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In prognostic research, there is strong evidence to suggest that time-interval data should be analysed in a statistical model using time interval as a continuous variable, rather than a dichotomised model, in order to minimise bias arising from what has been termed 'the waiting time paradox' (Crawford et al, 2002; Torring et al, 2011).
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When exploring the time interval to BC2 as a continuous variable, we found a significantly improved DDFS per year the longer the time interval to BC2 (HR = 0.97, p = 0.002, 95% CI 0.94-0.99).
This variable was analysed as a dichotomous variable (death within various discrete time intervals [yes/no]) and as a continuous variable (in days).
To asses a time trend, time was admitted as a continuous variable.
We additionally tested for linear trend across age groups with 15 years intervals, with time to administration of the initial antimicrobial agents assessed as a continuous variable measured in hours (with exclusion of two extreme outlying values > 100 hours) and with time assessed as a continuous variable after logarithmic transformation.
The change in incidence of dextrose administration over time (treated as a continuous variable for this analysis) was assessed by using unadjusted logistic regression, and the result is presented as an odds ratio (OR) with 95% confidence interval (CI).
In contrast, edgeR treats time not as a continuous variable but as multifactor.
Regarding use of coronary angiography, reperfusion therapy and inhospital mortality, we also added Killip class on arrival and symptom-to-door time (as a continuous variable in 1-h intervals) to the multivariable-adjusted analyses.
The HLM was used in the primary analysis, allowing us to treat time as a continuous variable, and handle unbalanced data and unequal spacing conditions caused by variation in testing intervals, in a flexible way (Raudenbush and Bryk, 2002).
Time was measured as a continuous variable.
Time was treated as a continuous variable.
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