Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting
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...
متن کاملInverse probability weighting with error-prone covariates.
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. ...
متن کاملCombining Multiple Imputation and Inverse-Probability Weighting
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...
متن کاملSpatial Interpolation via Inverse Path Distance Weighting
The R package ipdw provides functions for interpolation of georeferenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances. This method of interpolation requires significant computation and is only practical for relatively small and coarse grids. The ipdw implementation may provi...
متن کاملMissing confounding data in marginal structural models: a comparison of inverse probability weighting and multiple imputation.
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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Basic <html_ent glyph="@amp;" ascii="&"/> Clinical Pharmacology <html_ent glyph="@amp;" ascii="&"/> Toxicology
سال: 2006
ISSN: 1742-7835,1742-7843
DOI: 10.1111/j.1742-7843.2006.pto_329.x