Estimators of the conditional expectation, i.e., prediction, function involve a global bias-variance trade off. In some cases, an estimator that yields unbiased estimates of the conditional expectation for a particular partitioning of the data may be desirable. Such estimators are calibrated...

We introduce a new smooth estimator of the ROC curve based on log-concave density estimates of the constituent distributions. We show that our estimate is asymptotically equivalent to the empirical ROC curve if the underlying densities are in fact log-concave. In addition, we empirically show...

Detection and estimation of measures of familial aggregation is considered the first step to establish whether a certain disease has genetic component. Such measures are usually estimated from observational studies on siblings, parent-offspring, extended pedigrees or twins. When the trait of...

Age-period-cohort models provide a useful method for modeling incidence and mortality rates. There is great interest in estimating the rates of disease at given future time-points in order that plans can be made for the provision of the required future services. In the setting of using...

We extend Pearl's criticisms of principal stratification analysis as a method for interpreting and adjusting for intermediate variables in a causal analysis. We argue that this can be meaningful only in those rare cases that involve strong functional dependence, and even then may not be appropriate.

Most of the methodological literature on evaluating an additional marker for risk prediction involves purely statistical measures of classification performance. A disadvantage of a purely statistical measure is the difficulty in deciding the improvement in the measure that would make inclusion...

The attributable fraction of a disease due to an exposure is the fraction of disease cases in a population that can be attributed to that exposure. We consider the attributable fraction for a semi-continuous exposure, that is an exposure for which a clump of people have zero exposure and the...

Sequential Randomized Controlled Trials (SRCTs) are rapidly becoming essential tools in the search for optimized treatment regimes in ongoing treatment settings. Analyzing data for multiple time-point treatments with a view toward optimal treatment regimes is of interest in many types of...

We discuss using clinical trial data to construct and evaluate rules that use baseline covariates to assign different treatments to different patients. Given such a candidate personalization rule, we first note that its performance can often be evaluated without actually applying the rule to...

A semi-parametric spatial model for spatial dependence is proposed in Poisson regressions to study the effects of risk factors on incidence outcomes. The spatial model is constructed through an application of reproducing kernels. A Bayesian framework is proposed to infer the unknown parameters....