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Cox proportional hazards modeling accounting for left truncation was used for univariate and multivariate analyses with estimation of HRs and 95%% CIs.
Moreover, studies that did not report sufficient data for estimation of HRs were ruled out from this systematic review, and this might potentially generate bias.
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HP used with Method 1 gave the smallest estimation of HR ¯ t.
This approach allows a focused estimation of HR reaction from traffic noise during sleep.
With Method 2 where HR ¯ t ≃ HR ¯ a t, L W A u and L W A a gave very similar values, whereas HP gave a smaller estimation of HR ¯ t than L W A u.
With Method 1, the estimation of HR ¯ t was biased with HP and L W A u, but more with HP; the estimation of HR ¯ a t was biased with L W A a. Whatever the model applied, HR ¯ t (HP, L W A u ) and HR ¯ a t (LWA a ) were underestimated, i.e. a poor prognostic exposure effect tended to be ignored and a no exposure effect tended to become a protective one.
In these three profiles, the number of events in the exposed and in the non-exposed individuals is quite low, suggesting a possible lack of accuracy in the estimation of HR ¯ t.
Note that the combination HP and Method 1, taking the exposure as a time-dependent covariate or not, gave exactly the same estimation of HR ¯ t, whereas LWA did not.
For the remaining studies, estimations of HRs and 95% CIs were attempted using the spreadsheet provided by Tierney et al (2007.
In some configurations where the proportion of the profile with the smallest HR ¯ i t was the most highly represented profile leading to a low HR ¯ t, HP was better than L W A u. L W A a was the only model for estimating H R a (t) and led to very slightly biased estimations of HR a t (data not shown).
In addition, the collection of extensive baseline information for the entire SCCS cohort, and the systematic follow up for all-cause and cause-specific mortality, enabled robust, precise estimation of multivariate HRs associated with use of statins in the largest cohort of blacks studied to date.
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