Bayesian spatial analysis of demographic survey data

In this paper we analyze the spatial patterns of the risk of unprotected sexual intercourse for Italian women during their initial experience with sexual intercourse. We rely on geo-referenced survey data from the Italian Fertility and Family Survey, and we use a Bayesian approach relying on weakly informative prior distributions. Our analyses are based on a logistic regression model with a multilevel structure. The spatial pattern uses an intrinsic Gaussian conditional autoregressive (CAR) error component. The complexity of such a model is best handled within a Bayesian framework, and statistical inference is carried out using Markov Chain Monte Carlo simulation. In contrast with previous analyses based on multilevel model, our approach avoids the restrictive assumption of independence between area effects. This model allows us to borrow strength from neighbors in order to obtain estimates for areas that may, on their own, have inadequate sample sizes. We show that substantial geographical variation exists within Italy (Southern Italy has higher risks of unprotected first-time sexual intercourse). The findings are robust with respect to the specification of the prior distribution. We argue that spatial analysis can give useful insights on unmet reproductive health needs.contraceptive use, FFS, hierarchical Bayesian modeling, Italy, Monte Carlo Markov Chain, multilevel statistical models, spatial statistical demography

The Uniformly Most Powerful Invariant Test for the Shoulder Condition in Point Transect Sampling

Estimating population abundance is of primary interest in wildlife population studies. Point transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a given distance from the observer). The two most popular models for this function are the half-normal model and the negative exponential model. However, it appears that the estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is almost certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Therefore, testing whether such a hypothesis is consistent with the data at hand should be a primary concern in every study concerning the estimation of animal abundance. In this paper we propose a test for this purpose. This is the uniformly most powerful test in the class of the scale invariant tests. The asymptotic distribution of the test statistic is calculated by utilising both the half-normal and negative exponential model while the critical values and the power are tabulated via Monte Carlo simulations for small samples. Finally, the procedure is applied to two datasets of chipping sparrows collected at the Rocky Mountain Bird Observatory, Colorado..Point Transect Sampling, Shoulder Condition, Uniformly Most Powerful Invariant Test, Asymptotic Critical Values, Monte Carlo Critical Values

On the Uniformly Most Powerful Invariant Test for the Shoulder Condition in Line Transect Sampling

In wildlife population studies one of the main goals is estimating the population abundance. Line transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a given distance from the observer). Two common models for this function are the half-normal model and the negative exponential model. The estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is almost certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Therefore, testing whether such a hypothesis is consistent with the data is a primary concern in every study aiming at estimating animal abundance. In this paper we propose a test for this purpose. This is the uniformly most powerful test in the class of the scale invariant tests. The asymptotic distribution of the test statistic is worked out by utilising both the half-normal and negative exponential model while the critical values and the power are tabulated via Monte Carlo simulations for small samples. .Line Transect Sampling, Shoulder Condition, Uniformly Most Powerful Invariant Test, Asymptotic Critical Values, Monte Carlo Critical Values

Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse

In this paper we analyze the spatial patterns of the risk of unprotected sexual intercourse for Italian women during their initial experience with sexual intercourse. We rely on geo-referenced survey data from the Italian Fertility and Family Survey, and we use a Bayesian approach relying on weakly informative prior distributions. Our analyses are based on a logistic regression model with a multilevel structure. The spatial pattern uses an intrinsic Gaussian conditional autoregressive (CAR) error component. The complexity of such a model is best handled within a Bayesian framework, and statistical inference is carried out using Markov Chain Monte Carlo simulation. In contrast with previous analyses based on multilevel model, our approach avoids the restrictive assumption of independence between area effects. This model allows us to borrow strength from neighbors in order to obtain estimates for areas that may, on their own, have inadequate sample sizes. We show that substantial geographical variation exists within Italy (Southern Italy has higher risks of unprotected first-time sexual intercourse), and that the spatial pattern is stable across birth cohorts. The findings are robust with respect to the specification of the prior distribution. We argue that spatial analysis can give useful insights on unmet reproductive health needs. (KEYWORDS: spatial statistical demography, contraceptive use, hierarchical Bayesian modeling, Monte Carlo Markov Chain, multilevel statistical models, Italy, FFS)Italy, contraceptive usage

Spatial profiles in the analysis of event histories: an application to first sexual intercourse in Italy

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How important are household demographic characteristics to explain private car use patterns? A multilevel approach to Austrian data

Private car use is one of the major contributors to pollution in industrialised countries. It is therefore important to understand the factors that determine the demand for car use. In explaining the variability in car use, it is important to take into account household demographic characteristics and local and regional differences in infrastructure, in addition to the economic variables commonly used in the prevailing literature on the topic. The appropriate tool to explain car ownership and car use is, therefore, a multilevel statistical approach. An Austrian household survey from 1997 finds that household characteristics such as age, gender, education and employment of the household head, household size and housing quality can effect the variability of car ownership and car use. The same survey also gives a clear indication of regional heterogeneity. This heterogeneity persists when we controlled for the variability of regional economic welfare and infrastructure as indicated by population density.

A Geostatistical Approach to Define Guidelines for Radon Prone Area Identification

Radon is a natural radioactive gas known to be the main contributor to natural background radiation exposure and the major leading cause of lung cancer second to smoking. Indoor radon concentration levels of 200 and 400 Bq/m3 are reference values suggested by the 90/143/Euratom recommendation, above which mitigation measures should be taken in new and old buildings, respectively, to reduce exposure to radon. Despite this international recommendation, Italy still does not have mandatory regulations or guidelines to deal with radon in dwellings. Monitoring surveys have been undertaken in a number of western European countries in order to assess the exposure of people to this radioactive gas and to identify radon prone areas. However, such campaigns provide concentration values in each single dwelling included in the sample, while it is often necessary to provide measures of the pollutant concentration which refer to sub-areas of the region under study. This requires a realignment of the spatial data from the level at which they are collected (points) to the level at which they are necessary (areas). This is known as change of support problem. In this paper, we propose a methodology based on geostatistical simulations in order to solve this problem and to identify radon prone areas which may be suggested for national guidelines.Radon Prone Areas, kriging, geostatistical conditional simulation, change of support problem

Simulating interventions in graphical chain models for longitudinal data

Simulating the outcome of an intervention is a central problem in many fields as this allows decision-makers to quantify the effect of any given strategy and, hence, to evaluate different schemes of actions. Simulation is particularly relevant in very large systems where the statistical model involves many variables that, possibly, interact with each other. In this case one usually has a large number of parameters whose interpretation becomes extremely difficult. Furthermore, in a real system, although one may have a unique target variable, there may be a number of variables which might, and often should, be logically considered predictors of the target outcome and, at the same time, responses of other variables of the system. An intervention taking place on a given variable, therefore, may affect the outcome either directly and indirectly though the way in which it affects other variables within the system. Graphical chain models are particularly helpful in depicting all of the paths through which an intervention may affect the final outcome. Furthermore, they identify all of the relevant conditional distributions and therefore they are particularly useful in driving the simulation process. Focussing on binary variables, we propose a method to simulate the effect of an intervention. Our approach, however, can be easily extended to continuous and mixed responses variables. We apply the proposed methodology to assess the effect that a policy intervention may have on poorer health in early adulthood using prospective data provided by the 1970 British Birth Cohort Study (BCS70).chain graph, conditional approach, Gibbs Sampling, Simulation of interventions, age at motherhood, mental health

Modelling the distribution of health related quality of life of advancedmelanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood