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To investigate the effect of tumor site, measurement precision, tumor surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position.

This revised formulation has a parallel in Kaplan-Meier estimation that requires only that censoring not depend on future outcomes [ 6] and in discrete-time censoring models that are formulated as missing-data selection models [ 7].

This study has also demonstrated the informative capability of the gamma test in the input data selection for model development.

The same data collection, data selection, and model analysis using negative binomial distribution were applied independently in the 4 data sets to identify 'reproducible' factors, i.e. explanatory factors that were significant in different time periods and spaces.

For these models, we used a similar methodology to the 2010 DGRF; however, there are some differences in the data selection and model parameterization, and the underlying data sets rely on different satellites, and so we provide a more complete discussion of these two models.

We first see that the temporally un-constrained solution is very close to the constrained one and therefore, the combination of our data selection and model parameterization, independently of the chosen constraints, does not allow a temporal variability of the Gauss coefficients as large as CHAOS-2s variability.

It can also be a consequence of them using very similar data selection or modeling techniques.

One limitation of this approach is that a good statistical agreement between models does not necessarily mean these models are the most realistic; it can also be a consequence of the use of very similar data selection or modelling techniques.