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For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate.
For interventions that target health and well-being, the physiological or psychological basis for the mechanism of action, whether known or hypothesized, should guide the development of the exposure definition.
- Todd A Lee, A Simon Pickard
- 2013/01
- 2013
May 1, 2014 · Exposures are variables that we wish to evaluate for their potential relation with adverse health outcomes or variables that denote groups at particularly higher risk for adverse health. For example, we consider biological sex an exposure that may be associated with adverse health outcomes.
Feb 24, 2021 · For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate.
Jun 29, 2009 · When it is plausible that data are missing at random, but not completely at random, analyses based on complete cases may be biased. Such biases can be overcome using methods such as multiple imputation that allow individuals with incomplete data to be included in analyses.
- Jonathan A C Sterne, Ian R White, John B Carlin, Michael Spratt, Patrick Royston, Michael G Kenward,...
- 2009
From a selection bias perspective, restricting on C will amount to simple random sampling within level of exposure; from a missing data perspective, data are missing at random, or completely at random within level of exposure.
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Jun 1, 2015 · The aims of this article are to (1) demonstrate the potential pitfalls of attempting to correct randomisation errors and (2) provide guidance on handling common randomisation errors when they are discovered that maintains the goals of the intention-to-treat principle.
