A comparison of different methods for handling measurements affected by medication use
暂无分享,去创建一个
O. Dekkers | S. le Cessie | J. Choi | J. Choi
[1] J. A. Delaney,et al. A method to account for covariate-specific treatment effects when estimating biomarker associations in the presence of endogenous medication use , 2018, Statistical methods in medical research.
[2] Maarten van Smeden,et al. Random measurement error: Why worry? An example of cardiovascular risk factors , 2018, PloS one.
[3] R. Hanson,et al. Effect of different methods of accounting for antihypertensive treatment when assessing the relationship between diabetes or obesity and systolic blood pressure. , 2017, Journal of diabetes and its complications.
[4] Sylvie Chevret,et al. A multiple imputation approach for MNAR mechanisms compatible with Heckman's model , 2016, Statistics in medicine.
[5] J. A. Delaney,et al. Evaluating the treatment effects model for estimation of cross‐sectional associations between risk factors and cardiovascular biomarkers influenced by medication use , 2015, Pharmacoepidemiology and drug safety.
[6] Margreet Kloppenburg,et al. The Netherlands Epidemiology of Obesity (NEO) study: study design and data collection , 2013, European Journal of Epidemiology.
[7] Stef van Buuren,et al. MICE: Multivariate Imputation by Chained Equations in R , 2011 .
[8] M. Tobin,et al. Pharmacogenetic interactions and their potential effects on genetic analyses of blood pressure , 2011, Statistics in medicine.
[9] S. Cole,et al. Illustrating bias due to conditioning on a collider. , 2010, International journal of epidemiology.
[10] Robyn L McClelland,et al. Estimation of risk factor associations when the response is influenced by medication use: An imputation approach , 2008, Statistics in medicine.
[11] Theo Stijnen,et al. Using the outcome for imputation of missing predictor values was preferred. , 2006, Journal of clinical epidemiology.
[12] Ian R White,et al. Commentary: dealing with measurement error: multiple imputation or regression calibration? , 2006, International journal of epidemiology.
[13] Nuala A Sheehan,et al. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure , 2005, Statistics in medicine.
[14] J. Robins,et al. A Structural Approach to Selection Bias , 2004, Epidemiology.
[15] Ian R White,et al. The use of regression models for medians when observed outcomes may be modified by interventions , 2003, Statistics in medicine.
[16] S. Harrap,et al. Antihypertensive Treatments Obscure Familial Contributions to Blood Pressure Variation , 2003, Hypertension.
[17] J. Pankow,et al. Genome Scans for Blood Pressure and Hypertension: The National Heart, Lung, and Blood Institute Family Heart Study* , 2002, Hypertension.
[18] C. Lewis,et al. Treatment of Mild Hypertension Study: Final Results , 1993 .
[19] D. Relles,et al. Theory Testing in a World of Constrained Research Design , 1990 .
[20] Finis Welch,et al. What Do We Really Know about Wages? The Importance of Nonreporting and Census Imputation , 1986, Journal of Political Economy.