tag:crantastic.org,2005:/authors/6815Latest activity for Paul F. Blanche2019-12-18T16:23:47Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/963932019-12-18T16:23:47Z2019-12-18T16:23:47ZtimeROC was upgraded to version 0.4<a href="/packages/timeROC">timeROC</a> was <span class="action">upgraded</span> to version <a href="/packages/timeROC/versions/91641">0.4</a><br /><h3>Package description:</h3><p>Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/942402019-10-30T15:03:59Z2019-10-30T15:03:59Zwally was upgraded to version 1.0.10<a href="/packages/wally">wally</a> was <span class="action">upgraded</span> to version <a href="/packages/wally/versions/89592">1.0.10</a><br /><h3>Package description:</h3><p>A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/628852017-05-24T23:42:25Z2017-05-24T23:42:25Zwally was upgraded to version 1.0.9<a href="/packages/wally">wally</a> was <span class="action">upgraded</span> to version <a href="/packages/wally/versions/60132">1.0.9</a><br /><h3>Package description:</h3><p>A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/626392017-05-17T08:02:20Z2017-05-17T08:02:20Zwally was released<a href="/packages/wally">wally</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/491972016-03-12T00:15:30Z2016-03-12T00:15:30Zcrantastic_production tagged timeROC with Survival<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/timeROC">timeROC</a> with <a href="/task_views/Survival">Survival</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/397762015-03-24T23:44:34Z2015-03-24T23:44:34ZtimeROC was upgraded to version 0.3<a href="/packages/timeROC">timeROC</a> was <span class="action">upgraded</span> to version <a href="/packages/timeROC/versions/39600">0.3</a><br /><h3>Package description:</h3><p>Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/330032013-05-27T17:31:17Z2013-05-27T17:31:17ZtimeROC was upgraded to version 0.2<a href="/packages/timeROC">timeROC</a> was <span class="action">upgraded</span> to version <a href="/packages/timeROC/versions/28106">0.2</a><br /><h3>Package description:</h3><p>Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/266072012-11-03T13:11:27Z2012-11-03T13:11:27ZtimeROC was released<a href="/packages/timeROC">timeROC</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.</p>crantastic.org