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10.1002/sim.6783 http://scihub22266oqcxt.onion/10.1002/sim.6783 C4777666!4777666 !26506890     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26506890 &cmd=llinks): Failed to open stream: HTTP request failed! 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Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy |pdf=|usr=}}{{26506890 }} gab.com Text Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26506890 #FOAvip A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness. Twit Text FOAVip Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26506890 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26506890 #FOAvip English Wikipedia <ref>{{Cite pmid|26506890 }}</ref> Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy #MMPMID26506890 Laber EB ; Zhao YQ ; Regh T ; Davidian M ; Tsiatis A ; Stanford JB ; Zeng D ; Song R ; Kosorok MR Stat Med 2016[Apr]; 35 (8 ): 1245-56 PMID26506890 show ga A personalized treatment strategy formalizes evidence-based treatment selection by mapping patient information to a recommended treatment. Personalized treatment strategies can produce better patient outcomes while reducing cost and treatment burden. Thus, among clinical and intervention scientists, there is a growing interest in conducting randomized clinical trials when one of the primary aims is estimation of a personalized treatment strategy. However, at present, there are no appropriate sample size formulae to assist in the design of such a trial. Furthermore, because the sampling distribution of the estimated outcome under an estimated optimal treatment strategy can be highly sensitive to small perturbations in the underlying generative model, sample size calculations based on standard (uncorrected) asymptotic approximations or computer simulations may not be reliable. We offer a simple and robust method for powering a single stage, two-armed randomized clinical trial when the primary aim is estimating the optimal single stage personalized treatment strategy. The proposed method is based on inverting a plugin projection confidence interval and is thereby regular and robust to small perturbations of the underlying generative model. The proposed method requires elicitation of two clinically meaningful parameters from clinical scientists and uses data from a small pilot study to estimate nuisance parameters, which are not easily elicited. The method performs well in simulated experiments and is illustrated using data from a pilot study of time to conception and fertility awareness. |Biostatistics [MESH] |Computer Simulation [MESH] |Confidence Intervals [MESH] |Data Interpretation, Statistical [MESH] |Evidence-Based Practice/statistics & numerical data [MESH] |Female [MESH] |Fertility [MESH] |Humans [MESH] |Male [MESH] |Models, Statistical [MESH] |Pilot Projects [MESH] |Precision Medicine/statistics & numerical data [MESH] |Pregnancy [MESH] |Randomized Controlled Trials as Topic/*statistics & numerical data [MESH] |Regression Analysis [MESH]Using pilot data to size a two-arm randomized trial to find a nearly o(epmc).pdf DeepDyve Pubget Overpricing