Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R

Due to tradition and ease of estimation, the vast majority of clinical and epidemiological papers with time-to-event data report hazard ratios from Cox proportional hazards regression models. Although hazard ratios are well known, they can be difficult to interpret, particularly as causal contrasts, in many settings. Nonparametric or fully parametric estimators allow for the direct estimation of more easily causally interpretable estimands such as the cumulative incidence and restricted mean survival. However, modeling these quantities as functions of covariates is limited to a few categorical covariates with nonparametric estimators, and often requires simulation or numeric integration with parametric estimators. Combining pseudo-observations based on non-parametric estimands with parametric regression on the pseudo-observations allows for the best of these two approaches and has many nice properties. In this paper, we develop a user friendly, easy to understand way of doing event history regression for the cumulative incidence and the restricted mean survival, using the pseudo-observation framework for estimation. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and correct variance estimation

plotROC: A Tool for Plotting ROC Curves

Plots of the receiver operating characteristic (ROC) curve are ubiquitous in medical research. Designed to simultaneously display the operating characteristics at every possible value of a continuous diagnostic test, ROC curves are used in oncology to evaluate screening, diagnostic, prognostic and predictive biomarkers. I reviewed a sample of ROC curve plots from the major oncology journals in order to assess current trends in usage and design elements. My review suggests that ROC curve plots are often ineffective as statistical charts and that poor design obscures the relevant information the chart is intended to display. I describe my new R package that was created to address the shortcomings of existing tools. The package has functions to create informative ROC curve plots, with sensible defaults and a simple interface, for use in print or as an interactive web-based plot. A web application was developed to reach a broader audience of scientists who do not use R

ONTOGENY OF B LYMPHOCYTES : III. H-2 LINKAGE OF A GENE CONTROLLING THE RATE OF APPEARANCE OF COMPLEMENT RECEPTOR LYMPHOCYTES

The frequency of lymphocytes bearing complement receptors in the spleens of 2-wk old mice appears to be controlled by two independent genes. The presence of a "high" allele at either locus leads to intermediate or high frequency of CRL at 2 wk of age. One of the genes controlling complement receptor lymphocyte (CRL) frequency (CRL-1) is linked to the H-2 complex. Thus, in progeny of (AKR x DBA/2)F1 x DBA/2, all mice with a low frequency of CRL at 2 wk of age are homozygous for the H-2 type of the low CRL parent (DBA/2). Furthermore, in the B10 series of congenic mice, CRL frequency at 2 wk of age is similar to the frequency in the donor of the H-2 region. Thus, C57BL/10, B10.BR, and B10-D2 mice are all of the low CRL type while B10.A mice are intermediate in CRL frequency at 2 wk. C57BR and DBA/2, the donors of the H-2 complex of the B10.BR and B10.D2, respectively, are of low CRL type while the A/WySn, the donor of the H-2 complex in the B10.A, is an intermediate CRL strain. Similarly in the A/WySn series of congenic mice, A.CA, A.SW, and A.BY are all low CRL strains while the A/WySn is intermediate. Studies of CRL frequency in mice with recombinant H-2 chromosomes (B10.A(2R), (4R), and (5R); B6/TL+; and A/TL-) indicate that CRL-1 is to the right of the Ss-Slp genes and to the left of Tla

Institutional Aspects of High Unemployment in the Federal Republic of Germany

The sustained rise in German unemployment since 1973 poses a problem of critical importance for the world economy. Fewer than two decades ago, Germany boasted an average unemployment rate of under 1% and imported labor to relieve chronic labor shortages. By the mid-1980s, unemployment had risen to over 8 percent of the labor force. This paper investigates some of the reasons for the secular rise in unemployment. We find that while deficient aggregate demand can probably explain some of the current joblessness, the secular rise in unemployment has consisted primarily of an increase in the equilibrium rate of unemployment. We also find little evidence that this increase is due to changes in frictional unemployment. Rather, after reviewing institutional details of the labor market in Germany, we identify various impediments to the kinds of structural adjustments that have operated to maintain a fairly constant equilibrium rate of unemployment in the United States

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains `unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator