Hi Frank, thanks for your reply.

I have not located much in the way of literature that addresses this particular situation, that is, dealing with time based variations in the collection of follow up data, at time points that are outside of, and may materially deviate from the more narrow, discrete, protocol defined windows. It is possible that I am just not using the correct keywords, but I did try various terms and combinations.

There are some general papers that I located, a number outside of clinical trials, even outside of life sciences (e.g. engineering, economics, etc.), that discuss general issues around using time as a discrete versus continuous variable in modeling. Frequently, those are dealing with time series based analyses, versus the finite number of time points as typically seen in clinical trials.

So, this is a topic that would seem to be in need of further comparative work to elucidate the pros and cons of varying analytic methods and the impact (e.g. bias) on conclusions from relevant studies.

Fundamentally, I agree with your view that time is, in general, best dealt with as a continuous variable to account for irregularly spaced intervals across patients in this setting. Where I wrestle with the notion, is that there can be circumstances that one can envision where it may not be always apropos.

In favor of using time as a continuous variable as you note, is that you can deal with the variations in the actual data collection time points, both within and between patients, given the realities of those variations in the conduct of a trial as raised in this thread. One can then use a model based approach (e.g. mixed effects, gls, gee, etc.) to estimate the values of the outcome of interest at specific time points that are relevant to the trial design and the questions being posed.

Another potential benefit of using time as a continuous variable is that you only have one degree of freedom, if time is not transformed, versus having a larger number of degrees of freedom for time as a discrete variable. So, you may get more power for the model in that setting, if presuming a linear relationship is reasonable. However, if you perform a non-linear transform on time, such as using a spline, then you can possibly lose that advantage, depending upon the number of time points involved in the study timeline.

The potential downside to using time as a continuous variable is that, if there are only a few post baseline time points, you may not be able to reasonably transform time using a spline or some other non-linear method, perhaps risking overfitting the model. Thus you effectively revert to presuming a linear relationship between time as a continuous variable and the outcome measures of interest, which may not be reasonable. So, in that setting, using time as a discrete variable would allow for more flexibility, perhaps at the expense of forcing the presumption of consistency in the data collection time points. So there may be tradeoffs in that setting, and one may want to engage in some sensitivity analyses to assess potential bias in the estimates that result.

I look forward to your thoughts on the above. Thanks!