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10.1002/sim.6859 http://scihub22266oqcxt.onion/10.1002/sim.6859 C4853263!4853263 !26750518     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 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\26750518 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Med 2016 ; 35 (13 ): 2221-34 Nephropedia Template TP {{tp|p=26750518 |t=2016. Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia |pdf=|usr=}}{{26750518 }} gab.com Text Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26750518 #FOAvip Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. Twit Text FOAVip Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=26750518 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26750518 #FOAvip English Wikipedia <ref>{{Cite pmid|26750518 }}</ref> Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia #MMPMID26750518 Ertefaie A ; Shortreed S ; Chakraborty B Stat Med 2016[Jun]; 35 (13 ): 2221-34 PMID26750518 show ga Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. |*Data Interpretation, Statistical [MESH] |Antipsychotic Agents/*administration & dosage/therapeutic use [MESH] |Decision Support Techniques [MESH] |Drug Administration Schedule [MESH] |Humans [MESH] |Linear Models [MESH] |Models, Statistical [MESH] |Regression Analysis [MESH] |Schizophrenia/*drug therapy [MESH]Q-learning residual analysis: application to the effectiveness of sequ(epmc).pdf DeepDyve Pubget Overpricing