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10.7326/M20-4044 http://scihub22266oqcxt.onion/10.7326/M20-4044 32634024!7350552!32634024     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ann+Intern+Med 2020 ; 173 (8): 632-637 Nephropedia Template TP {{tp|p=32634024|t=2020. How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 |pdf=|usr=}}{{32634024}} gab.com Text How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 JO: Ann Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=32634024 #FOAvip Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19. Twit Text FOAVip How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 ????Ann Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=32634024 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32634024 #FOAvip English Wikipedia <ref>{{Cite pmid|32634024}}</ref> How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19 #MMPMID32634024 McCaw ZR; Tian L; Vassy JL; Ritchie CS; Lee CC; Kim DH; Wei LJ Ann Intern Med 2020[Oct]; 173 (8): 632-637 PMID32634024show ga Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19. |*Betacoronavirus[MESH] |*Pandemics[MESH] |COVID-19[MESH] |COVID-19 Serotherapy[MESH] |Coronavirus Infections/epidemiology/*therapy[MESH] |Humans[MESH] |Immunization, Passive/methods[MESH] |Pneumonia, Viral/epidemiology/*therapy[MESH] |Randomized Controlled Trials as Topic/*methods[MESH] |SARS-CoV-2[MESH]How to Quantify and Interpret Treatment Effects in Comparative Clinica(epmc).pdf DeepDyve Pubget Overpricing