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10.1002/sim.6842 http://scihub22266oqcxt.onion/10.1002/sim.6842 C4848146!4848146!26678242     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2016 ; 35 (12): 1985-99 Nephropedia Template TP {{tp|p=26678242|t=2016. Propensity score and doubly robust methods for estimating the effect of treatment on censored cost |pdf=|usr=}}{{26678242}} gab.com Text Propensity score and doubly robust methods for estimating the effect of treatment on censored cost JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26678242 #FOAvip The estimation of treatment effects on medical costs is complicated by the need to account for informative censoring, skewness and the effects of confounders. Since medical costs are often collected from observational claims data, we investigate propensity score (PS) methods such as covariate adjustment, stratification and inverse probability weighting taking into account informative censoring of the cost outcome. We compare these more commonly used methods to doubly robust estimation (DR). We then use a machine learning approach called Super-Learner (SL) to choose among conventional cost models to estimate regression parameters in the DR approach and to choose among various model specifications for PS estimation. Our simulation studies show that when the PS model is correctly specified, weighting and DR perform well. When the PS model is misspecified, the combined approach of DR with SL can still provide unbiased estimates. SL is especially useful when the underlying cost distribution comes from a mixture of different distributions or when the true PS model is unknown. We apply these approaches to a cost analysis of two bladder cancer treatments, cystectomy versus bladder preservation therapy, using SEER-Medicare data. Twit Text FOAVip Propensity score and doubly robust methods for estimating the effect of treatment on censored cost ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26678242 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26678242 #FOAvip English Wikipedia <ref>{{Cite pmid|26678242}}</ref> Propensity score and doubly robust methods for estimating the effect of treatment on censored cost #MMPMID26678242 Li J; Handorf E; Bekelman J; Mitra N Stat Med 2016[May]; 35 (12): 1985-99 PMID26678242show ga The estimation of treatment effects on medical costs is complicated by the need to account for informative censoring, skewness and the effects of confounders. Since medical costs are often collected from observational claims data, we investigate propensity score (PS) methods such as covariate adjustment, stratification and inverse probability weighting taking into account informative censoring of the cost outcome. We compare these more commonly used methods to doubly robust estimation (DR). We then use a machine learning approach called Super-Learner (SL) to choose among conventional cost models to estimate regression parameters in the DR approach and to choose among various model specifications for PS estimation. Our simulation studies show that when the PS model is correctly specified, weighting and DR perform well. When the PS model is misspecified, the combined approach of DR with SL can still provide unbiased estimates. SL is especially useful when the underlying cost distribution comes from a mixture of different distributions or when the true PS model is unknown. We apply these approaches to a cost analysis of two bladder cancer treatments, cystectomy versus bladder preservation therapy, using SEER-Medicare data. äPropensity score and doubly robust methods for estimating the effect o(epmc).pdf DeepDyve Pubget Overpricing