269 research outputs found

    Prikaz knjige: Zorislav Kaleb - Djelovanje kaznene presude na parnični postupak /Vizura, Zagreb, 2008./

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    Nedavno je u izdanju nakladničke kuće Vizura objavljena knjiga mr. sc. Zorislava Kaleba, suca Kaznenog odjela Općinskog suda u Zagrebu, Djelovanje kaznene presude na parnični postupak s podnaslovom Vezanost parničnog suda za pravomoćnu presudu kaznenog suda. Knjiga Djelovanje kaznene presude na parnični postupak predstavlja autorov izra¬đeni i obranjeni magistarski rad na poslijediplomskom znanstvenom studiju iz trgovačkog prava i prava društava na Pravnom fakultetu Sveučilišta u Zagrebu dana 4. travnja 2006. godine na temu Vezanost parničnog suda za pravomoćnu presudu kaznenog suda pred Povjerenstvom koje su činili akademik Jakša Barbić, predsjednik Povjerenstva, prof. dr. se. Mihajlo Dika kao mentor, te prof. dr. se. Davor Krapac i dr. se. Branko Vukmir kao članovi Povjerenstva. Isti rad je autor u međuvremenu dijelom prilagodio radi objave kao monografije, a također je unio i izmjene koje su se u međuvremenu dogodile našem pozitivnom zakonodavstvu. U knjizi se obrađuje odnos između parničnog i kaznenog postupka, vezanost par¬ničnog suda za pravomoćnu osuđujuću presudu kaznenog suda po tužbi iz istog događaja, prejudicijelno djelovanje presude kaznenog suda na parnični postupak, djelovanje kaznene presude kao pravno relevantne činjenice u parničnom postupku, prekid postupka po od¬luci suda u parničnom postupku, donošenje pravomoćne kaznene presude kao razlog za ponavljanje parničnog postupka, te neki zaključni prijedlozi de legeferenda. Rad dijelom komparativno obrađuje istu materiju i u nekim drugim zakonodavstvima u opsegu koliko je to zanimljivo za našeg praktičara. Knjiga je rezultat dvogodišnjeg istraživanja autora prvenstveno odluka Županijskog suda u Zagrebu i Vrhovnog suda Republike Hrvatske, te potom i dostupnih odluka drugih domaćih i stranih sudova

    Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression

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    Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time—for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4–96.2% and the slope 94.5–96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson’s cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide

    Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

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    Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual’s lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual’s lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour

    Polygenic risk for depression, anxiety and neuroticism are associated with the severity and rate of change in depressive symptoms across adolescence

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    Background Adolescence marks a period where depression will commonly onset. Twin studies show that genetic influences play a role in how depression develops and changes across adolescence. Recent genome‐wide association studies highlight that common genetic variants – which can be combined into polygenic risk scores (PRS) – are also implicated in depression. However, the role of PRS in adolescent depression and changes in adolescent depression is not yet understood. We aimed to examine associations between PRS for five psychiatric traits and depressive symptoms measured across adolescence using cross‐sectional and growth‐curve models. The five PRS were as follows: depression (DEP), major depressive disorder (MDD), anxiety (ANX), neuroticism (NEU) and schizophrenia (SCZ). Methods We used data from over 6,000 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) to examine associations between the five PRS and self‐reported depressive symptoms (Short Mood and Feelings Questionnaire) over 9 occasions from 10 to 24 years. The PRS were created from well‐powered genome‐wide association studies conducted in adult populations. We examined cross‐sectional associations between the PRS at each age and then again with longitudinal trajectories of depressive symptoms in a repeated measures framework using multilevel growth‐curve analysis to examine the severity and the rate of change. Results There was strong evidence that higher PRS for DEP, MDD and NEU were associated with worse depressive symptoms throughout adolescence and into young adulthood in our cross‐sectional analysis, with consistent associations observed across all nine occasions. Growth‐curve analyses provided stronger associations (as measured by effect sizes) and additional insights, demonstrating that individuals with higher PRS for DEP, MDD and NEU had steeper trajectories of depressive symptoms across development, all with a greater increasing rate of change during adolescence. Evidence was less consistent for the ANX and SCZ PRS in the cross‐sectional analysis, yet there was some evidence for an increasing rate of change in adolescence in the growth‐curve analyses with the ANX PRS. Conclusions These results show that common genetic variants as indexed by varying psychiatric PRS show patterns of specificity that influence both the severity and rate of change in depressive symptoms throughout adolescence and then into young adulthood. Longitudinal data that make use of repeated measures designs have the potential to provide greater insights how genetic factors influence the onset and persistence of adolescent depression
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