SUMMARY A parametric mixture model provides a regression framework for analysing failure‐time data that are subject to censoring and multiple modes of failure. The regression context allows us to adjust for concomitant variables and to assess their effects on the joint distribution of time and type of failure. The mixing parameters correspond to the marginal probabilities of the various failure types and are modelled as logistic functions of the covariates. The hazard rate for each conditional distribution of time to failure, given type of failure, is modelled as the product of a piece‐wise exponential function of time and a log‐linear function of the covariates. An EM algorithm facilitates the maximum likelihood analysis and illuminates the contributions of the censored observations. The methods are illustrated with data from a heart transplant study and are compared with a cause‐specific hazard analysis. The proposed mixture model can also be used to analyse multivariate failure‐time data.

In this paper we derive and investigate nonparametric estimators of the distributions of lifetime and time until onset associated with an irreversible disease that is detectable only at death. The nonparametric maximum likelihood solution requires an iterative algorithm. An alternative though closely related pair of estimators for the lifetime and onset distributions exists in closed form. These estimators are the familiar Kaplan-Meier estimator and an isotonic regression estimator, respectively. First-order approximations provide variance estimators. The proposed methods generalize and shed additional light on the constrained estimators presented by Kodell, Shaw and Johnson (1982, Biometrics 38, 43-58). Data from an animal experiment illustrate the techniques.

Objective. Growing evidence suggests increasing frequencies of autoimmunity and certain autoimmune diseases, but findings are limited by the lack of systematic data and evolving approaches and definitions. This study was undertaken to investigate whether the prevalence of antinuclear antibodies (ANA), the most common biomarker of autoimmunity, changed over a recent 25-year span in the US.Methods. Serum ANA were measured by standard indirect immunofluorescence assays on HEp-2 cells in 14,211 participants age ≥12 years from the National Health and Nutrition Examination Survey, with approximately one-third from each of 3 time periods: 1988-1991, 1999-2004, and 2011-2012. We used logistic regression adjusted for sex, age, race/ethnicity, and survey design variables to estimate changes in ANA prevalence across the time periods.Results. The prevalence of ANA was 11.0% (95% confidence interval [95% CI] 9.7-12.6%) in 1988-1991, 11.5% (95% CI 10.3-12.8%) in 1999-2004, and 15.9% (95% CI 14.3-17.6%) in 2011-2012 (P for trend < 0.0001), which corresponds to ~22 million, ~27 million, and ~41 million affected individuals, respectively. Among adolescents age 12-19 years, ANA prevalence increased substantially, with odds ratios (ORs) of 2.02 (95% CI 1.16-3.53) and 2.88 (95% CI 1.64-5.04) in the second and third time periods relative to the first (P for trend < 0.0001). ANA prevalence increased in both sexes (especially in men), older adults (age ≥50 years), and non-Hispanic whites. These increases in ANA prevalence were not explained by concurrent trends in weight (obesity/overweight), smoking exposure, or alcohol consumption.Conclusion. The prevalence of ANA in the US has increased considerably in recent years. Additional studies to determine factors underlying these increases in ANA prevalence could elucidate causes of autoimmunity and enable the development of preventative measures.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. SUMMARYThis paper proposes a logistic regression model for comparing treatment groups with respect to tumour prevalence. The prevalence test commonly used to compare treatments in animal tumorigenicity experiments (Hoel and Walburg, 1972;Peto et al., 1980) is essentially equivalent to a likelihood score test derived under a logistic model that expresses tumour prevalence as a function of time and treatment. The more general regression context suggests an alternative to the convention of grouping observations into arbitrarily chosen intervals. The model also incorporates covariates, provides a framework for estimating the strength of a dose-response relationship and for testing a central assumption underlying the usual prevalence test, and is computationally simple to analyse.

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