No abstract

The results suggest a reduced incidence of major CHD events at high supplemental vitamin C intakes. The risk reductions at high vitamin E or carotenoid intakes appear small.

With the growing number of epidemiologic publications on the relation between dietary factors and cancer risk, pooled analyses that summarize results from multiple studies are becoming more common. Here, the authors describe the methods being used to summarize data on diet-cancer associations within the ongoing Pooling Project of Prospective Studies of Diet and Cancer, begun in 1991. In the Pooling Project, the primary data from prospective cohort studies meeting prespecified inclusion criteria are analyzed using standardized criteria for modeling of exposure, confounding, and outcome variables. In addition to evaluating main exposure-disease associations, analyses are also conducted to evaluate whether exposure-disease associations are modified by other dietary and nondietary factors or vary among population subgroups or particular cancer subtypes. Study-specific relative risks are calculated using the Cox proportional hazards model and then pooled using a random- or mixed-effects model. The study-specific estimates are weighted by the inverse of their variances in forming summary estimates. Most of the methods used in the Pooling Project may be adapted for examining associations with dietary and nondietary factors in pooled analyses of case-control studies or case-control and cohort studies combined.

A slightly greater risk of lung cancer was associated with the consumption of > or = 30 g alcohol/d than with no alcohol consumption. Alcohol consumption was strongly associated with greater risk in male never smokers. Residual confounding by smoking may explain part of the observed relation.

Certain statistical models specify a conditional mean function, given a random effect and covariates of interest. On the other hand, one may instead model a marginal mean only in terms of the covariates. We discuss some common situations where conditional and marginal means coincide. In a Gaussian linear mixed effects model we have equivalent interpretations of the conditional and marginal regression parameter estimates. Similar results exist for more general link functions. In this paper we give a short overview of some models, where conditional and marginal results are equivalent and we illustrate this with some examples. When the conditional mean is additive in a random effect on the log scale, it is seen that the marginal mean equals the conditional mean plus a constant, such that slope parameters have the same interpretation in both formulations. No further distributional assumptions are needed in either of these cases. With a logit link and a double exponential random effect, a closed form marginal link function is derived from the conditional model. When a logit or probit link is used with a normal random effect, the marginal mean parameters become attenuated by a factor which depends on parameters of the distribution of the covariates. In a conditional Weibull proportional hazards model with a positive stable frailty, the marginal hazards are again Weibull but with slope parameters attenuated towards zero.

No abstract

A slightly greater risk of lung cancer was associated with the consumption of > or = 30 g alcohol/d than with no alcohol consumption. Alcohol consumption was strongly associated with greater risk in male never smokers. Residual confounding by smoking may explain part of the observed relation.

No abstract