2017
DOI: 10.1086/691464
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Improving Causal Inference: Recommendations for Covariate Selection and Balance in Propensity Score Methods

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Cited by 41 publications
(51 citation statements)
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“…An advantage of the PS analysis is that it allows the balance of covariates to be reviewed (e.g. using standardised differences) [ 35 , 36 ]. Achieving covariate balance strengthens the internal validity of our study and gives greater confidence that any effect observed may be due to the causal relationship between PCV13 and protection from hypoxic pneumonia [35] .…”
Section: Discussionmentioning
confidence: 99%
“…An advantage of the PS analysis is that it allows the balance of covariates to be reviewed (e.g. using standardised differences) [ 35 , 36 ]. Achieving covariate balance strengthens the internal validity of our study and gives greater confidence that any effect observed may be due to the causal relationship between PCV13 and protection from hypoxic pneumonia [35] .…”
Section: Discussionmentioning
confidence: 99%
“…In acute stroke, it is challenging to evaluate the effects of care on delirium independent of stroke severity because the need for care depends on it. Analysis using PS enables causal inference in observational studies by creating quasi-experimental comparability [24,27,29,32]. The results of this study thus provide evidence that some kinds of care are responsible for the development of delirium in patients with acute stroke and that reducing the amount of such care can prevent delirium.…”
Section: Discussionmentioning
confidence: 83%
“…In PS analysis, valid inference relies on a set of vital assumptions, no unmeasured confounding, and positivity (true PS should not be 0 or 1) [24,27,32]. We believe unmeasured confounding was minimal in this study because we prospectively measured a comprehensive set of known as well as potential predictors of delirium.…”
Section: Discussionmentioning
confidence: 99%
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“…It is an effect size where the difference in covariate means between two groups is divided by the spread in means (Kainz et al 2017;Stuart 2010). The literature often refers to a threshold value of 0.1 (e.g., Haukoos and Lewis 2015;Kainz et al 2017). Standardised biases below this threshold indicate negligible imbalance whereas values exceeding this limit would point to considerable pre-event differences between both groups.…”
Section: Analytical Strategymentioning
confidence: 99%