“Tenured Allies” and the Normalization of Contingent Labor

Even the best-intentioned tenured faculty can make matters worse for their contingent colleagues if they make a habit of not addressing the elephant in the room

Water Transfer: Shall We Sink or Swim Together?

For all life water is necessary. For many uses it is convenient. In much of its functioning it is commonplace. But commonplace things often are the least appreciated and the hardest to understand . . . . In considering its uses and abundance and properties, however, we must keep in mind this main fact: Water is needed for life

Bivariate Interval-Censored Failure Time Data

This is the peer reviewed version of the following article: Cook, R. J., Zeng, L. and Lee, K.-A. (2008), A Multistate Model for Bivariate Interval-Censored Failure Time Data. Biometrics, 64: 1100–1109. doi: 10.1111/j.1541-0420.2007.00978.x, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2007.00978.x/abstract. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. The definitive version is available at http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2007.00978.x/abstract’Interval-censored life-history data arise when the events of interest are only detectable at periodic assessments. When interest lies in the occurrence of two such events, bivariate-interval censored event time data are obtained. We describe how to fit a four-state Markov model useful for characterizing the association between two interval-censored event times when the assessment times for the two events may be generated by different inspection processes. The approach treats the two events symmetrically and enables one to fit multiplicative intensity models that give estimates of covariate effects as well as relative risks characterizing the association between the two events. An expectation-maximization (EM) algorithm is described for estimation in which the maximization step can be carried out with standard software. The method is illustrated by application to data from a trial of HIV patients where the events are the onset of viral shedding in the blood and urine among individuals infected with cytomegalovirus.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

Simultaneous Confidence Intervals Based on the Percentile Bootstrap Approach

This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O(mB log(B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals

Designed Extension of Survival Studies: Application to Clinical Trials with Unrecognized Heterogeneity

It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of the weighted log rank test under the assumption of a proportional hazards frailty model. The proposed method is illustrated through application to a brain tumor trial