multipleNCC: Inverse Probability Weighting of Nested Case-Control Data
نویسندگان
چکیده
منابع مشابه
Inverse probability weighting.
Statistical analysis usually treats all observations as equally important. In some circumstances, however, it is appropriate to vary the weight given to different observations. Well known examples are in meta-analysis, where the inverse variance (precision) weight given to each contributing study varies, and in the analysis of clustered data. Differential weighting is also used when different p...
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Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. ...
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Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate outcome), MI needs a model for the joint distribut...
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In this document I describe a re-analysis of data from the Southampton Women’s Survey (SWS). The original complete-case analysis implicitly assumed that the data are missing completely at random. Inverse-probability weighting (IPW) and multiple imputation (MI) are more sophisticated methods for handling missing data, which make the weaker assumption that the data are missing at random. We sough...
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Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision. The issue of missing data for inverse probability of treatment weighted estimation of marginal structural models (MSMs) has often been addressed, though little has been done to compa...
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ژورنال
عنوان ژورنال: The R Journal
سال: 2016
ISSN: 2073-4859
DOI: 10.32614/rj-2016-030