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10.1186/s12874-017-0338-0 http://scihub22266oqcxt.onion/10.1186/s12874-017-0338-0 C5408373!5408373!28454568     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Res+Methodol 2017 ; 17 (ä): ä Nephropedia Template TP {{tp|p=28454568|t=2017. Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance |pdf=|usr=}}{{28454568}} gab.com Text Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance JO: BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=28454568 #FOAvip Background: Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods: We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results: We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion: If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering. Electronic supplementary material: The online version of this article (doi:10.1186/s12874-017-0338-0) contains supplementary material, which is available to authorized users. Twit Text FOAVip Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance ????BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=28454568 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28454568 #FOAvip English Wikipedia <ref>{{Cite pmid|28454568}}</ref> Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance #MMPMID28454568 Nguyen TL; Collins GS; Spence J; Daurès JP; Devereaux PJ; Landais P; Le Manach Y BMC Med Res Methodol 2017[]; 17 (ä): ä PMID28454568show ga Background: Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods: We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results: We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion: If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering. Electronic supplementary material: The online version of this article (doi:10.1186/s12874-017-0338-0) contains supplementary material, which is available to authorized users. äDouble-adjustment in propensity score matching analysis: choosing a th(epmc).pdf DeepDyve Pubget Overpricing