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10.1097/MLR.0000000000000011 http://scihub22266oqcxt.onion/10.1097/MLR.0000000000000011 C4043938!4043938!24309675     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       Med+Care 2016 ; 54 (4): e23-9 Nephropedia Template TP {{tp|p=24309675|t=2016. Distinguishing selection bias and confounding bias in comparative effectiveness research |pdf=|usr=}}{{24309675}} gab.com Text Distinguishing selection bias and confounding bias in comparative effectiveness research JO: Med Care http://www.kidney.de/pget.php?&tw=1&pmid=24309675 #FOAvip Comparative effectiveness research (CER) aims to provide patients and physicians with evidence-based guidance on treatment decisions. As researchers conduct CER they face myriad challenges. While inadequate control of confounding is the most-often cited source of potential bias, selection bias which arises when patients are differentially excluded from analyses is a distinct phenomenon with distinct consequences: confounding bias compromises internal validity while selection bias compromises external validity. Despite this distinction, however, the label ?treatment-selection bias? is being used in the CER literature to denote the phenomenon of confounding bias. Motivated by an on-going study of treatment choice for depression on weight change over time, we formally distinguish confounding and selection bias in CER. By formally distinguishing selection and confounding bias we clarify important scientific, design and analysis issues relevant to ensuring validity. First is that the two types of bias may arise simultaneously in any given study; even if confounding bias is completely controlled, a study may nevertheless suffer from selection bias so that the results are not generalizable to the patient population of interest. Second is that statistical methods used to mitigate the two biases are themselves distinct; methods developed to control one type of bias should not be expected to address the other. Finally, the control of selection and confounding bias will often require distinct covariate information. Consequently, as researchers plan future studies of comparative effectiveness, care must be taken to ensure that all data elements relevant to both confounding and selection bias are collected. Twit Text FOAVip Distinguishing selection bias and confounding bias in comparative effectiveness research ????Med Care http://www.kidney.de/pget.php?&tw=1&pmid=24309675 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24309675 #FOAvip English Wikipedia <ref>{{Cite pmid|24309675}}</ref> Distinguishing selection bias and confounding bias in comparative effectiveness research #MMPMID24309675 Haneuse S Med Care 2016[Apr]; 54 (4): e23-9 PMID24309675show ga Comparative effectiveness research (CER) aims to provide patients and physicians with evidence-based guidance on treatment decisions. As researchers conduct CER they face myriad challenges. While inadequate control of confounding is the most-often cited source of potential bias, selection bias which arises when patients are differentially excluded from analyses is a distinct phenomenon with distinct consequences: confounding bias compromises internal validity while selection bias compromises external validity. Despite this distinction, however, the label ?treatment-selection bias? is being used in the CER literature to denote the phenomenon of confounding bias. Motivated by an on-going study of treatment choice for depression on weight change over time, we formally distinguish confounding and selection bias in CER. By formally distinguishing selection and confounding bias we clarify important scientific, design and analysis issues relevant to ensuring validity. First is that the two types of bias may arise simultaneously in any given study; even if confounding bias is completely controlled, a study may nevertheless suffer from selection bias so that the results are not generalizable to the patient population of interest. Second is that statistical methods used to mitigate the two biases are themselves distinct; methods developed to control one type of bias should not be expected to address the other. Finally, the control of selection and confounding bias will often require distinct covariate information. Consequently, as researchers plan future studies of comparative effectiveness, care must be taken to ensure that all data elements relevant to both confounding and selection bias are collected. äDistinguishing selection bias and confounding bias in comparative effe(epmc).pdf DeepDyve Pubget Overpricing