Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores.

By Sharon-Lise T. Normand, M. Landrum, E. Guadagnoli, John Z. Ayanian, T. J. Ryan, P. Cleary, Barbara J. McNeil. Journal of Clinical Epidemiology, 2001

1,230 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/85b8f3325bf6fa27fe99324bb48613a39e65ffc7

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Citations in the corpus

Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research (2009, background): “Some authors have suggested that, in many cases, standardized differences that are less 10% can indicate negligible imbalance in a baseline covariate between treated and untreated subjects (Austin, 2007, 2008a; Normand et al., 2001).”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, background): “1 has been taken to indicate a negligible difference in the mean or prevalence of a covariate between treatment groups (Normand et al., 2001).”
Propensity score matching strategies for evaluating substance abuse services for child welfare clients (2006, background): “, 2001; Gum, Thamilarasan, Watanabe, Blackstone, & Lauer, 2001), epidemiology (Normand et al., 2001), education research (Morgan, 2001), sociology (Smith, 1997), and psychology (Jones, D’Agostino, Gondolf, & Heckert, 2004).”
Evaluation of subset matching methods and forms of covariate balance (2016, background): “[36]), but the fact that two covariates have distributions with similar first moments does not imply that differences in other moments do not introduce bias in the effect estimates (see Sections 4 and 5; a related formal argument is Proposition 4.”
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, background): “1 absolute standardized differences or less (Normand et al. 2001), but again this choice is problem-specific.”
Propensity Score Analysis: Recent Debate and Discussion (2020, methodology): “463 The PSA approach has been used in a variety of disciplines and professions, including epidemiology (e.g., Normand et al., 2001), medicine (e.g., Gum et al., 2001), psychology (e.g., Jones et al., 2004), social work (e.g., Guo et al., 2006), and sociology (e.g., Morgan, 2001).”
A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003 (2008, background): “6 of the standard deviation of the logit of the propensity score [52, 69].”
Covariate balancing propensity score by tailored loss functions (2016, background): “A widely used criterion is that a standardized difference above 10% is unacceptable (Austin and Stuart, 2015, Normand et al., 2001), which is the dashed line in Figure 1.”
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies (2015, background): “While there is no consensus as to what value of a standardized difference can be taken to indicate the presence of meaningful confounding, some authors have suggested that a standardized difference in excess of 10% may be indicative of meaningful imbalance in a covariates between treated and control”
Design sensitivity in observational studies (2004, methodology): “For several recent applications of this method of sensitivity analysis, see Aakvik (2001), Li et al. (2001) and Normand et al. (2001).”
The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments (2013, background): “1 to indicate imbalance in baseline covariates that is of potential concern [56].”