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10.1089/aid.2014.0037 http://scihub22266oqcxt.onion/10.1089/aid.2014.0037 C4250953!4250953!25183195     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       AIDS+Res+Hum+Retroviruses 2014 ; 30 (12): 1170-7 Nephropedia Template TP {{tp|p=25183195|t=2014. Worth the Weight: Using Inverse Probability Weighted Cox Models in AIDS Research |pdf=|usr=}}{{25183195}} gab.com Text Worth the Weight: Using Inverse Probability Weighted Cox Models in AIDS Research JO: AIDS Res Hum Retroviruses http://www.kidney.de/pget.php?&tw=1&pmid=25183195 #FOAvip In an observational study with a time-to-event outcome, the standard analytical approach is the Cox proportional hazards regression model. As an alternative to the standard Cox model, in this article we present a method that uses inverse probability (IP) weights to estimate the effect of a baseline exposure on a time-to-event outcome. IP weighting can be used to adjust for multiple measured confounders of a baseline exposure in order to estimate marginal effects, which compare the distribution of outcomes when the entire population is exposed versus when the entire population is unexposed. For example, IP-weighted Cox models allow for estimation of the marginal hazard ratio and marginal survival curves. IP weights can also be employed to adjust for selection bias due to loss to follow-up. This approach is illustrated using an example that estimates the effect of injection drug use on time until AIDS or death among HIV-infected women. Twit Text FOAVip Worth the Weight: Using Inverse Probability Weighted Cox Models in AIDS Research ????AIDS Res Hum Retroviruses http://www.kidney.de/pget.php?&tw=1&pmid=25183195 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25183195 #FOAvip English Wikipedia <ref>{{Cite pmid|25183195}}</ref> Worth the Weight: Using Inverse Probability Weighted Cox Models in AIDS Research #MMPMID25183195 Buchanan AL; Hudgens MG; Cole SR; Lau B; Adimora AA AIDS Res Hum Retroviruses 2014[Dec]; 30 (12): 1170-7 PMID25183195show ga In an observational study with a time-to-event outcome, the standard analytical approach is the Cox proportional hazards regression model. As an alternative to the standard Cox model, in this article we present a method that uses inverse probability (IP) weights to estimate the effect of a baseline exposure on a time-to-event outcome. IP weighting can be used to adjust for multiple measured confounders of a baseline exposure in order to estimate marginal effects, which compare the distribution of outcomes when the entire population is exposed versus when the entire population is unexposed. For example, IP-weighted Cox models allow for estimation of the marginal hazard ratio and marginal survival curves. IP weights can also be employed to adjust for selection bias due to loss to follow-up. This approach is illustrated using an example that estimates the effect of injection drug use on time until AIDS or death among HIV-infected women. äWorth the Weight: Using Inverse Probability Weighted Cox Models in AID(epmc).pdf DeepDyve Pubget Overpricing