. Efficient odds ratio estimation using error-prone data from a multi-national HIV research cohort. Biometrics 2021 July;Epub ahead of print.

    Tau R, Lotspeich SC, Shaw PA, Shepherd BE. Efficient semiparametric inference for two-phase studies with outcome and covariate measurement errors. Statistics in Medicine, 2021 Feb 10;40(3):725-38.

  • Multiple Imputation

    Giganti MJ, Shaw PA, Chen G, Bebawy SS, Turner MM, Sterling TR, Shepherd BE: Accounting for dependent errors in predictors and time-to-event outcomes using validation samples and multiple imputation Annals of Applied Statistics 2020;14(2):1045-61.

    Shepherd B, Shaw PA and Dodd L. Using audit Information to adjust parameter estimates for data errors in clinical trials. Clinical Trials, 2012 Dec; 9(6): 721-729.

  • Regression calibration

    Boe LA, Tinker LF, Shaw PA: An Approximate Quasi-Likelihood Approach for Error-Prone Failure Time Outcomes and Exposures. To appear in Statistics in Medicine,  Pre-print, arXiv:2004.01112 [stat.ME].

    Baldoni P, Sotres-Alvarez D, Lumley TS, Shaw PA. On the use of Regression Calibration in a Complex Sampling Design with Application to the Hispanic Community Health Study/Study of Latinos.  American Journal of Epidemiology  2021 July; 190(7): 1366–1376.

    Oh EJ, Shepherd BE, Lumley T, Shaw PA. Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error. Statistics in Medicine 2021 Feb 10;40(3):631-49.

    Illenberger N, Small DS, and Shaw PA. Understanding regression to the mean in the context of synthetic controls and other matched difference in difference methods. Epidemiology 2020; 31(6):815-22.

    Shaw PA, He J, and Shepherd B. Regression calibration to correct correlated errors in outcome and exposure. Statistics in Medicine,  2021 Jan 30;40(2):271-86.

    Shepherd B, Shaw PA and Dodd L. Using audit Information to adjust parameter estimates for data errors in clinical trials. Clinical Trials, 2012 Dec; 9(6): 721-729.

    Shaw PA, Prentice, RL. Hazard ratio estimation for biomarker-calibrated dietary exposures. Biometrics, 2012 Jun; 68(2): 397-407.

  • Efficient two-phase study design

    Yang JB, Shepherd BE, Lumley T, Shaw PA. Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall. arXiv preprint arXiv:2106.09494. 2021 Jun 17.

    Chen T, Lumley T. Optimal multiwave sampling for regression modeling in two‐phase designs. Statistics in Medicine. 2020 Dec 30;39(30):4912-21.

    Amorim G, Tau R, Lotspeich SC, Shaw PA, Lumley T, Shepherd BE. Two-Phase Sampling Designs for Data Validation in Settings with Covariate Measurement Error and Continuous Outcome. To appear in Journal of the Royal Statistical Society, Series A,

    Han K, Lumley T, Shepherd BE, Shaw PA: Two-phase analysis and study design for survival models with error-prone exposures.  Statistical Methods in Medical Research 2021 Mar;30(3):857-74.

  • Reviews

    Shepherd BE, Shaw PA. Errors in multiple variables in HIV cohort and electronic health record data: statistical challenges and opportunities. Invited paper: Statistical Communications in Infectious Disease, 2020; 12(S1), pp. 20190015. https://doi.org/10.1515/scid-2019-0015

    Freedman LS and Shaw PA.: On the formation and use of calibration equations in nutritional epidemiology- Discussion of the Paper by R. L. Prentice and Y. Huang. Statistical Theory and Related Fields, 2018; 2(1): 11-13.

    Anderson GL, Burns CJ, Larsen J, Shaw PA. Use of administrative data to increase the practicality of clinical trials: Insights from the Women’s Health Initiative. Clinical Trials, 2016 Oct; 13(5):519-26.

    Lumley T, Shaw PA, Dai JY. Connections between survey calibration estimators and semiparametric models for incomplete data. International Statistical Review, 2011, 79 (2): 200–220.

  • STRATOS Guidance papers

    Keogh RH, Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, Küchenhoff H, Tooze JA, Wallace MP, Kipnis V, Freedman LS. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part I – basic theory, validation studies and simple methods of adjustment. Statistics in Medicine 2020 Jul 20;39(16):2197-2231.

    Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, Keogh RH, Kipnis V, Tooze JA, Wallace MP, Küchenhoff H, Freedman LS. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part II –more complex methods of adjustment and advanced topics.  Statistics in Medicine 2020 Jul 20;39(16):2232-2263.

  • Book chapters

    Shaw PA. Regression calibration for covariate measurement error. Chapter in: Handbook of Measurement Error Models, Editors: Grace Yi, Aurore Delaigle, Paul Gustafson. Chapman and Hall/CRC, To appear September 2021.

    Mossavar-Rahmani Y, Tinker LF, Neuhouser ML, Huang Y, Shaw P, Beasley JM, Di C, Zheng C, Li W, Johnson K, and Prentice RL. Women’s Health Initiative Dietary Modification Trial: Update and Application of Biomarker Calibration to Self-Report Measures of Diet and Physical Activity In: N. Balakrishnan, ed., Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs. John Wiley and Sons; 2013.