PubMed · 9115013
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J B Cologne. 1997. Counterintuitive matching.. https://pubmed.ncbi.nlm.nih.gov/9115013/
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Stone's method for assessing disease risk around a point source through isotonic regression is routinely used in spatial epidemiology. It is useful in situations where the relationship of risk with exposure (distance being commonly used as a surrogate variable) is assumed monotonic but otherwise of unknown form. This paper extends this method to non-spatial epidemiology, where typically two or more risk factors are present. The methodology described is based on the additive isotonic model approach of Bacchetti; versions appropriate to count (Poisson) data and case-control (binomial) data are described. In both cases, adjustment for covariates is incorporated, and a Monte Carlo method of hypothesis testing and interval estimation is presented. The methodology is illustrated through a case-control example concerning the analysis of the possible effect of preconceptional external ionizing radiation doses on the sex ratio at birth among children of fathers working at the Sellafield nuclear installation, Cumbria, U.K.
There is a lot of confusion in the literature about the "differences" between "model-based" and "model-free" methods and about which approach is better suited for detection of the genes predisposing to complex multifactorial phenotypes. By starting from first principles, we demonstrate that the differences between the two approaches have more to do with study design than statistical analysis. When simple data structures are repeatedly ascertained, no assumptions about the genotype-phenotype relationship need to be made for the analysis to be powerful, since simple data structures admit only a small number of df. When more complicated and/or heterogeneous data structures are ascertained, however, the number of df in the underlying probability model is too large to have a powerful, truly "model-free" test. So-called "model-free" methods typically simplify the underlying probability model by implicitly assuming that, in some sense, all meioses connecting two affected individuals are informative for linkage with identical probability and that the affected individuals in a pedigree share as many disease-predisposing alleles as possible. By contrast, "model-based" methods add structure to the underlying parameter space by making assumptions about the genotype-phenotype relationship, making it possible to probabilistically assign disease-locus genotypes to all individuals in the data set on the basis of the observed phenotypes. In this study, we demonstrate the equivalence of these two approaches in a variety of situations and exploit this equivalence to develop more powerful and efficient likelihood-based analogues of "model-free" tests of linkage and/or linkage disequilibrium. Through the use of a "pseudomarker" locus to structure the space of observations, sib-pairs, triads, and singletons can be analyzed jointly, which will lead to tests that are more well-behaved, efficient, and powerful than traditional "model-free" tests such as the affected sib-pair, transmission/disequilibrium, haplotype relative risk, and case-control tests. Also described is an extension of this approach to large pedigrees, which, in practice, is equivalent to affected relative-pair analysis. The proposed methods are equally applicable to two-point and multipoint analysis (using complex-valued recombination fractions).
PURPOSE: To better predict permanent complications from arteriovenous malformation (AVM) radiosurgery. METHODS AND MATERIALS: Data from 85 AVM patients who developed symptomatic complications following gamma knife radiosurgery and 337 control patients with no complications were evaluated as part of a multi-institutional study. Of the 85 patients with complications, 38 patients were classified as having permanent symptomatic sequelae (necrosis). AVM marginal doses varied from 10-35 Gy and treatment volumes from 0.26-47.9 cc. Median follow-up for patients without complications was 45 months (range: 24-92). RESULTS: Multivariate analysis of the effects of AVM location and the volume of tissue receiving 12 Gy or more (12-Gy-Volume) allowed construction of a significant postradiosurgery injury expression (SPIE) score. AVM locations in order of increasing risk and SPIE score (from 0-10) were: frontal, temporal, intraventricular, parietal, cerebellar, corpus callosum, occipital, medulla, thalamus, basal ganglia, and pons/midbrain. The final statistical model predicts risks of permanent symptomatic sequelae from SPIE scores and 12-Gy-Volumes. Prior hemorrhage, marginal dose, and Marginal-12-Gy-Volume (target volume excluded) did not significantly improve the risk-prediction model for permanent sequelae (p >/= 0.39). CONCLUSION: The risks of developing permanent symptomatic sequelae from AVM radiosurgery vary dramatically with location and, to a lesser extent, volume. These risks can be predicted according to the SPIE location-risk score and the 12-Gy-Volume.