Book Review: Variable Outcomes among Children of Depressed Women

Book reviewed in this article:
Marian Radke-Yarrow, Children of Depressed Mothers: FromEarly Childhood to Maturity     

The Supreme Court and Race

The Supreme Court has attempted to redefine race relations for decades. This unfortunately turned into a quagmire from which it was able to extricate itself only by lowering the intensity of its moral rhetoric. In particular, the Fullilove case represented a welcome retreat from race-conscious social engineering.     

Marginally restricted sequential D‐optimal designs

In many experiments, not all explanatory variables can be controlled. When the units arise sequentially, different approaches may be used. The authors study a natural sequential procedure for “marginally restricted” D‐optimal designs. They assume that one set of explanatory variables (x₁) is observed sequentially, and that the experimenter responds by choosing an appropriate value of the explanatory variable x₂. In order to solve the sequential problem a priori, the authors consider the problem of constructing optimal designs with a prior marginal distribution for x₁. This eliminates the influence of units already observed on the next unit to be designed. They give explicit designs for various cases in which the mean response follows a linear regression model; they also consider a case study with a nonlinear logistic response. They find that the optimal strategy often consists of randomizing the assignment of the values of x₂.     

Ranked set sampling for ordered categorical variables

The authors show how ranked set sampling, both balanced and unbalanced, can be extended to ordered categorical variables with the goal of estimating the probabilities of all categories. They use ordinal logistic regression to aid in the ranking of the ordinal variable of interest. They also propose an optimal allocation scheme and methods for implementing it under either perfect or imperfect rankings. Results from a simulation study using data from the third National Health and Nutrition Examination Survey indicate that the use of ordinal logistic regression in ranking leads to substantial gains in precision for estimation of cell probabilities.     

Proportional hazards regression in the presence of missing study eligibility information

We consider the study of censored survival times in the situation where the available data consist of both eligible and ineligible subjects, and information distinguishing the two groups is sometimes missing. A complete-case analysis in this context would use only subjects known to be eligible, resulting in inefficient and potentially biased estimators. We propose a two-step procedure which resembles the EM algorithm but is computationally much faster. In the first step, one estimates the conditional expectation of the missing eligibility indicators given the observed data using a logistic regression based on the complete cases (i.e., subjects with non-missing eligibility indicator). In the second step, maximum likelihood estimators are obtained from a weighted Cox proportional hazards model, with the weights being either observed eligibility indicators or estimated conditional expectations thereof. Under ignorable missingness, the estimators from the second step are proven to be consistent and asymptotically normal, with explicit variance estimators. We demonstrate through simulation that the proposed methods perform well for moderate sized samples and are robust in the presence of eligibility indicators that are missing not at random. The proposed procedure is more efficient and more robust than the complete case analysis and, unlike the EM algorithm, does not require time-consuming iteration. Although the proposed methods are applicable generally, they would be most useful for large data sets (e.g., administrative data), for which the computational savings outweigh the price one has to pay for making various approximations in avoiding iteration. We apply the proposed methods to national kidney transplant registry data.     

Modeling restricted mean survival time under general censoring mechanisms

Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing methods, we allow censoring to depend on both baseline and time-dependent factors. Large sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite sample performance. We apply the proposed methods to model RMST in the absence of liver transplantation among end-stage liver disease patients. This analysis requires accommodation for dependent censoring since pre-transplant mortality is dependently censored by the receipt of a liver transplant.     

Density zoning and class segregation in U.S. metropolitan areas

Objectives:Socioeconomic segregation rose substantially in U.S. cities during the final decades of the 20th century, and we argue that zoning regulations are an important cause of this increase.Methods. We measure neighborhood economic segregation using the Gini coefficient for neighborhood income inequality and the poor-affluent exposure index. These outcomes are regressed on an index of density zoning developed from the work of Pendall for 50 U.S. metropolitan areas, while controlling for other metropolitan characteristics likely to affect urban housing markets and class segregation.Results. For both 2000 and changes from 1990 to 2000, OLS estimates reveal a strong relationship between density zoning and income segregation, and replication using 2SLS suggests that the relationship is causal. We also show that zoning is associated with higher interjurisdictional inequality.Conclusions. Metropolitan areas with suburbs that restrict the density of residential construction are more segregated on the basis of income than those with more permissive density zoning regimes. This arrangement perpetuates and exacerbates racial and class inequality in the United States.