The mathematical representation of smoking history is an important tool in analysis of epidemiological and clinical data. Hoffmann and colleagues recently proposed a single aggregate measure of smoking exposure that incorporates intensity, duration, and time since cessation. This comprehensive smoking index (CSI), which may be incorporated in any regression model, depends on a half-life (tau) and a lag (delta) parameters that have to be fixed a priori, or estimated by maximizing the fit. The CSI has not previously been used for analysis of cancer data. Following some preliminary results on smoking and lung cancer, the authors proposed a new version of the CSI for lung cancer. The aim of this study was to investigate the performance of the original and the new versions of the CSI in the analysis of three data sets from two case-control studies of lung cancer undertaken in Montreal, in 1979-1985 in males, and in 1996-2000 in both males and females. The estimates of tau and delta for both versions of the CSI were similar across data sets. The new version of the CSI fitted the three data sets systematically although moderately better than the original version, and at least as well as other representations of lifetime smoking history that used separate variables for time since cessation and cumulative amount of cigarettes smoked. The results suggest that the CSI may be an attractive and parsimonious alternative to conventional modelling of different aspects of smoking history for lung cancer.

Our results suggest that, already in adolescence, accumulation of IAF may promote development of the MS, affecting the metabolic and inflammatory components similarly in both sexes but influencing BP adversely only in males. The latter may be attributed, in part, to the augmentation of sympathetic activity also seen only in males.

Marginal structural models (MSM) provide a powerful tool to control for confounding by a time-dependent covariate without inappropriately adjusting for its role as a variable affected by treatment . In this paper, we demonstrate that it is possible to fit a marginal structural Cox model directly, rather than the typical approach of using pooled logistic regression, using the weighted Cox proportional hazards function that has been implemented in standard software. To evaluate the performance of the marginal structural Cox model directly via inverse probability of treatment weighting, we conducted several simulation studies based on two datagenerating models: one which replicates the simulations of Young et al. (2009) and an additional, more clinically plausible approach which mimics survival data with time-dependent confounders and time-varying treatment. Using the simulations, we illustrate the limitations of the conventional time-dependent Cox model and the MSM fitted via pooled logistic regression. Furthermore, we propose two novel normalized weights with the goal of reducing the MSM estimators' variability. The performance of the normalized weights is evaluated alongside the usual unstabilized and stabilized weights.

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Objective. Care in rheumatoid arthritis (RA) is optimized by involvement of rheumatologists. We wished to determine whether patients suspected of having new-onset RA in Québec consulted with a rheumatologist, to document any delay in these consultations, and to determine factors associated with prompt consultation. Methods. Physician reimbursement administrative data were obtained for all adults in Québec. Suspected new-onset cases of RA in the year 2000 were defined operationally as a physician visit for RA (based on the International Classification of Diseases, Ninth Revision diagnostic codes), where there had been no prior visit code to any physician for RA in the preceding 3 years. For those patients who were first diagnosed by a nonrheumatologist, Cox regression modeling was used to identify patient and physician characteristics associated with time to consultation with a rheumatologist. Results. Of the 10,001 persons coded as incident RA by a nonrheumatologist, only 27.3% consulted a rheumatologist within the next 2.5-3.5 years. Of those who consulted, the median time from initial visit to a physician for RA to consultation with a rheumatologist was 79 days. The strongest predictors of shorter time to consultation were female sex, younger age, being in a higher socioeconomic class, and having greater comorbidity. Conclusion. Our data suggest that the vast majority of patients suspected of having new-onset RA do not receive rheumatology care. Further action should focus on this issue so that outcomes in RA may be optimized.