Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

Secondary analysis of dangerousness among death sentenced capital murderers

The theory of incapacitation involves reducing an offender's ability or capacity to commit further crimes. Capital punishment accomplishes this goal. An executed murderer never murders again. However, we do not execute all murderers, only capital murderers. This policy produces several research questions. Do capital murderers present a special risk to society? Are capital murderers more likely to murder or commit other violent crimes again than other murderers or the average citizen? To answer these questions, many states require a prediction of future dangerousness of a newly convicted murderer. To what extent has the judgment of future dangerousness matched actuarial data of subsequent murders and serious crimes? Using a secondary analysis, this investigation attempted to assemble available data of postconviction dangerousness of death sentenced capital murderers to create a more comprehensive actuarial account of subsequent dangerousness and to present the data in a common format used by the Federal Bureau of Investigation and the Bureau of Justice Statistics. Across 14 studies identified with relevant data, there were 13 instances of subsequent murder and 462 serious crime or prison rule violations.     

Robust testing with generalized partial linear models for longitudinal data

By approximating the nonparametric component using a regression spline in generalized partial linear models (GPLM), robust generalized estimating equations (GEE), involving bounded score function and leverage-based weighting function, can be used to estimate the regression parameters in GPLM robustly for longitudinal data or clustered data. In this paper, score test statistics are proposed for testing the regression parameters with robustness, and their asymptotic distributions under the null hypothesis and a class of local alternative hypotheses are studied. The proposed score tests reply on the estimation of a smaller model without the testing parameters involved, and perform well in the simulation studies and real data analysis conducted in this paper.     

Interpreting DNA Mixtures with Related Contributors in Subdivided Populations

Abstract.  In this paper, we study the statistical interpretation of forensic DNA mixtures with related contributors in subdivided populations. Compact general formulae for match probabilities are obtained for two situations: a relative of one tested person is an unknown contributor of a DNA mixture; and two related unknowns are contributors. The effect of kinship and population structure is illustrated using a real case example.     

How faith‐based social service organizations manage secular pressures associated with government funding

This article reports selected findings from a qualitative case study of two faith‐based social service organizations to address two questions: (1) How does government funding influence the religious characteristics of faith‐based social service organizations? (2) How do government‐funded, faith‐based social service organizations manage the tensions arising from both secular and religious contexts? The findings suggest that the adaptation of secular institutional practices is not as inevitable as some have feared. Rather, the two organizations studied showed convincingly that their faith traditions and values were alive and widely evident throughout their organizations. Three key strategies emerged as means for maintaining religiousness in the face of secular pressures: (1) Religious identities were perceived as given rather than chosen, and therefore were not negotiable; (2) religious values provided strong justification for seeking relationships with others who do not share their faith; (3) the religious worldview blurred religious and secular distinctions so that secular technologies and practices could comfortably be utilized.     

An introduction to econometric applications of empirical process theory for dependent random variables

This paper discusses some uses in econometrics of empirical process theory for dependent rendom variables. Examples considered include non-standard parametric hypotheses tests and semiparametric estimation. The application of bracketing functional limit results is discussed in some detail