DDMSLT: A Computer Program for Estimating the Duration-Dependent Multistate Life Table Model

Demographic transitions from one state to another -- such as from married to divorced or from working to not-working -- are often considered to depend not only on age but also on the duration in the status. Yet the traditional life table methodology to describe these phenomena can only account for one demographic time dimension (mostly age). This paper is in line with recent IIASA efforts to generalize the life table approach to account for at least two relevant demographic time dimensions (age and duration). It presents a computer program that conveniently estimates the duration-dependent multistate life table model which was suggested in earlier papers by Douglas Wolf

BIVOPROB: A Computer Program for Maximum-Likelihood Estimation of Bivariate Ordered-Probit Models for Censored Data

Despite the large number of models devoted to the statistical analysis of censored data, relatively little attention has been given to the case of censored discrete outcomes. In this paper, the author presents a technical description and user's guide to a computer program for estimating bivariate ordered-probit models for censored and uncensored data. The model and program are currently being applied in an analysis of World Fertility Survey data for Europe and the United States, and the results of this work will be described in a forthcoming IIASA working paper

Desired and Excess Fertility in Europe and the United States: Indirect Estimates from World Fertility Survey Data

How many children couples want, and how many unwanted births occur, is essential information for the guiding of family planning program in Leas Developed Countries, and for measures to encourage childbearing in More Developed Countries. The World Fertility Survey (WFS) was organized for ascertaining such facts; it is said to be the most ambitious piece of social research ever undertaken, with field work by statistical agencies in nearly 60 countries, coordinated by a central star of unchallenged credentials in statistics and demography. The major effort was in the LDCs, but 16 European countries and the United States were also included, and it is from these latter that have come the data on which the present working paper is based. Apparently the interpretation of the results requires even more technical skill than the original surveys did. The difficulty to be overcome is that births unwanted at the time they occurred come to be very much wanted afterwards. Hence the retrospective statements of women on how many of the children born to them were unwanted would not be of much value, even if it were feasible to request such statements. It is this gap in information that the author has filled, using from the WFS only the statements on how many children were already born, and how many further children were expected. As an example of the estimates here published, the United States showed 28.4 per cent of women with two children, and of these 4.8 per cent wanted none, and 2.7 per cent wanted one child. The 28.4 per cent is a directly observed number; the 4.8 and the 2.7 per cent are inferred by the indirect technique here expounded. It turns out that the women not in the labor force have higher proportions of unwanted births than those in the labor force; even at given levels of education the former may be thought of as more traditional. Working women, moreover, have a stronger incentive to be careful than housewives. The use of this technique on data available in the late 1970s would have forecast the fall in the late 19808, if it were supposed that sophistication in birth control is spreading through the population. On the same supposition the possibility of further falls in fertility is one of the conclusions from the figures given here, more for some countries than for others

Reproducibility in the absence of selective reporting : An illustration from large-scale brain asymmetry research

Altres ajuts: Max Planck Society (Germany).The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function