In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the...

In estimating optimal adaptive treatment strategies, the tailor treatment variables used for patient profiles are typically hand-picked by experts. However these variables may not yield an estimated optimal dynamic regime that is close to the optimal regime which uses all variables. The question...

A fast and efficient estimation method is proposed that compensates the distortion in nonlinear transformation models. A likelihood-based estimator is developed that can be computed by an EM-type algorithm. The consistency of the estimator is shown and its limit distribution is provided. The new...

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability...

Marginal structural models (MSM) provide a powerful tool to control for confounding by a time-dependent covariate without inappropriately adjusting for its role as a variable affected by treatment (Hernán et al., 2000). In this paper, we demonstrate that it is possible to fit a marginal...

Collaborative double robust targeted maximum likelihood estimators represent a fundamental further advance over standard targeted maximum likelihood estimators of a pathwise differentiable parameter of a data generating distribution in a semiparametric model, introduced in van der Laan, Rubin...

This is a response to Nancy Cook's Readers' Reaction to "Measures to Summarize and Compare the Predictive Capacity of Markers."

Targeted maximum likelihood estimation of a parameter of a data generating distribution, known to be an element of a semi-parametric model, involves constructing a parametric model through an initial density estimator with parameter ? representing an amount of fluctuation of the initial density...

The evaluation of the ability of a diagnostic test to separate diseased subjects from non-diseased subjects is a crucial issue in modern medicine. The accuracy of a continuous-scale test at a chosen cut-off level can be measured by its sensitivity and specificity, i.e. by the probabilities that...

Sample size requirements that achieve a prespecified expected lower limit for a confidence interval about the intraclass kappa statistic are supplied for the case of multiple raters and a binary outcome variable. The expected lower confidence limit achievable for a given number of subjects and...