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There is a log-linear relationship between the independent variables and the underlying hazard function.
The model is called proportional hazard because, while no assumptions are made about the shape of the underlying hazard function, the model equations specify a multiplicative relationship between the underlying hazard function and the log-linear function of the covariates.
Afterward it was confirmed that a smoothed hazard function provided a realistic estimation of the underlying hazard function.
We addressed the discrepancy in magnitude of the underlying hazard between the UK and Japanese patients through model recalibration.
In much smaller studies where there are fewer events there may not always be sufficient information to adequately estimate the underlying hazard using this model.
It will be helpful to explore the form of the underlying hazard function and also assess the availability of data in each interval.
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Advances in computer simulation and modeling have made it possible to include information about the underlying hazards, as well as about the exposure and vulnerability of populations so that warning information can truly inform response.
Use of flexible parametric models to more easily capture the shape of the underlying hazards function and extrapolate future survival estimates.
In most studies, different competing risk types will have substantially different underlying hazard functions and thus the applicability of the unstratified L-M model is restricted.
The unique design of the experiments makes it possible to compare the effects on survival produced by different treatments in terms of changes in population heterogeneity and underlying hazard.
Thus, although PMI choices and outcomes might be observed in discrete intervals, PMI research often makes use of continuous time models that do not rely on specific assumptions about underlying hazard functions.
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