Bias and precision of measures of survival gain from right‐censored data

In cost‐effectiveness analyses of drugs or health technologies, estimates of life years saved or quality‐adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close‐out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain – difference in median, restricted mean and extended mean survival – when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost‐effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade‐offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable. Copyright © 2015 John Wiley & Sons, Ltd.     

An experimental investigation of intrinsic motivations for giving

This paper presents results from a modified dictator experiment aimed at distinguishing and quantifying intrinsic motivations for giving. We employ an experimental design with three treatments that vary the recipient (experimenter, charity) and amount passed (fixed, varying). We find giving to the experimenter not to be significantly different from giving to a charity, when the amount the subject donates crowds out the amount donated by the experimenter such that the charity always receives a fixed amount. This result suggests that the latter treatment, first used by Crumpler and Grossman (J Public Econ 92(5–6):1011–1021, 2008), does not provide a clean test of warm glow motivation. We then propose a new method of detecting warm glow motivation based on the idea that in a random-lottery incentive (RLI) scheme, such as the one we employ, warm glow accumulates and this may lead to satiation, whereas purely altruistic motivation does not. We also provide bounds on the magnitudes of warm glow and pure altruism as motives that drive giving in our experiment.     

From Amazon to Apple: Modeling Online Retail Sales,Purchase Incidence,and Visit Behavior

In this study, we propose a multivariate stochastic model for Web site visit duration, page views, purchase incidence, and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions and involves copula components. It allows for the strong nonlinear relationships between the sales and visit variables to be explored in detail, and can be used to construct sales predictions. The model is readily estimated using maximum likelihood, making it an attractive choice in practice given the large sample sizes that are commonplace in online retail studies. We examine a number of top-ranked U.S. online retailers, and find that the visit duration and the number of pages viewed are both related to sales, but in very different ways for different products. Using Bayesian methodology, we show how the model can be extended to a finite mixture model to account for consumer heterogeneity via latent household segmentation. The model can also be adjusted to accommodate a more accurate analysis of online retailers like apple.com that sell products at a very limited number of price points. In a validation study across a range of different Web sites, we find that the purchase incidence and sales amount are both forecast more accurately using our model, when compared to regression, probit regression, a popular data-mining method, and a survival model employed previously in an online retail study. Supplementary materials for this article are available online.     

Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics

We study sequential Bayesian inference in stochastic kinetic models with latent factors. Assuming continuous observation of all the reactions, our focus is on joint inference of the unknown reaction rates and the dynamic latent states, modeled as a hidden Markov factor. Using insights from nonlinear filtering of continuous-time jump Markov processes we develop a novel sequential Monte Carlo algorithm for this purpose. Our approach applies the ideas of particle learning to minimize particle degeneracy and exploit the analytical jump Markov structure. A motivating application of our methods is modeling of seasonal infectious disease outbreaks represented through a compartmental epidemic model. We demonstrate inference in such models with several numerical illustrations and also discuss predictive analysis of epidemic countermeasures using sequential Bayes estimates.     

Modeling potential time to event data with competing risks

Patients receiving radical prostatectomy are at risk of metastasis or prostate cancer related death, and often need repeated clinical evaluations to determine whether additional adjuvant or salvage therapies are needed. Since the prostate cancer is a slowly progressing disease, and these additional therapies come with significant side effects, it is important for clinical decision making purposes to estimate a patient’s risk of cancer metastasis, in the presence of a competing risk by death, under the hypothetical condition that the patient does not receive any additional therapy. In observational studies, patients may receive additional therapy by choice; the time to metastasis without any therapy is often a potential outcome and not always observed. We study the competing risks model of Fine and Gray (J Am Stat Assoc, 94:496–509, 1999) with adjustment for treatment choice by inverse probability censoring weighting (IPCW). The model can be fit using standard software for partial likelihood with double IPCW weights. The proposed methodology is used in a prostate cancer study to predict the post-prostatectomy cumulative incidence probability of cancer metastasis without additional adjuvant or salvage therapies.     

Black-White Disparities in Life Expectancy: How Much Can the Standard SES Variables Explain?

This article quantifies the extent to which socioeconomic and demographic characteristics can account for black-white disparities in life expectancy in the United States. Although many studies have investigated the linkages between race, socioeconomic status, and mortality, this article is the first to measure how much of the life expectancy gap remains after differences in mortality are purged of the compositional differences in socioeconomic characteristics between blacks and whites. The decomposition is facilitated by a reweighting technique that creates counterfactual estimation samples in which the distribution of income, education, employment and occupation, marital status, and other theoretically relevant variables among blacks is made to match the distribution of these variables among whites. For males, 80% of the black-white gap in life expectancy at age 1 can be accounted for by differences in socioeconomic and demographic characteristics. For females, 70% percent of the gap is accounted for. Labor force participation, occupation, and (among women only) marital status have almost no additional power to explain the black-white disparity in life expectancy after precise measures for income and education are controlled for.     

Diverging Destinies: Maternal Education and the Developmental Gradient in Time With Children

Using data from the 2003?C2007 American Time Use Surveys (ATUS), we compare mothers?? (N = 6,640) time spent in four parenting activities across maternal education and child age subgroups. We test the hypothesis that highly educated mothers not only spend more time in active child care than less-educated mothers but also alter the composition of that time to suit children??s developmental needs more than less-educated mothers. Results support this hypothesis: not only do highly educated mothers invest more time in basic care and play when youngest children are infants or toddlers than when children are older, but differences across education groups in basic care and play time are largest among mothers with infants or toddlers; by contrast, highly educated mothers invest more time in management activities when children are 6 to 13?years old than when children are younger, and differences across education groups in management are largest among mothers with school-aged children. These patterns indicate that the education gradient in mothers?? time with children is characterized by a ??developmental gradient.??     

Gender, political ideology, and climate change beliefs in an extractive industry community

This paper presents results from a survey on attitudes toward climate change in Alberta, Canada, home to just 10% of Canada??s population, but the source of 35% of the country??s greenhouse-gas emissions (Environment Canada 2011). Results show high levels of awareness, but much lower levels of perceived climate change impacts for one??s self or region. Women expressed significantly greater awareness and sense of perceived impacts about climate change than men; however, gender differences appear predominantly associated with socioeconomic factors. Indeed, in all, political ideology had the strongest predictive value, with individuals voting for the conservative party significantly less likely to anticipate significant societal climate change impacts. This finding, in turn, is strongly associated with beliefs regarding whether climate change is human induced. Particularly notable is the finding that the gender gap in climate change beliefs and perceived impacts is not attributed to gendered social roles, as indicated by occupational and familial status. Instead, gender distinctions appear to be related to the lower tendency for women to ascribe to a conservative political ideology relative to men.