On dependency in double-hurdle models

Dependency, Weak identification, Fisher's information,

Modeling time-to-cure from severe acute malnutrition: application of various parametric frailty models

BACKGROUND: In developing countries about 3.5% of children aged 0–5 years are victims of severe acute malnutrition (SAM). Once the morbidity has developed the cure process takes variable period depending on various factors. Knowledge of time-to-cure from SAM will enable health care providers to plan resources and monitor the progress of cases with SAM. The current analysis presents modeling time-to-cure from SAM starting from the day of diagnosis in Wolisso St. Luke Catholic hospital, southwest Ethiopia. METHODS: With the aim of coming up with appropriate survival (time-to-event) model that describes the SAM dataset, various parametric clustered time-to-event (frailty) models were compared. Frailty model, which is an extension of the proportional hazards Cox survival model, was used to analyze time-to-cure from SAM. Kebeles (villages) of the children were considered as the clustering variable in all the models. We used exponential, weibull and log-logistic as baseline hazard functions and the gamma as well as inverse Gaussian for the frailty distributions and then based on AIC criteria, all models were compared for their performance. RESULTS: The median time-to-cure from SAM cases was 14 days with the maximum of 63 days of which about 83% were cured. The log-logistic model with inverse Gaussian frailty has the minimum AIC value among the models compared. The clustering effect was significant in modeling time-to-cure from SAM. The results showed that age of a child and co-infection were the determinant prognostic factors for SAM, but sex of the child and the type of malnutrition were not significant. CONCLUSIONS: The log-logistic with inverse Gaussian frailty model described the SAM dataset better than other distributions used in this study. There is heterogeneity between the kebeles in the time-to-cure from SAM, indicating that one needs to account for this clustering variable using appropriate clustered time-to-event frailty models

No Causal Effect of Unemployment on Smoking? A German Panel Study

Schunck R, Rogge B. No Causal Effect of Unemployment on Smoking? A German Panel Study. International Journal of Public Health. 2012;57(6):867-874.Objectives This study analyses the effects of different unemployment durations on smoking behaviour in Germany by investigating smoking take-up, relapse, quitting and smoking intensity. Methods Longitudinal data from the German Socio-Economic Panel from the years 1998, 2001, 2002, 2004, 2006, and 2008 were used to examine the effect of unemployment (52,940 observations from 17,028 respondents, aged 17–65 years). Unemployment duration was measured at 1–6, 7–12, 13–24, and 24+ months. Effects were estimated using zero-inflated negative binomial regressions and fixed effects logistic panel regressions. Results The zero-inflated negative binomial regression models suggest that the likelihood of smoking increases with unemployment, while smoking intensity is not affected. However, fixed effects logistic regression models demonstrate that unemployment is neither a significant predictor for taking up smoking, relapsing, nor quitting. Conclusions The results indicate that in Germany, there is no direct causal effect of unemployment on smoking behaviour. The observed relationship between smoking and unemployment appears to be driven by stable, unobserved differences between employed and unemployed respondents

Medical history of discordant twins and environmental etiologies of autism

The environmental contributions to autism spectrum disorder (ASD) and their informative content for diagnosing the condition are still largely unknown. The objective of this study was to investigate associations between early medical events and ASD, as well as autistic traits, in twins, to test the hypothesis of a cumulative environmental effect on ASD risk. A total of 80 monozygotic (MZ) twin pairs (including a rare sample of 13 twin pairs discordant for clinical ASD) and 46 dizygotic (DZ) twin pairs with varying autistic traits, were examined for intra-pair differences in early medical events (for example, obstetric and neonatal factors, first year infections). First, differences in early medical events were investigated using multisource medical records in pairs qualitatively discordant for ASD. The significant intra-pair differences identified were then tested in relation to autistic traits in the remaining sample of 100 pairs, applying generalized estimating equations analyses. Significant association of the intra-pair differences in the MZ pairs were found for the cumulative load of early medical events and clinical ASD (Z = - 2.85, P = 0.004) and autistic traits (ß = 78.18, P = 0.002), as well as infant dysregulation (feeding, sleeping abnormalities, excessive crying and worriedness), when controlling for intelligence quotient and attention deficit hyperactivity disorder comorbidity. The cumulative load of early medical events in general, and infant dysregulation in particular, may index children at risk of ASD owing to non-shared environmental contributions. In clinical practice, these findings may facilitate screening and early detection of ASD