Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.

Two common features of clinical trials, and other longitudinal studies, are (1) a primary interest in composite endpoints, and (2) the problem of subjects withdrawing prematurely from the study. In some settings, withdrawal may only affect observation of some components of the composite endpoint, for example when another component is death, information on which may be available from a national registry. In this paper, we use the theory of augmented inverse probability weighted estimating equations to show how such partial information on the composite endpoint for subjects who withdraw from the study can be incorporated in a principled way into the estimation of the distribution of time to composite endpoint, typically leading to increased efficiency without relying on additional assumptions above those that would be made by standard approaches. We describe our proposed approach theoretically, and demonstrate its properties in a simulation study

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

Comment on ``Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data'' [arXiv:0804.2958]Comment: Published in at http://dx.doi.org/10.1214/07-STS227B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Putting Melendez-Diaz on Ice: How Autopsy Reports Can Survive the Supreme Court\u27s Confrontation Clause Jurisprudence

(Excerpt) This Note examines how the Supreme Court’s holding in the Melendez-Diaz case has impacted autopsy reports as evidentiary tools in criminal cases. Part I offers some background on autopsy reports and forensic pathology, discusses key evidentiary rules, and the history of the Sixth Amendment’s Confrontation Clause leading up to Melendez-Diaz. Part II explores the breadth and consequences of Melendez-Diaz, particularly as they impact autopsy reports. Part III analyzes how autopsy reports differ fundamentally from many other types of forensic reports, notably because of policy issues they implicate. Finally, Part IV defines and presents the “lean rule” as an alternative for MEs and courts alike that does not implicate the defendant’s Sixth Amendment interests, that provides the prosecution with facts and observations that can still be used should the particular ME be unavailable, and that allows MEs to continue performing their duties in a relatively uninterrupted fashion

Sequential Methods for Comparing Years of Life Saved in the Two-Sample Censored Data Problem

This research develops nonparametric strategies for sequentially monitoring clinical trial data where detecting years of life saved is of interest. The recommended test statistic looks at integrated differences in survival estimates during the time frame of interest. In many practical situations, the test statistic presented has an independent increments covariance structure. Hence, with little additional work, we may apply these testing procedures using available methodology. In the case where an independent increments covariance structure is present, we suggest how clinical trial data might be monitored using these statistics in an information-based design. The resulting study design maintains the desired stochastic operating characteristics regardless of the shapes of the survival curves being compared. This offers an advantage over the popular log-rank-based design strategy since more restrictive assumptions relating to the behavior of the hazards are required to guarantee the planned power of the test. Recommendations for how to sequentially monitor clinical trial progress in the non-independent increments case are also provided along with an example.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65520/1/j.0006-341X.1999.01085.x.pd

Independent increments in group sequential tests : a review

In order to apply group sequential methods for interim analysis for early stopping in clinical trials, the joint distribution of test statistics over time has to be known. Often the distribution is multivariate normal or asymptotically so, and an application of group sequential methods requires multivariate integration to determine the group sequential boundaries. However, if the increments between successive test statistics are independent, the multivariate integration reduces to a univariate integration involving simple recursion based on convolution. This allows application of standard group sequential methods. In this paper we review group sequential methods and the development that established independent increments in test statistics for the primary outcomes of longitudinal or failure time data

Experimental data on the properties of polymer-modified cement grouts using epoxy and acrylic resin emulsions

AbstractThe use of additives to improve the quality of cement grouts is crucial for civil engineering, especially in foundation construction. This article presents experimental data concerning the compressive strength, elastic modulus, bleeding and injectability of microfine cement grouts modified with epoxy and acrylic resin emulsions. Strength properties were obtained at different curing ages. For further analysis and detailed discussion of properties of polymer-modified cement grouts, see “Fundamental properties of epoxy resin-modified cement grouts” (C.A. Anagnostopoulos, G. Sapidis, E. Papastergiadis, 2016) [1]

Nonparametric Survival Estimation Using Prognostic Longitudinal Covariates

One of the primary problems facing statisticians who work with survival data is the loss of information that occurs with right-censored data. This research considers trying to recover some of this endpoint information through the use of a prognostic covariate which is measured on each individual. We begin by defining a survival estimate which uses time-dependent covariates to more precisely get at the underlying survival curves in the presence of censoring. This estimate has a smaller asymptotic variance than the usual Kaplan-Meier in the presence of censoring and reduces to the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) in situations where the covariate is not prognostic or no censoring occurs. In addition, this estimate remains consistent when the incorporated covariate contains information about the censoring process as well as survival information. Because the Kaplan-Meier estimate is known to be biased in this situation due to informative censoring, we recommend use of our estimate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91897/1/Murray Tsiatis 1996 Biometrics.pd

A fresh look at the Semiparametric Cram\'{e}r-Rao Bound

This paper aims at providing a fresh look at semiparametric estimation theory and, in particular, at the Semiparametric Cram\'{e}r-Rao Bound (SCRB). Semiparametric models are characterized by a finite-dimensional parameter vector of interest and by an infinite-dimensional nuisance function that is often related to an unspecified functional form of the density of the noise underlying the observations. We summarize the main motivations and the intuitive concepts about semiparametric models. Then we provide a new look at the classical estimation theory based on a geometrical Hilbert space-based approach. Finally, the semiparametric version of the Cram\'{e}r-Rao Bound for the estimation of the finite-dimensional vector of the parameters of interest is provided.Comment: Submitted to EUSIPCO 201