Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.Comment: Published in at http://dx.doi.org/10.1214/14-STS499 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mathematical modelling of tissue-engineering angiogenesis

We present a mathematical model for the vascularisation of a porous scaffold following implantation in vivo. The model is given as a set of coupled non-linear ordinary differential equations (ODEs) which describe the evolution in time of the amounts of the different tissue constituents inside the scaffold. Bifurcation analyses reveal how the extent of scaffold vascularisation changes as a function of the parameter values. For example, it is shown how the loss of seeded cells arising from slow infiltration of vascular tissue can be overcome using a prevascularisation strategy consisting of seeding the scaffold with vascular cells. Using certain assumptions it is shown how the system can be simplified to one which is partially tractable and for which some analysis is given. Limited comparison is also given of the model solutions with experimental data from the chick chorioallantoic membrane (CAM) assay

Time-varying risk, interest rates, and exchange rates in general equilibrium

Under mild assumptions, the data indicate that fluctuations in nominal interest rate differentials across currencies are primarily fluctuations in time-varying risk. This finding is an immediate implication of the fact that exchange rates are roughly random walks. If most fluctuations in interest differentials are thought to be driven by monetary policy, then the data call for a theory which explains how changes in monetary policy change risk. Here we propose such a theory based on a general equilibrium monetary model with an endogenous source of risk variation - a variable degree of asset market segmentation.Asset pricing

A Story of Change: Adult Learners’ Experiences of Questioning their Beliefs and Assumptions in a Graduate Course in Reflective Practice

The purpose of this study was to understand the experience of students in a graduate course on the topic of Reflective Practice (RP). A phenomenological method was utilized to frame interviews with eight students discussing challenges to their beliefs and assumptions that arose during the course. Based on a thematic analysis of the interview data, three major figural themes and one ground theme emerged. The three figural themes indicated that participants experienced changes in their beliefs and assumptions about student-to-student and student-to-teacher relationships and about similarities and differences among their own and others’ belief systems, in addition to their own comfort with a highly interactive teaching and learning environment. For example, participants’ initial beliefs about differences in student and teacher expertise, related authority, and early discomfort with the RP process gave way to beliefs about multiple expertise, equality, and increased comfort with dialoguing about personal and controversial topics. The ground theme indicated that time was a key factor in participants’ experiences: that is, changes in their beliefs and assumptions occurred over time and appeared to extend past the end of the course although no attempt was made to investigate long-term outcomes of participants’ experiences. The findings suggest a need for further research on the sustainability of changes in beliefs and assumptions beyond the course experience, the possibility of replicating the results in other areas of study and in courses with more diverse demographics, and inquiry into how students’ beliefs and assumptions change during shorter intervals of the teaching and learning process. In the area of practice, the findings suggest that instructors interested in gearing their pedagogy to student subject matter needs might also consider inquiring into the students’ initial beliefs and assumptions about teaching and learning, as well as how their own assumptions and beliefs frame their interactions with students

Real-Time Analysis of Oil Price Risks Using Forecast Scenarios

Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the baseline forecast under alternative scenarios about future oil demand and oil supply conditions. Such scenario analysis is of central importance for end-users of oil price forecasts interested in evaluating the risks underlying these forecasts. We show how policy-relevant forecast scenarios can be constructed from recently proposed structural vector autoregressive models of the global oil market and how changes in the probability weights attached to these scenarios affect the upside and downside risks embodied in the baseline real-time oil price forecast. Such risk analysis helps forecast users understand what assumptions are driving the forecast. An application to real-time data for December 2010 illustrates the use of these tools in conjunction with reduced-form vector autoregressive forecasts of the real price of oil, the superior realtime forecast accuracy of which has recently been established.Econometric and statistical methods; International topics

SimpactCyan 1.0 : an open-source simulator for individual-based models in HIV epidemiology with R and Python interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework

Nonparametric Inference for Unbalanced Time Series Data

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/Bootstrap theory applies, but at the expense of throwing away data and perhaps losing efficiency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid. We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data. We also discuss efficiency and power. Unbalanced data of the type we consider are quite common in financial panel data, see, for example, Connor and Korajczyk (1993). These data also occur in cross-country studies.Bootstrap, efficient, HAC estimation, missing data, subsampling.