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Même si les effets écologiques des pluies acides ont été largement signalées, on connaît peu de choses au sujet des effets de la pollution atmosphérique acide sur la santé humaine. Cependant, certaines études épidémiologiques et certaines études impliquant des animaux laissent entendre que l'acidité est un facteur important dans l'explication des troubles respiratoires provoqués par les aérosols. Cet article réexamine quelques aspects de la question et traite de leurs répercussions sur l'élaboration et l'analyse d'études épidémiologiques. Nous mettons en relief deux types de modes d'exposition: les expositions de pointe mais épisodiques à la pollution de l'air et les expositions chroniques à des niveaux constamment élevés de polluants atmosphériques. De nouvelles méthodes d'analyse des études épisodiques sont tirées de travaux récents sur l'analyse des variables répétées de classement en catégories. Les études d'exposition à long terme requièrent des comparaisons entre les groupes et ces comparaisons peuvent être assujetties aux effets du modèle inhérent aux sondages à plusieurs étapes. Nous étudions les conséquences qu'ont ces effets sur les études épidémiologiques. Enfin, cet article traite des erreurs de mesure dues à une prise de mesures à l'extérieur en vue d'évaluer quantitativement une exposition individuelle à la pollution de l'air. Les effets qu'ont de telles erreurs sur les analyses conventionnelles d'études de la pollution atmosphérique peuvent êtrc évalueés grâce à des travaux récents sur les problèmes à variables explicatives sujettes à erreur. [ABSTRACT FROM AUTHOR]

Influence analysis and leverage analysis are important and well-established adjuncts to ordinary least squares (OLS) regression, but analogous regression diagnostics are not generally available for multivariate regression problems. This article describes measures of influence and leverage for a generalized least squares (GLS) estimator of the regression coefficients in a class of multivariate linear models for repeated measurements. When the covariance matrix of the observation vectors is known, the GLS estimator can be expressed as an OLS estimator calculated from transformed variables. In this situation, the multivariate regression diagnostics are directly related to the univariate measures. When the covariance matrix is unknown, however, multistage estimation procedures are required. We describe measures of influence and leverage for a three-step estimator that uses the residuals from an initial OLS regression to estimate the covariance matrix of the response vectors. These diagnostics account for the influence of individual data values on the estimated covariance matrix. The article reports on two different measures of influence and the relation between them: (a) derivative influence, the differential change in an estimated parameter or predicted value resulting from a slight perturbation in the weight assigned to a given observation or vector of observations, and (b) deletion influence, the change in a parameter estimate resulting from the deletion of the vector of observations. Both of these measures can be represented as the sum of components that correspond to the stages of the GLS estimation. Examination of the relative asymptotic sizes of these components and of the differences between derivative and deletion influence leads to modified versions of these... [ABSTRACT FROM AUTHOR]

This article describes a method for comparing responses in two groups of subjects observed repeatedly at a common set of observation times when the response is an ordered categorical outcome. The method, which allows both time-dependent covariates and missing observations, consists of two analytic steps. In the first step, the data are analyzed separately at each occasion using a regression model chosen from the class of models for ordinal data proposed by McCullagh (1980). The joint asymptotic distribution of the estimates of these occasion-specific regression coefficients and a consistent estimator of their asymptotic covariance matrix are obtained without imposing any parametric model of dependence on the repeated observations. In the second step, this asymptotic distribution, together with appropriate simultaneous inference procedures, is used to characterize the overall difference between groups and the variation in group differences over time. The missing-data process may differ between groups to be compared, but it must be independent of response given the covariate values. The new procedures are illustrated by an analysis of annual reports of severity of wheezing in a cohort of preadolescent children participating in a longitudinal study of air pollution and respiratory health. [ABSTRACT FROM AUTHOR]

A response by Stuart J. Pocock and James H. Ware to a letter to the editor about their article on translating statistical findings into English is presented.