Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1002/sim.6847 http://scihub22266oqcxt.onion/10.1002/sim.6847 C4848174!4848174!26707831     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26707831&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 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       Stat+Med 2016 ; 35 (12): 1944-71 Nephropedia Template TP {{tp|p=26707831|t=2016. Sample Size Calculations for Micro-randomized Trials in mHealth |pdf=|usr=}}{{26707831}} gab.com Text Sample Size Calculations for Micro-randomized Trials in mHealth JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26707831 #FOAvip The use and development of mobile interventions are experiencing rapid growth. In ?just-in-time? mobile interventions, treatments are provided via a mobile device and they are intended to help an individual make healthy decisions ?in the moment,? and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a ?micro-randomized? trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity. Twit Text FOAVip Sample Size Calculations for Micro-randomized Trials in mHealth ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26707831 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26707831 #FOAvip English Wikipedia <ref>{{Cite pmid|26707831}}</ref> Sample Size Calculations for Micro-randomized Trials in mHealth #MMPMID26707831 Liao P; Klasnja P; Tewari A; Murphy SA Stat Med 2016[May]; 35 (12): 1944-71 PMID26707831show ga The use and development of mobile interventions are experiencing rapid growth. In ?just-in-time? mobile interventions, treatments are provided via a mobile device and they are intended to help an individual make healthy decisions ?in the moment,? and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a ?micro-randomized? trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity. äSample Size Calculations for Micro-randomized Trials in mHealth (epmc).pdf DeepDyve Pubget Overpricing