We address an inference issue where the value of a covariate is measured at the date of the survey but is used to explain behavior that has occurred long before the survey. This causes bias because the value of the covariate does not follow the temporal order of events. We propose an expected likelihood approach to adjust for such bias and illustrate it with data on the effects of educational level achieved by the time of marriage on risks of divorce. For individuals with anticipatory educational level (whose reported educational level was completed after marriage), conditional probabilities of having attained the reported level before marriage are computed. These are then used as weights in the expected likelihood to obtain adjusted estimates of relative risks. For our illustrative data set, the adjusted estimates of relative risks of divorce did not differ significantly from those obtained from anticipatory analysis that ignores the temporal order of events. Our results are slightly different from those in two other studies that analyzed the same data set in a Bayesian framework, though the studies are not fully comparable to each other.

Objectives: In cats, although ultrasonography remains the preferred modality to evaluate the gastrointestinal tract, computed tomographic (CT) examination of the abdomen is commonly performed. However, a normal description of the gastrointestinal tract is lacking. This study describes the conspicuity and contrast enhancement pattern of the normal gastrointestinal tract in cats using dual-phase CT.

Materials and Methods: Pre- and dual-phase postcontrast (early scan at 30 seconds and late scan mean at 84 seconds) abdominal CT exams of 39 cats without history, clinical signs or diagnosis of gastrointestinal disease were reviewed. The gastrointestinal tract was examined for conspicuity and enhancement pattern using commercially available viewing software (Osirix, v.6.5.2), and diameters of 16 gastrointestinal segments were recorded and compared with published radiographic and ultrasonographic reference values.

Results: Of the 624 gastrointestinal segments, 530 (84.9%) were identified on precontrast studies and 545 (87.3%) segments on postcontrast studies. Of the gastrointestinal wall segments, 257 (41.2%) were identified on precontrast studies and 314 (50.3%) on postcontrast studies. Gastrointestinal segment diameters correlated well with published normal values, whereas wall thickness measurements usually were smaller compared with sonographic normal values. Early mucosal surface enhancement was frequently seen in the gastric cardia and fundus and ileocolic junction, and a mainly transmural wall enhancement in other gastrointestinal segments.

Clinical Significance: Dual-phase CT allows for the identification of gastrointestinal tract segments and walls in cats. Contrast enhancement improves conspicuity and demonstrates wall layering in the cardia, fundus and ileocolic junction.

We consider a Gaussian variational approximation of the posterior density in high-dimensional state space models. The number of parameters in the covariance matrix of the variational approximation grows as the square of the number of model parameters, so it is necessary to find simple yet effective parametrisations of the covariance structure when the number of model parame-ters is large. We approximate the joint posterior density of the state vectors by a dynamic factor model, having Markovian time dependence and a factor covariance structure for the states. This gives a reduced description of the dependence struc-ture for the states, as well as a temporal conditional independence structure sim-ilar to that in the true posterior. We illustrate the methodology on two examples. The first is a spatio-temporal model for the spread of the Eurasian collared-dove across North America. Our approach compares favorably to a recently proposed ensemble Kalman filter method for approximate inference in high-dimensional hi-erarchical spatio-temporal models. Our second example is a Wishart-based multi-variate stochastic volatility model for financial returns, which is outside the class of models the ensemble Kalman filter method can handle.

Playing outdoors in nature with peers has been attributed most importance forchildren's healthy development but is increasingly marginalised because of the attractiveness of screen-based play. Careful merging of digital technology into outdoor play environments rich on elements from nature could potentially help bridge digital play with more traditional play activities outdoors. A systematic comparison was made of outdoor play in more or less green settings, with and without digital installations or traditional play equipment. The separate and combined role of digital artefacts, play equipment and natural elements, were investigated, with particular focus on the effects of merging digital materials into nature. A group of children aged 6-8 were involved in a field study in a three-week period playing in a traditional playground, a forest and in a forest with digitally enhanced play artefacts. Children's play behaviour was evaluated using a behavioural tracking method, a questionnaire and a contextual interview with the children, and a physical activity measure, in combination with inventories including maps to document the design, and the ecological and physical status of the settings. The study documents differences in children's play behaviour across the three settings. It differs most between the digital forest setting and the forest setting regarding the play categories imaginative play, physical play and rule play and the digital forest setting stands out when it comes to expressive play and imaginative play. It is discussed how particular attributes in the physical environment influence the overall play flow and the interactive effects of natural material and digital material. Ecologically, the forest and the forest with digitally enhanced artefacts were more diverse than the traditional playground, but the natural material present was important for play in all settings.

We extend the existing family of flexible survival models by assembling models scattered across the literature into a more knit form and under the same umbrella. New special cases are obtained not only by constraining the shape and scale parameters of the extended generalized gamma (EGG) model to fixed constants, but also by imposing relationships (such as equality, reciprocal, and negative reciprocal) between them. Apart from common parametric distributions such as exponential, Weibull, gamma, and log normal, the further extended family includes Rayleigh, inverse Rayleigh, ammag, inverse ammag, and half-normal distributions. The models are applied, in a Bayesian framework, on time to entry into first marriage among Eritrean men and women based on data from the 2010 Population and Health Survey. The application demonstrates that the further extended family of distributions provides a wide range of alternatives for a baseline distribution in the analysis of survival data. The empirical results reveal that the inverse gamma model fits best the data for men. It also performs closely as good as the EGG model in the data for women as well as in the combined sample.

We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not follow the temporal order of events. We propose a dynamic Bayesian approach for modelling jointly the anticipatory covariate and the event of interest, and allowing the effects of the anticipatory covariate to vary over time. The issues are illustrated with data on the effects of education attained by the survey-time on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to elevated relative risks of divorce across educational levels. The results are partially in accordance with previous findings based on analyses of the same data set. More importantly, our findings provide new insights in that the bias due to anticipatory covariates varies over marriage duration.

We propose a dynamic Bayesian survival model for analyzing differentials in the timing of family initiation. Such formulation relaxes the strong assumption of constant hazard ratio in conventional proportional hazards models and allows covariate effects to vary over time. Inference is fully Bayesian, and efficient sequential Monte Carlo (particle filter) is used to sample from the posterior distribution. We illustrate the proposed model with data on entry into first marriage among Eritrean men and women surveyed in the 2010 Eritrean Population and Health Survey. Results from the conventional proportional hazards model indicate significant differences in family initiation among all educational and residential groups. In the dynamic model, on the other hand, only one educational and one residential group among the women and only one residential group among the men differ from their respective baseline groups. Since the empirical relative intensities of entry into first marriage vary across respondents’ ages, we argue that the proposed dynamic model captures differentials in family initiation more accurately.

Existing family of flexible survival models is further extended by assembling models scattered across the literature under the same umbrella. New special cases areobtained not only by constraining the shape and scale parameters to fixed constants,but also by imposing relationships (like equality, reciprocal, and negative reciprocal) between them. Apart from common parametric distributions like exponential,Weibull, gamma, and log-normal; the further extended family includes Rayleigh, inverse Rayleigh, Ammag, inverse Ammag, and half-normal distributions. The modelsare applied, in a Bayesian framework, on time to entry into first marriage amongEritrean men and women based on data from the 2010 Population and Health Survey.The further extended family of distributions provides a wide range of alternativesfor a baseline distribution in the analysis of survival data. The Inverse gamma modelfits the men-data best while it performs as good as the EGG model in the womenand combined sample.

Sequential probit approach is used to model differentials in the effects of women’seducation on parity progression in Ethiopia. Since reasons to have a first childmay differ from those to have, say, a second or third child, we allow the effects ofcovariates on the progression propensities to vary between parities in the same model.Data used for illustration come from the Ethiopian Mini Demographic and HealthSurvey of 2019 in which 8885 women from 11 regions were interviewed. Resultsshow that the sequential model provides more insight than conventional modelswhen exploring the association between education and parity progression. We alsofound similarities and differences in the effects of education on parity progressionamong the regions. A random effect term to account for women’s clustering withinhouseholds was significant in a model for the entire country but disappeared whenregion was included as a covariate in the model.

This chapter presents a multilevel extension of the Cox proportional hazards model where a shared frailty term is included to account for clustering of women within households. The extended model is used to analyze regional differences in the intensity of transition to parenthood among 15019 Ethiopian women aged 15–49 years old in the country’s Demographic and Health Survey of 2016. Women’s birth cohort, residence and educational level were used as background variables. Conventional Cox proportional hazards models and two multilevel models (with gamma distributed and log-normal distributed frailty terms) are fitted to data for the entire country and, separately, for each of the nine regions and two city administrations. We found that household frailty effects are fairly small in the nine regions but the log-normal frailties were significant in the entire country and the two city administrations which are relatively heterogeneous with inhabitants from many ethnic groups. We also found regional differences in the effects of the background variables on the intensity of transition to parenthood but the effects were generally stable across the three models in each region. Overall, we recommend use of multilevel survival models to account for clustering of women into households and proper care in the choice of distribution of the household random effects.

Multilevel Cox proportional hazards models with shared- and correlated-frailty termsto account for clustering of women within households is used to analyse regionaldifferences in transition to parenthood among 15019 Ethiopian women from thecountry’s Demographic and Health Survey (DHS) of 2016. We found that bothgamma- and lognormal-distributed shared household frailty effects are fairly smallin the nine regions though the log-normal frailties were significant in the entirecountry and the two city administrations which are relatively heterogeneous withinhabitants from many ethnic groups. Allowing for correlation between the randomeffects within households revealed significant clustering effects in almost all regions.We also found regional differences in the effects of background variables on the intensity of transition to parenthood. Overall, we recommend use of correlated frailtymodels to account for clustering of women into households and proper care in thechoice of distribution of the household random effects.

We examine the effects of the 3-points-for-a-win (3pfaw) rule in the football world. Data that form the basis of our analyses come from seven leagues around the world (Albania, Brazil, England, Germany, Poland, Romania, and Scotland) and consist of mean goals and proportions of decided matches over a period of about six years before- and about seven years after the introduction of the rule in the respective leagues. Bayesian change-point analyses and Shiryaev-Roberts tests show that the rule had no effects on the mean goals but, indeed, had increasing effects on the proportions of decided matches in most of the leagues studied. This, in turn, implies that while the rule has given teams the incentive to aim at winning matches, such aim was not achieved by scoring excess goals. Instead, it was achieved by scoring enough goals in order to win and, at the same time, defending enough in order not to lose. Our results are in accordance with recent findings on comparing the values of attack and defense - that, in top-level football, not conceding a goal is more valuable than scoring a single goal.

Studies on associations between education and demographic- orhealth-outcome have traditionally used womanís education (alone orin combination with partnerís education) as regressors in models relating covariates to the outcome. In this study, we adapt modelsdeveloped in the social mobility litearture to examine e§ects of differences between couplesíeducational levels on womenís propensity toutilise facilities (specically deliver in health facilities). Diagonal Reference Modeling (DRM) which accounts for origin (womanís education),destination (partnerís education), and ímobilityí(di§erences betweencouplesíeducational levels) is applied on data from Demographic andHealth Surveys (DHS) in Öve African countries (Angola, Ethiopia,Kenya, Namibia, and Nigeria). The results reveal strong e§ects ofeducational di§erences on womenís decision to deliver at health facilities. More importantly, such strong e§ects would be concealed if datais analyzed using standard modeling approaches. Use of Diagonal Reference Models is strongly recommended if correct policy interventionsare to be implemented.

We address a problem in causal inference from retrospective surveys where the value of a covariate is measured at the date of the survey but is used to explain behaviour that has occurred long before the survey. This causes bias because the anticipatory covariate does not follow the temporal order of events. We propose a Bayesian dynamic modelling approach that allows effects of the anticipatory covariate to vary over time and, thereby, restore its value at the event of interest. The issues are illustrated with data on the effects of anticipatory educational level on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to underestimation of the relative risks of divorce across educational levels. The results build, in part, on previous analyses of the same data set but also reveal that the degree of underestimation varies over marriage durations.

A multiplicative hazard model in the presence of anticipatory covariates is estimated by maximum likelihood. The case study concerns the e§ects of educational level on risks of divorce. For individuals with anticipatory educational levels, conditional probabilities of having attained the reported level before marriage are used as weights in the likelihood. The adjusted estimates of relative risks do not di§er signiÖcantly from those from anticipatory analysis.

In social mobility research, the diagonal reference model (DRM) is argued to best isolate the effect of social mobility from origin and destination status effects. In demographic research, standard analyses of the duration until an event occurs rely heavily on the appropriate use of covariates that change over time. We apply these best-practice methods to the study of social mobility and demographic outcomes in Sweden using register data that covers the years 1996–2012. The mortality analysis includes 1,024,142 women and 747,532 men and the fertility analysis includes 191,142 women and 164,368 men. We identify the challenges inherent in this combination and present strategies with an application to how social mobility is related to both fertility and mortality. Our application is successful at incorporating all requirements related to these methods. Our findings suggest, however, that certain data characteristics, such as a relatively high share of missing data, can be problematic. We also find that controlling for origin and destination status generally provides acceptable estimates of the mobility association in the specific case of Sweden and the relationship between social mobility and both fertility and mortality.