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- Bayesian methods (1)
- Case-control studies (1)
- Causal inference (1)
- Confounding (1)
- Covariate balance (1)
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- Diagnostic (1)
- Joint exposure (1)
- Latent class model (1)
- Local dependence (1)
- Measuremet error (1)
- Model-based clustering (1)
- Partially-identified models (1)
- Pneumonia etiology (1)
- Sensitivity analysis; partial identification; asymptotic linearity; bias analysis; statistical inference; causal inference (1)
- Time-varying exposure (1)
Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Nested Partially-Latent, Class Models For Dependent Binary Data, Estimating Disease Etiology, Zhenke Wu, Maria Deloria-Knoll, Scott L. Zeger
Nested Partially-Latent, Class Models For Dependent Binary Data, Estimating Disease Etiology, Zhenke Wu, Maria Deloria-Knoll, Scott L. Zeger
Johns Hopkins University, Dept. of Biostatistics Working Papers
The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. The paper describes a latent variable model designed to infer from case-control data the etiology distribution for the population of cases, and for an individual case given his or her measurements. We assume each observation is drawn from a mixture model for which each component represents one cause or disease class. The model addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcome …
The Statistics Of Sensitivity Analyses, Alexander R. Luedtke, Ivan Diaz, Mark J. Van Der Laan
The Statistics Of Sensitivity Analyses, Alexander R. Luedtke, Ivan Diaz, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Suppose one wishes to estimate a causal parameter given a sample of observations. This requires making unidentifiable assumptions about an underlying causal mechanism. Sensitivity analyses help investigators understand what impact violations of these assumptions could have on the causal conclusions drawn from a study, though themselves rely on untestable (but hopefully more interpretable) assumptions. Díaz and van der Laan (2013) advocate the use of a sequence (or continuum) of interpretable untestable assumptions of increasing plausibility for the sensitivity analysis so that experts can have informed opinions about which are true. In this work, we argue that using appropriate statistical procedures …
A General Framework For Diagnosing Confounding Of Time-Varying And Other Joint Exposures, John W. Jackson
A General Framework For Diagnosing Confounding Of Time-Varying And Other Joint Exposures, John W. Jackson
Harvard University Biostatistics Working Paper Series
No abstract provided.