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10.1002/pds.3983 http://scihub22266oqcxt.onion/10.1002/pds.3983 C4930363!4930363 !26875591     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 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 Deprecated: Implicit conversion from float 261.2 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 Deprecated: Implicit conversion from float 261.2 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 Deprecated: Implicit conversion from float 261.2 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 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 294.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 294.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26875591 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Pharmacoepidemiol+Drug+Saf 2016 ; 25 (4 ): 453-61 Nephropedia Template TP {{tp|p=26875591 |t=2016. Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring |pdf=|usr=}}{{26875591 }} gab.com Text Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring JO: Pharmacoepidemiol Drug Saf http://www.kidney.de/pget.php?&tw=1&pmid=26875591 #FOAvip PURPOSE: The objective of this study was to evaluate regression, matching, and stratification on propensity score (PS) or disease risk score (DRS) in a setting of sequential analyses where statistical hypotheses are tested multiple times. METHODS: In a setting of sequential analyses, we simulated incident users and binary outcomes with different confounding strength, outcome incidence, and the adoption rate of treatment. We compared Type I error rate, empirical power, and time to signal using the following confounder adjustments: (i) regression; (ii) treatment matching (1:1 or 1:4) on PS or DRS; and (iii) stratification on PS or DRS. We estimated PS and DRS using lookwise and cumulative methods (all data up to the current look). We applied these confounder adjustments in examining the association between non-steroidal anti-inflammatory drugs and bleeding. RESULTS: Propensity score and DRS methods had similar empirical power and time to signal. However, DRS methods yielded Type I error rates up to 17% for 1:4 matching and 15.3% for stratification methods when treatment and outcome were common and confounding strength with treatment was stronger. When treatment and outcome were not common, stratification on PS and DRS and regression yielded 8-10% Type I error rates and inflated empirical power. However, when outcome and treatment were common, both regression and stratification on PS outperformed other matching methods with Type I error rates close to 5%. CONCLUSIONS: We suggest regression and stratification on PS when the outcomes and/or treatment is common and use of matching on PS with higher ratios when outcome or treatment is rare or moderately rare. Twit Text FOAVip Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring ????Pharmacoepidemiol Drug Saf http://www.kidney.de/pget.php?&tw=1&pmid=26875591 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26875591 #FOAvip English Wikipedia <ref>{{Cite pmid|26875591 }}</ref> Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring #MMPMID26875591 Xu S ; Shetterly S ; Cook AJ ; Raebel MA ; Goonesekera S ; Shoaibi A ; Roy J ; Fireman B Pharmacoepidemiol Drug Saf 2016[Apr]; 25 (4 ): 453-61 PMID26875591 show ga PURPOSE: The objective of this study was to evaluate regression, matching, and stratification on propensity score (PS) or disease risk score (DRS) in a setting of sequential analyses where statistical hypotheses are tested multiple times. METHODS: In a setting of sequential analyses, we simulated incident users and binary outcomes with different confounding strength, outcome incidence, and the adoption rate of treatment. We compared Type I error rate, empirical power, and time to signal using the following confounder adjustments: (i) regression; (ii) treatment matching (1:1 or 1:4) on PS or DRS; and (iii) stratification on PS or DRS. We estimated PS and DRS using lookwise and cumulative methods (all data up to the current look). We applied these confounder adjustments in examining the association between non-steroidal anti-inflammatory drugs and bleeding. RESULTS: Propensity score and DRS methods had similar empirical power and time to signal. However, DRS methods yielded Type I error rates up to 17% for 1:4 matching and 15.3% for stratification methods when treatment and outcome were common and confounding strength with treatment was stronger. When treatment and outcome were not common, stratification on PS and DRS and regression yielded 8-10% Type I error rates and inflated empirical power. However, when outcome and treatment were common, both regression and stratification on PS outperformed other matching methods with Type I error rates close to 5%. CONCLUSIONS: We suggest regression and stratification on PS when the outcomes and/or treatment is common and use of matching on PS with higher ratios when outcome or treatment is rare or moderately rare. |*Computer Simulation [MESH] |*Confounding Factors, Epidemiologic [MESH] |Anti-Inflammatory Agents, Non-Steroidal/*adverse effects/therapeutic use [MESH] |Diffusion of Innovation [MESH] |Female [MESH] |Hemorrhage/epidemiology/*etiology [MESH] |Humans [MESH] |Male [MESH] |Propensity Score [MESH]Evaluation of propensity scores, disease risk scores, and regression i(epmc).pdf DeepDyve Pubget Overpricing