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10.1016/j.bbmt.2015.12.005 http://scihub22266oqcxt.onion/10.1016/j.bbmt.2015.12.005 C4756459!4756459!26712591     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26712591&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Biol+Blood+Marrow+Transplant 2016 ; 22 (3): 557-63 Nephropedia Template TP {{tp|p=26712591|t=2016. Observational Studies: Matching or Regression?|pdf=|usr=}}{{26712591}} gab.com Text Observational Studies: Matching or Regression JO: Biol Blood Marrow Transplant http://www.kidney.de/pget.php?&tw=1&pmid=26712591 #FOAvip In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be employed. Often baseline characteristics of the patients may be imbalanced between the groups and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes the groups appear comparable. In this article, we considered these two options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared to a matched study. Real data from a hematopoietic cell transplantation study is used as an example. Twit Text FOAVip Observational Studies: Matching or Regression ????Biol Blood Marrow Transplant http://www.kidney.de/pget.php?&tw=1&pmid=26712591 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26712591 #FOAvip English Wikipedia <ref>{{Cite pmid|26712591}}</ref> Observational Studies: Matching or Regression? #MMPMID26712591 Brazauskas R; Logan BR Biol Blood Marrow Transplant 2016[Mar]; 22 (3): 557-63 PMID26712591show ga In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be employed. Often baseline characteristics of the patients may be imbalanced between the groups and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes the groups appear comparable. In this article, we considered these two options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared to a matched study. Real data from a hematopoietic cell transplantation study is used as an example. äObservational Studies: Matching or Regression?(epmc).pdf DeepDyve Pubget Overpricing