scholarly journals Controlling for confounding via propensity score methods can result in biased estimation of the conditional AUC: A simulation study

2019 ◽  
Vol 18 (5) ◽  
pp. 568-582
Author(s):  
Hadiza I. Galadima ◽  
Donna K. McClish
Keyword(s):  
Propensity Score ◽  
Simulation Study ◽  
Biased Estimation ◽  
Propensity Score Methods

scholarly journals Evaluating uses of data mining techniques in propensity score estimation: a simulation study

2008 ◽  
Vol 17 (6) ◽  
pp. 546-555 ◽  
Author(s):  
Soko Setoguchi ◽  
Sebastian Schneeweiss ◽  
M. Alan Brookhart ◽  
Robert J. Glynn ◽  
E. Francis Cook
Keyword(s):  
Data Mining ◽  
Propensity Score ◽  
Simulation Study ◽  
Data Mining Techniques

scholarly journals Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis

2018 ◽  
Vol 28 (12) ◽  
pp. 3534-3549 ◽  
Author(s):  
Arman Alam Siddique ◽  
Mireille E Schnitzer ◽  
Asma Bahamyirou ◽  
Guanbo Wang ◽  
Timothy H Holtz ◽  
...  
Keyword(s):  
Propensity Score ◽  
Simulation Study ◽  
Antimicrobial Agents ◽  
Treatment Success ◽  
Likelihood Estimation ◽  
Multidrug Resistant ◽  
Patient Treatment ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Propensity Score Adjustment

This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.

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