tag:crantastic.org,2005:/packages/tmleLatest activity for tmle2019-02-20T15:43:15Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/849882019-02-20T15:43:15Z2019-02-20T15:43:15Ztmle was upgraded to version 1.3.0-2<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/80795">1.3.0-2</a><br /><h3>Package description:</h3><p>Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/730082018-03-25T23:22:46Z2018-03-25T23:22:46Ztmle was upgraded to version 1.3.0-1<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/69693">1.3.0-1</a><br /><h3>Package description:</h3><p>Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/711292018-02-01T17:03:41Z2018-02-01T17:03:41Ztmle was upgraded to version 1.3.0<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/67931">1.3.0</a><br /><h3>Package description:</h3><p>Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/583762017-01-09T16:51:43Z2017-01-09T16:51:43Ztmle was upgraded to version 1.2.0-5<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/55898">1.2.0-5</a><br /><h3>Package description:</h3><p>Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM() function to the package, for estimating the parameters of a marginal structural model for a binary point treatment effect. The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/269512012-11-15T17:51:49Z2012-11-15T17:51:49Ztmle was upgraded to version 1.2.0-3<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/22847">1.2.0-3</a><br /><h3>Package description:</h3><p>tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect. The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/269192012-11-14T14:32:24Z2012-11-14T14:32:24Ztmle was upgraded to version 1.2.0-2<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/22815">1.2.0-2</a><br /><h3>Package description:</h3><p>tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect. The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/223092012-05-11T18:33:18Z2012-05-11T18:33:18Ztmle was upgraded to version 1.2.0-1<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/18627">1.2.0-1</a><br /><h3>Package description:</h3><p>tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect. The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/215562012-04-03T07:31:28Z2012-04-03T07:31:28Ztmle was upgraded to version 1.2<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/17982">1.2</a><br /><h3>Package description:</h3><p>tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect. The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/163732011-09-29T11:50:44Z2011-09-29T11:50:44Ztmle was upgraded to version 1.1.1<a href="/packages/tmle">tmle</a> was <span class="action">upgraded</span> to version <a href="/packages/tmle/versions/14579">1.1.1</a><br /><h3>Package description:</h3><p>tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 1006. This implementation calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/112742011-02-04T11:50:27Z2011-02-04T11:50:27Ztmle was released<a href="/packages/tmle">tmle</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.</p>crantastic.org