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O Davidov

Publications and source records attributed to O Davidov.

8 recordsLinked to original sources
PubMed2001

Referent sampling, family history and relative risk: the role of length-biased sampling.

Familial risk of disease is often assessed using case control studies based on referent databases. A referent database is a collection of family histories of cases typically assembled as a result of one family member being diagnosed with disease. This sampling scheme is equivalent to sampling families proportional to their size. The larger the family, the greater the probability of finding the family in the referent registry. This phenomena is known as length-biased sampling. The consequence of this kind of sampling is to bias the regression estimate associated with family history. The estimate is typically inflated in comparison to what is true for the actual population.

Journal Article↗
PubMed2000

Designing cancer prevention trials: a stochastic model approach.

There is growing interest in the design and implementation of cancer prevention trials. The key idea is to have agents which interfere with carcinogenesis and/or the preclinical stage. In this article we develop multi-stage stochastic models for the planning of cancer prevention trials. For known inputs it is possible to calculate the incidence of disease for the control and intervention groups. Consequently we find designs that balance the required sample size and follow-up time while guaranteeing prespecified error probabilities. Moreover such models can incorporate the mode of action of the intervention as well as compliance. The model has been applied to breast cancer to determine the implications for planning breast cancer intervention trials. Although the model addresses issues in cancer prevention, it is quite general and may be suitable for other chronic diseases.

Age Factors↗
PubMed1998

Asymptomatic incidence and duration of prostate cancer.

Prostate cancer is known as a disease with an extremely high prevalence relative to its clinical incidence in the population. The combination of preclinical incidence and duration that could yield this phenomenon is of tremendous interest to researchers trying to understand the natural history of the disease and to develop efficient screening strategies. In this article, the authors present estimates of the age-specific asymptomatic incidence and average preclinical duration of prostate cancer. The methodological approach is to first estimate the age-specific incidence of new (stage AI) prostate cancers using preclinical prevalence data from autopsy studies performed between 1941 and 1964 and clinical incidence data for the years 1960-1986 from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Then, the preclinical prevalence estimates are divided by the derived preclinical incidence estimates to yield estimates of the average duration of asymptomatic disease. The estimated mean duration among white men is between 11 and 12 years and appears to be approximately 1 year shorter for blacks than for whites. Comparison of the lifetime risks of preclinical and clinical disease suggests that approximately 75% of prostate cancers will never become diagnosed if clinical incidence remains at levels observed in 1984-1986, prior to the introduction of prostate-specific antigen (PSA) screening in the population.

Adult↗
PubMed1998

Urn sampling and the proportional hazard model.

This paper investigates the urn sampling analogue for the score statistic relating survival to covariates assuming a proportional hazard model. The exact permutation distribution can be calculated as well as the exact low order moments for arbitrary censoring patterns. The asymptotic distribution of the score statistic is an easy consequence. The method is naturally extended to deal with the multivariate case, time varying covariates and interval censoring. Finally the relationship between the censoring process, the survival times and covariates are studied considering different reference sets for the distribution of the score statistic. Some assumptions about the censoring process are investigated and as a consequence the effect of censoring is clarified.

Biometry↗
PubMed1997

Population-based family study designs: an interdisciplinary research framework for genetic epidemiology.

Most complex traits such as cancer and coronary heart diseases are attributed either to heritable factors or to environmental factors or to both. Dissecting the genetic and environmental etiology of complex traits thus requires an interdisciplinary research strategy. Genetic studies generally involve families and investigate familial aggregations of traits, segregation of major disease genes, and locations of disease genes on the human genome, the latter of which can be identified via linkage analysis. Epidemiologic studies often use population-based case-control studies to establish the role of specific environmental factors. Integrating both objectives, genetic epidemiology is to assess the associations of environmental factors with disease status, to quantify the aggregation of cases within families, to characterize putative disease genes via segregation analysis, and to localize disease genes via linkage analysis with genetic markers. To accomplish these objectives through designed studies, we propose a class of population-based family study designs, which are formed by choosing among sampling designs at three stages. The objectives of sampling at these three stages are 1) combined aggregation and association analysis, 2) combined segregation, aggregation, and association analysis, and 3) combined linkage, segregation, aggregation, and association analysis. These designs form an interdisciplinary research framework for genetic epidemiology. Our preliminary exploration of this framework and related analytic methods indicates that population-based family study designs retain the efficiency of linkage analysis for localizing disease genes without losing the property of being population-based, and they will therefore allow an assessment of a joint contribution of genetic and environmental factors to complex traits.

Case-Control Studies↗
PubMed1997

A population-based family study (II): Segregation analysis.

We used segregation analysis to investigate the genetic etiology of the disease in Problem 2A. Under the assumption of a dominant major gene, our analysis suggests a major gene with relative risk of 58 and an allele frequency of 0.013. Under an additive gene assumption, it appears that there may be two genes with relative risks of 39 and 17 and allele frequencies of 0.015 and 0.075, respectively.

Case-Control Studies↗