A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.     

Modeling Earnings Dynamics

In this paper, we use indirect inference to estimate a joint model of earnings, employment, job changes, wage rates, and work hours over a career. We use the model to address a number of important questions in labor economics, including the source of the experience profile of wages, the response of job changes to outside wage offers, and the effects of seniority on job changes. We also study the dynamic response of wage rates, hours, and earnings to various shocks, and measure the relative contributions of the shocks to the variance of earnings in a given year and over a lifetime. We find that human capital accounts for most of the growth of earnings over a career, although job seniority and job mobility also play significant roles. Unemployment shocks have a large impact on earnings in the short run, as well as a substantial long‐term effect that operates through the wage rate. Shocks associated with job changes and unemployment make a large contribution to the variance of career earnings and operate mostly through the job‐specific error components of wages and hours.     

Some neglected social indicators

The attention now being given in the social sciences to time series indicators that measure the ‘Social Health’ of the nation is a most welcome development. Too often, sweeping claims of social change have very little hard supporting evidence. The new trend indicators offer the opportunity for more rigorous analysis of diverse subjects than is often employed in the ‘soft sciences’. Public opinion indicators show a decline in favor for U.S. business which has resulted in Congressional legislation of business, labor reform, restrictions on the environment, consumerism, and inflation. More restrictions can be expected if the social indicators are correct. Political indicators tell us that Republican party loyalty and allegiance have steadily declined since the 1950s and perhaps the ‘emerging Republican majority’ is merely a myth. There are vast implications to be derived from indicators that show the birth rate nearing zero population growth; religion steadily losing influence in America over the past 30 years; and, the youth are far more sober than the rebellious few who draw mass media attention. Social indicators have been very predictive of the future but largely ignored. A clearinghouse is needed to systematically monitor existing social indicators to avoid waste, duplication and downright reglect of important information.     

Premarital childbearing in Thamaga Village,Botswana

This paper examines the diminished importance of marriage as a setting for childbearing in Botswana. It uses qualitative data gathered in Thamaga Village during 1995 to explore the cultural basis of this development. Marriage practices and traditional attitudes to marriage, childbearing and sexual relations are reviewed, and factors identified as having undermined them are discussed. Rational adaptation theory and social disorganization theory then provide a framework within which forces encouraging premarital childbearing in Thamaga are investigated. Both theories are found to be useful. Especially where women are older, premarital childbearing is often strategic and goaldirected, providing a sense of self-worth, labour and old-age security. In many other cases, however, it reflects spontaneous sexual activity generated by the undermining of social controls and inauspicious economic circumstances. Societal attitudes to premarital motherhood become less condemnatory after about age 25, as women are judged to have waited long enough for marriage.     

The Value of a Statistical Life: A Critical Review of Market Estimates Throughout the World

A substantial literature over the past thirty years has evaluated tradeoffs between money and fatality risks. These values in turn serve as estimates of the value of a statistical life. This article reviews more than 60 studies of mortality risk premiums from ten countries and approximately 40 studies that present estimates of injury risk premiums. This critical review examines a variety of econometric issues, the role of unionization in risk premiums, and the effects of age on the value of a statistical life. Our meta-analysis indicates an income elasticity of the value of a statistical life from about 0.5 to 0.6. The paper also presents a detailed discussion of policy applications of these value of a statistical life estimates and related issues, including risk-risk analysis.     

Do bettors prefer long shots because they are risk-lovers,or are they just overconfident?

This study examines whether bettors' risk preferences or overconfidence in choosing winners better explains their well documented preference for low-probability wagers. Although previous studies using racetrack data often suggest that risk-loving behavior explains long-shot preference, such data cannot distinguish between the alternative explanations. We use football betting data to make the comparison and find that overconfidence more closely fits the data. This result complements evidence of overconfidence from behavioral studies as well as stock-market models of overconfident noise traders.