The surprising implications of familial association in disease risk

Background: A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward. Methods: We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases. Results: We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population. Conclusions: The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.Comment: 17 pages, 5 figure

Tears, remorse and reparation in Henrik Ibsen's Peer Gynt

For more than 100 years, Henrik Ibsen’s Peer Gynt has been interpreted in the light of Søren Kierkegaard. With a problematic self as an essence of the play, one has emphasized a Kierkegaardian choice, necessary for Peer to become an integrated person. This paper challenges these interpretations by focusing on mourning as a way to develop the self in Peer Gynt. The reading reveals a striking correspondence, concerning structure and dynamics, between Peer’s way of dealing with feelings like sadness, guilt and remorse and Klein’s model of paranoid-schizoid and depressive position. Peer is facing painful feelings throughout the play. He identifies them quite easily, but is not able to tolerate the pain and avoids them with omnipotent fantasies, manic manoeuvres and denial. Hence, no reparation through mourning takes place, his development is arrested and he is unable to form a genuine love relationship with Solveig. The reading demonstrates an impressively profound complexity in Ibsen’s representation of Peer’s character, and a striking richness in detail in how it corresponds to Klein’s anthropology

Additive Intensity Regression Models in Corporate Default Analysis

We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of model checking techniques to identify misspecifications. In our final model, we find evidence of time-variation in the effects of distance-to-default and short-to-long term debt. Also we identify interactions between distance-to-default and other covariates, and the quick ratio covariate is significant. None of our macroeconomic covariates are significant

Can framing change individual attitudes towards immigration?

Does framing change individual attitudes towards immigration? This thesis analyzes the effect of providing information about the unemployment- and employment rate of immigrants in Norway, as well as information about the impact the rates may have on the Norwegian welfare state. I expose some treatment groups to statistics of the rates, and others to information about how the rates may affect the Norwegian welfare state. I conduct a randomized survey experiment with more than 1,000 respondents to investigate whether framing of the behavior (unemployed or employed) and/or the impact of this behavior (cost or benefit) changes views and attitudes towards immigration policy. These views and attitudes may reflect underlying beliefs and preferences, which again may be situation-dependent. The paper finds that the respondents internalize the framing, and that information about the employment rate of immigrants in Norway (60 percent) causes individuals to rate their preferences for immigration policy more strictly. This suggests that people react negatively to a seemingly low employment rate of immigrants. The results indicate that the experimental design activates certain beliefs and preferences for immigration, and that framing causes a short-term change in preferences for immigration policy. Since individual preferences are a determinant of policy outcome, and immigration policy is an important domain for political parties, my results implicate that providing negative information about the behavior of immigration right before an election, may affect the results of the election. More generally, various types of information may influence how people perceive immigration and are accordingly important for policy outcomes and integration.MasteroppgaveECON39

Nonparametric survival analysis of epidemic data

This paper develops nonparametric methods for the survival analysis of epidemic data based on contact intervals. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. We show that the Nelson-Aalen estimator produces an unbiased estimate of the contact interval cumulative hazard function when who-infects-whom is observed. When who-infects-whom is not observed, we average the Nelson-Aalen estimates from all transmission networks consistent with the observed data using an EM algorithm. This converges to a nonparametric MLE of the contact interval cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic. In an appendix, we show that these methods extend chain-binomial models to continuous time.Comment: 30 pages, 6 figure

Graphical models for marked point processes based on local independence

A new class of graphical models capturing the dependence structure of events that occur in time is proposed. The graphs represent so-called local independences, meaning that the intensities of certain types of events are independent of some (but not necessarily all) events in the past. This dynamic concept of independence is asymmetric, similar to Granger non-causality, so that the corresponding local independence graphs differ considerably from classical graphical models. Hence a new notion of graph separation, called delta-separation, is introduced and implications for the underlying model as well as for likelihood inference are explored. Benefits regarding facilitation of reasoning about and understanding of dynamic dependencies as well as computational simplifications are discussed.Comment: To appear in the Journal of the Royal Statistical Society Series

Does Cox analysis of a randomized survival study yield a causal treatment effect?

The final publication (Aalen, Odd O., Richard J. Cook, and Kjetil Røysland. Does Cox analysis of a randomized survival study yield a causal treatment effect?. Lifetime Data Analysis 21(4) (2015): 579-593. DOI: 10.1007/s10985-015-9335-y) is available at http://link.springer.com/article/10.1007/s10985-015-9335-yStatistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferable in general for estimation.Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626