It may be possible to measure hazard characterisation outputs against planned targets for timely delivery, user-friendliness, outcome-focus and temporal/spatial/intensity forecast accuracy.
Cox proportional hazard models were then fit to measure hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of interest.
It was impossible to combine both types of data in the same analysis; therefore for each variable separate sub analyses were performed on the effect measures hazard ratio (HR), median survival time (MST) and median disease free interval (MDFI).
Here, hazard intensity refers to the severity of the hazard, measured with a Hazard Exposure Measurement Unit (HEMU; see next paragraph).
Proportional hazards regression methods were used to examine the associations between dietary exposure levels and colorectal cancer risk, measured by hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) (Cox, 1972).
Risk of mortality, measured as hazard ratios (HR) and 95% confidence intervals (CI), adjusted for potential confounders, was calculated via Cox proportional hazards regression.
First, due to competing events for NI, the association between risk factors of NI as measured using hazard rates may not coincide with the association using cumulative probability (risk).
The association between exposure (sex reassignment) and outcome (mortality, morbidity, crime) was measured by hazard ratios (HR) with 95% CIs, taking follow-up time into account.
The effect of background characteristics on survival was measured using hazard ratios, and Poisson regression models were used for multivariate analyses.
The strength of the association was measured by hazard ratio (HR) and its 95% confidence interval (CI) and P-value.
