Bug: AUC start 0 (which also needs Feature: C0 imputation)

[Gero1999]

Issue

Closes #2 #60

Description

After running NCA, AUC calculations always consider the discrete start (start at where first concentration sample was collected). This can indeed alter slightly the real values of the AUC. This is a consequence of 2 problems:

1. Doses times are being considered at the first measure, instead of at the real dose time
2. No imputed measure at dose time is consdiered

Change description.

Definition of Done

C0 imputation option for the user specified in Setup > Data Selection

How to test

• At "NCA">"NCA Results", when C0 is imputed it is expected that start=AFRLT-ARRLT (being AFRLT-ARRLT= 0 in DOSNO=1)
• At "NCA">"NCA Results", when C0 is not imputed it is expected that start={time of 1st observation}
• AUC values (auclast, aucinf...) from start=AFRLT-ARRLT are calculated when C0 is imputed
• All variables that should only be based on observed data (cmax, tmax...) are not considering C0 imputations
• Test that PKNCA also deals well with predoses as duplicates for the NCA
• Check slope selector plots and NCA results remain well after running NCA
• Check if in general C0 imputation logic works well (NCA experts):
  if C0 exists ~ C0=C0
  if DOSNO=1 & IV bolus (ADOSEDUR=0, ROUTE=intravascular) ~ C0=0
  if DOSNO>1 & not IV bolus ~ C0=predose
  if IV bolus and monoexponential data ~ logslope
  if IV bolus and not monoexponential data ~ C0=C1

Contributor checklist

• Code passes lintr checks
• Code passes all unit tests
• New logic covered by unit tests
• New logic is documented

Notes to reviewer

Anything that the reviewer should know before tacking the pull request?

[js3110]

Nice work! Some requests:

Fixes:

• App crashes if AUC rule sets are selected
• Slope selector not working- i tested an exclusion and the app crashed. Haven't tried selections.
• Data table in Data selection tab is not sorted- needs to be sorted by USUBJID for better visualisation.

Enhancements:

• It would be good if the user can see an updated data table after the imputations, so they know what the data being used looks like. If this is too complex then maybe you can add an explanation of what the imputation is doing. But it would be good if they can see what data is being created.
• Some clarification/relabelling is required in terms of what the imputation does. 'C0 missing' is not always accurate, for example if the predose value is present in the data set but ATime is <0, it gets excluded, but its not technically missing. Some more clarification on this would be good for the user so they know that imputation is also for predose samples.
• Predose samples/ duplicates are not being included. Need to figure out a way to ensure they are- by creating duplicates manually or making sure PKNCA includes them.