BLINPLUS

The SAS %BLINPLUS Macro. The macro %blinplus corrects for measurement error in one or more model covariates logistic regression coefficients, their standard errors, and odds ratios and 95% confidence intervals for a biologically meaningful difference specified by the user (the ”weights”). Regression model parameters from Cox models (PROC PHREG) and linear regression models (PROC REG) can also be corrected. A validation study is required to empirically characterize the measurement error model. Options are given for main study/external validation study designs, and main study/internal validation study designs (Spiegelman, Carrol, Kipnis; 2001). Technical details are given in Rosner et al. (1989), Rosner et al. (1990), and Spiegelman et all (1997).


References in zbMATH (referenced in 32 articles )

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  1. Linda Nab, Maarten van Smeden, Ruth H. Keogh, Rolf H.H. Groenwold: mecor: An R package for measurement error correction in linear regression models with a continuous outcome (2021) arXiv
  2. Chen, Xia; Mao, Liyue: Penalized empirical likelihood for partially linear errors-in-variables models (2020)
  3. Johnson, Nels; Kim, Inyoung: Generalized linear models with covariate measurement error and unknown link function (2017)
  4. Shen, Junshan; Li, Zhaonan; Yu, Hanjun; Fang, Xiangzhong: Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data (2017)
  5. Yu, Chang; Zhang, Sanguo; Friedenreich, Christine; Matthews, Charles E.: Using repeated measures to correct correlated measurement errors through orthogonal decomposition (2017)
  6. Moffatt, Joanne L.; Scarf, Phil: Sequential regression measurement error models with application (2016)
  7. Wang, Le; Shaw, Pamela A.; Mathelier, Hansie M.; Kimmel, Stephen E.; French, Benjamin: Evaluating risk-prediction models using data from electronic health records (2016)
  8. Bang, Heejung; Chiu, Ya-Lin; Kaufman, Jay S.; Patel, Mehul D.; Heiss, Gerardo; Rose, Kathryn M.: Bias correction methods for misclassified covariates in the Cox model: comparison of five correction methods by simulation and data analysis (2013)
  9. Guo, Ying; Prof. Little, Roderick J.: Bayesian multiple imputation for assay data subject to measurement error (2013)
  10. Huang, Xianzheng; Zhang, Hongmei: Variable selection in linear measurement error models via penalized score functions (2013)
  11. Lyles, Robert H.; Kupper, Lawrence L.: Approximate and pseudo-likelihood analysis for logistic regression using external validation data to model log exposure (2013)
  12. Thomas, Laine; Stefanski, Leonard A.; Davidian, Marie: Moment adjusted imputation for multivariate measurement error data with applications to logistic regression (2013)
  13. Wang, Molin; Liao, Xiaomei; Spiegelman, Donna: Can efficiency be gained by correcting for misclassification? (2013)
  14. Wang, Qihua; Cui, Wenquan: Probability density estimation with surrogate data and validation sample (2013)
  15. Lee, Shen-Ming; Li, Chin-Shang; Hsieh, Shu-Hui; Huang, Li-Hui: Semiparametric estimation of logistic regression model with missing covariates and outcome (2012)
  16. Skrondal, Anders; Kuha, Jouni: Improved regression calibration (2012)
  17. Qiu, Weiliang; Rosner, Bernard: Measurement error correction for the cumulative average model in the survival analysis of nutritional data: application to nurses’ health study (2010)
  18. Xiao, Zhiguo; Shao, Jun; Palta, Mari: GMM in linear regression for longitudinal data with multiple covariates measured with error (2010)
  19. Dupuy, Jean-François; Leconte, Eve: A study of regression calibration in a partially observed stratified Cox model (2009)
  20. Liang, Hua; Song, Weixing: Improved estimation in multiple linear regression models with measurement error and general constraint (2009)

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