Hi,
An initial question:
What is the intent of the study?
Is it for publication (e.g. peer reviewed manuscript, poster, podium presentation), or for a Real World Experience (RWE) regulatory submission, or for another intention?
I ask because, in some cases, depending upon the target journal/meeting in the first case, they may want to see a more detailed SAP (separate from the statistical methods defined in the protocol), including planned interim analyses and adjustments for multiple testing, missing data handling, etc. even for an observational study. There are some that do not require such detail beyond what is in the protocol.
In the second case, you would almost certainly need a separate, detailed SAP for a RWE regulatory submission, including the details of planned interim analyses.
That all being said, generally speaking, there is great debate about the need for a pre-defined, stand-alone, detailed SAP for observational studies that includes the details of planned interim analyses, as compared to the expectations for an RCT and people come down on both sides of the issue.
In the case of a formally powered RCT, one needs to pre-specify key analyses and methods for defined endpoints, and in the case of interim analyses, how you are going to handle adjustments for multiple testing and the pre-specification of interim stopping rules, if any. Your a priori power/sample size calculations need to take the timing and number of interim analyses into account, since the sample size will be inflated at some level to account for the sequential testing method intended, and you need to deal with the adjustments to the relevant alpha decision thresholds along the way, including for the final analysis.
In many cases, the content of the statistical methods section of the observational study protocol can suffice to provide the details of analyses that are consistent with your primary/secondary goals for the study, etc. Beyond those, it is common that there can be language therein that refers to “preliminary, exploratory analyses” that may be performed from “time to time” while the study is underway (e.g. interim analyses), to support publications, etc, along with other relevant details as apropos.
One key is to clearly note that there will be no decisions made to stop or modify the study based upon the interim analyses, albeit, there may be other motivations to do so (e.g. data quality, new external information, safety, etc.).
The key is to understand and describe the relevant limitations of the study as designed, including the interpretation of the interim and inter-group comparative analyses as they may be presented/published. Since this is not an RCT, as you note, the impacts of varying confounding factors (collected and uncollected), including patient selection bias, are important to consider.
For general comments, since you will be conducting longitudinal analyses with serial measurements at varying timepoints, consider using time as a continuous variable as opposed to a categorical one, as Frank has noted on several occasions here, due to the variability of the exact timing of the follow up contacts around your intended contact windows. You can then generate model based estimates of your parameters of interest at clinically relevant, fixed time points, as needed. Also how you are going to deal with the inevitable declining sample size over time, especially if there is a differential between the groups, as you will lose patients to follow up, early discontinuation and so forth, leading to unbalanced data. For example, mixed effects models can be helpful here and there are some threads in the forum that cover some of the considerations for their use.