Economic status proxies in studies of fertility in developing countries: Does the measure matter?

This paper investigates the consequences of using different economic status proxies on the estimated impact of economic status and other determinants of fertility. Using micro survey data from Ghana and Peru, we find that the proxies for income that best predict fertility are a principal components score of the ownership of consumer durable goods and a simple sum of ownership of these durable goods. Furthermore, the choice of the proxy generally has a minor influence on the predicted effects of the control variables. We compare the results from using a restricted set of proxies, such as those available in the Demographic and Health Surveys, with the results obtained using a lengthier set of proxies. Our results suggest implications beyond fertility analyses by providing researchers with an awareness of the sensitivity of microanalyses to the treatment of economic status. Our results also suggest practical recommendations for the collection of survey data.     

Neither individualism nor anti-individualism: The coevolution of social systems and psychic systems

Major paradigms in sociology and social sciences usually embrace either individualism or anti-individualism as fundamental worldview. This paper explores a third way between individualism and anti-individualism developed by German sociologist Niklas Luhmann in his systems theory. Luhmann treats actors or individuals as psychic systems and he distinguishes them from social systems. In a nutshell, social systems produce communication, whereas psychic systems produce consciousness. In line with anti-individualism, Luhmann therefore argues that social systems are irreducible to psychic systems and their actions. On the other hand, Luhmann joins ranks with individualism to assert that psychic systems are not rigorously constrained by social systems. Ultimately, Luhmann explains that social systems and psychic systems are part of each other's environment and that each type of systems provides the ecological conditions that the other type depends on to emerge and grow. To discuss this third way between individualism and anti-individualism, the paper examines more specifically two central points in Luhmann's theory: (1) how social systems and psychic systems are separated from each other, and (2) how social systems and psychic systems are coevolving.     

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.     

Predicting risk for adverse health events using random forest

Estimation of person-specific risk for adverse health events in medicine has been approached almost exclusively using parametric statistical methods. Random forest is a machine learning method based on tree ensembles that is completely nonparametric and for this reason may be better suited for risk prediction. An introduction to a random forest is provided with a focus on its application to risk prediction. Using data from a total joint replacement registry, we illustrate risk prediction for the binary outcome of 90-day mortality following implantation, as well as time to device failure for aseptic reasons with the competing risk of mortality. Using the methods described in this paper, the random forest could be applied to risk prediction in a wide variety of medical fields. Issues related to implementation are discussed.