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May 1, 2014 · In this book, we use an imperfect but practical term “exposure” to denote a wide range of variables that may cause health indicators or may be associated with variables that cause health indicators.
1. Introduction. Missing data are inevitable and ubiquitous in medical and social research. They often complicate the analysis and cause consternation in the study team. Yet there have been substantial methodological developments in the analysis of partially observed datasets, and there are now many available approaches.
The term “significant events” is a summary of the definition given by the EU-BSS for unintended and accidental overexposures in Article 4 (99): medical exposure that is significantly different from the medical exposure intended for a given purpose.
Missing exposure information is a very common feature of many observational studies. Here we study identifiability and efficient estimation of causal effects on vector outcomes, in such cases where treatment is unconfounded but partially missing.
- Edward H Kennedy
- 2020
Nov 20, 2017 · Throughout this paper we focus on explicit missing data, characterized by missing values for the exposure, outcome, or covariates in an analytical data set.
In epidemiology, the term “exposure” can be broadly applied to any factor that may be associated with an outcome of interest. When using observational data sources, researchers often rely on readily available (existing) data elements to identify whether individuals have been exposed to a factor of interest.
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Dec 5, 2013 · In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and...
