Related references
Note: Only part of the references are listed.Surprise!
Stephen R. Cole et al.
AMERICAN JOURNAL OF EPIDEMIOLOGY (2021)
Two-phase analysis and study design for survival models with error-prone exposures
Kyunghee Han et al.
STATISTICAL METHODS IN MEDICAL RESEARCH (2021)
Bias Analysis Gone Bad
Timothy L. Lash et al.
AMERICAN JOURNAL OF EPIDEMIOLOGY (2021)
Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature
Manuel R. Blum et al.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2020)
Extending inferences from a randomized trial to a target population
Issa J. Dahabreh et al.
EUROPEAN JOURNAL OF EPIDEMIOLOGY (2019)
Hierarchical Semi-Bayes Methods for Misclassification in Perinatal Epidemiology
Richard F. MacLehose et al.
EPIDEMIOLOGY (2018)
Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures
Marc G. Weisskopf et al.
ENVIRONMENTAL HEALTH PERSPECTIVES (2018)
Understanding and misunderstanding randomized controlled trials
Angus Deaton et al.
SOCIAL SCIENCE & MEDICINE (2018)
Biases in Randomized Trials A Conversation Between Trialists and Epidemiologists
Mohammad Ali Mansournia et al.
EPIDEMIOLOGY (2017)
A commentary on 'A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding'
Sander Greenland
STATISTICS IN MEDICINE (2017)
Instrumental variables as bias amplifiers with general outcome and confounding
P. Ding et al.
BIOMETRIKA (2017)
Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses
Miguel A. Hernan et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2016)
From bad to worse: collider stratification amplifies confounding bias in the obesity paradox
Hailey R. Banack et al.
EUROPEAN JOURNAL OF EPIDEMIOLOGY (2015)
All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework
Jessie K. Edwards et al.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2015)
Priors, Parameters, and Probability A Bayesian Perspective on Sensitivity Analysis
Paul Gustafson et al.
EPIDEMIOLOGY (2014)
Good practices for quantitative bias analysis
Timothy L. Lash et al.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2014)
Bayesian inference in partially identified models: Is the shape of the posterior distribution useful?
Paul Gustafson
ELECTRONIC JOURNAL OF STATISTICS (2014)
The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients
Daniel Westreich et al.
AMERICAN JOURNAL OF EPIDEMIOLOGY (2013)
Uncovering selection bias in case-control studies using Bayesian post-stratification
S. Geneletti et al.
STATISTICS IN MEDICINE (2013)
Is Probabilistic Bias Analysis Approximately Bayesian?
Richard F. MacLehose et al.
EPIDEMIOLOGY (2012)
Berkson's Bias, Selection Bias, and Missing Data
Daniel Westreich
EPIDEMIOLOGY (2012)
Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding
Lawrence C. McCandless et al.
STATISTICS IN MEDICINE (2012)
Invited Commentary: Understanding Bias Amplification
Judea Pearl
AMERICAN JOURNAL OF EPIDEMIOLOGY (2011)
Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders
P. Gustafson et al.
BIOMETRICS (2010)
Bayesian adjustment for exposure misclassification in case-control studies
Rong Chu et al.
STATISTICS IN MEDICINE (2010)
Adjusting for selection bias in retrospective, case-control studies
Sara Geneletti et al.
BIOSTATISTICS (2009)
Bayesian Methods for Correcting Misclassification An Example from Birth Defects Epidemiology
Richard F. MacLehose et al.
EPIDEMIOLOGY (2009)
Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods
Sander Greenland
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2009)
Interval Estimation for Messy Observational Data
Paul Gustafson et al.
STATISTICAL SCIENCE (2009)
Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources
Sander Greenland
STATISTICAL SCIENCE (2009)
Maximum-likelihood and closed-form estimators of epidemiologic measures under misclassification
Sander Greenland
JOURNAL OF STATISTICAL PLANNING AND INFERENCE (2008)
Bayesian sensitivity analysis for unmeasured confounding in observational studies
Lawrence C. McCandless et al.
STATISTICS IN MEDICINE (2007)
Inference for case-control studies when exposure status is both informatively missing and misclassified
Robert H. Lyles et al.
STATISTICS IN MEDICINE (2006)
Sensitivity analysis of misclassification: A graphical and a Bayesian approach
Haitao Chu et al.
ANNALS OF EPIDEMIOLOGY (2006)
Multiple-imputation for measurement-error correction
Stephen R. Cole et al.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2006)
Smoothing observational data: A philosophy and implementation for the health sciences
S Greenland
INTERNATIONAL STATISTICAL REVIEW (2006)
Antidepressant use and breast cancer risk
C Chien et al.
BREAST CANCER RESEARCH AND TREATMENT (2006)
A method to automate probabilistic sensitivity analyses of misclassified binary variables
MP Fox et al.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY (2005)
Multiple-bias modelling for analysis of observational data
S Greenland
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY (2005)
A validation study of patient interview data and pharmacy records for antihypertensive, statin, and antidepressant medication use among older women
DM Boudreau et al.
AMERICAN JOURNAL OF EPIDEMIOLOGY (2004)
The value of risk-factor (black-box) epidemiology
S Greenland et al.
EPIDEMIOLOGY (2004)
Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes
DO Scharfstein et al.
BIOSTATISTICS (2003)
Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment
S Greenland
RISK ANALYSIS (2001)
Data, design, and background knowledge in etiologic inference
JM Robins
EPIDEMIOLOGY (2001)
Causation of bias: The episcope
M Maclure et al.
EPIDEMIOLOGY (2001)
Share Paper
