Visit Window for Longitudinal Studies

In the previous post, the time window was discussed. The time window is an issue in studies with relatively shorter durations (such as the clinical pharmacology studies and clinical trials in analgesic drugs). The measures outside the time window can still be used in analysis by using the actual time in calculations. The impact is relatively neglectable.   

In clinical trials with a longitudinal design where the study subjects are followed up at pre-specified schedules for a long period of time, the visit window is an issue to be handled. The visit schedules may not be followed due to the reasons of 1) patient side (for example, travel arrangement); 2) investigator side (for example, investigator may have other responsibilities and not able to see the patient on a specific day); 3) the study procedure cannot be performed on the specified visit day (for example, the CT, MRI, … may not be arranged at the exact visit date).

It is common that the visit window is allowed and specified in the protocol, in this way, not every out of window visit will be recorded as the protocol deviation. The visit window needs to be protocol specific and needs to specified according to the length of the study and interval of the study visits. A study with every four-week visit schedule may have a visit window +/- 1 week; a study with every six-month visit schedule may have a visit window +/- a month.

It is inevitable to have always some subjects with visit outside the visit window. The study will also allow having unscheduled visits and early termination visit (the early termination visit can occur at any time during the study). These visits and measures outside the visit window will need to be used in the analysis so that the missing data can be minimized, and the more representative measures are selected to be used in the analysis. This needs to be handled through programming (usually in analysis dataset programming or CDISC ADaM dataset programming). Generally, two steps are needed:

Specify the Visit Slotting Algorithm

Based on the actual visit date, allocate the actual measure to the specific visit. An example below is for a six-month (24 weeks exactly) study. The visit slotting algorithm can be specified as the following:

Nominal Visit (Scheduled Visit)	Scheduled Visit Day (Study Day)	Slotting Intervals
Baseline	1	-3 to 1 day
Week 1	8	2 to 11 days
Week 2	15	12 to 22 days
Week 4	29	23 to 43 days
Week 8	57	44 to 71 days
Week 12	85	72 to 99 days
Week 16	113	100 to 127 days
Week 20	141	128 to 155 days
Week 24	169	156 to 14 days after the last treatment dose

Selecting the Value for Analysis 

After all the observations have been slotted based on the algorithm above, it is very possible that there are multiple valid observations for an assessment within an assigned analysis visit. Only one of these observations will be used for summary statistics and analyses. In the ADaM program, this is to determine the ANL01FL (‘Y’ if the observation is selected for analysis)

The observation to be used is determined using the following hierarchy (in decreasing order):

The observation closest to the target study day

The latter observation, if 2 observations are equally close to the target study day

Impact on the Missing Data Imputation

If the missing value needs to be imputed, the imputation should be implemented after the above two steps. For missing values where the last observation carried forward (LOCF) algorithm is applied, it is always the last valid observation on treatment carried forward, even though this might not be the observation obtained by the above hierarchy and used in the summaries by visit window.

Impact on analyses using a mixed model such as MMRM (mixed model repeat measure)

After the slotting and determining the analysis flag (ANL01FL), the mixed model will be based on the data with ANL01FL =’Y’ (i.e., some observations within a visit window may be excluded from the analysis).  Can we use all observations and use the actual visit day in the mixed model analysis? If you try, you may run into the issue that the model does not converge. 

Time Windows for PK Blood Sampling

In the drug development process, especially the early non-clinical or clinical studies, it is critical to understand the DMPK. DMPK, or Drug Metabolism and Pharmacokinetics, is an important part of studies often referred to as ADME (Absorption, Distribution, Metabolism, and Elimination).   

• Absorption (how much and how fast, often referred to as the absorbed fraction or bioavailability)
• Distribution (where the drug is distributed, how fast and how extensive)
• Metabolism (how fast, what mechanism/route, what metabolite is formed, and whether they are active or toxic)
• Elimination (how fast, which route)

In clinical pharmacology studies, the purpose of the study is to characterize the pharmacokinetics profile to assess the bioavailability and bioequivalence. In clinical pharmacology studies, usually, the series of blood samples will be collected for measuring the concentration of the study drug and its metabolites. Based on the measured concentrations from the series blood samples, pharmacokinetics parameters can be calculated. The common pharmacokinetics parameters are AUC (area under the time-concentration curve), Cmax (maximum concentration), Tmax (time to maximum concentration), T1/2 (half life),…

When designing a clinical pharmacology study, it is critical to decide and select adequate time points for blood sampling. The ideal blood sampling scheme will include the time points close to the Tmax (so that we can get a good estimate for Cmax and Tmax) and have good spaced time points to characterize the elimination phase (so that we can get a good estimate for T1/2).

From the practicality standpoint, it is not easy to draw the blood samples at the exact time according to the sampling scheme specified in the study protocol. For the phase I study using healthy volunteers at confined clinical research unit, the time windows can be easily controlled. However, for PK studies in patients across many investigational sites, it was very difficult to keep all blood draws within the narrow windows.

It is very common that time windows are allowed for these blood sampling time points. For example, for sampling time at 1 hour (60 minutes) after the study drug administration, we may add a time window to allow the 1-hour sample to be drawn any time between 55 – 65 minutes after the study drug administration, denotes as 60 +/- 5 minutes. A time window of 5 minutes is allowed for this time point. If the blood drawn is outside the time window (for example outside 55 - 65 minutes), a protocol deviation will be recorded. If the data entry is through an EDC (electronic data capture) system, the edit checks are usually built in to track the deviations for sampling time outside windows.

There are some articles arguing the necessity of the time windows.

• Using Sampling “Windows” for PK Blood Samples
• Should you Include a +/- Time Window Around Collection of PK/PD Samples in a Clinical Trial?

The arguments are 1) when we calculate the pharmacokinetic parameters, the actual sampling times are used. The deviations from the nominal time or pre-specified time point have little impact on the calculation of the PK parameters. 2) if the time windows are set too tight and any outside time windows are recorded as protocol deviation, there will be a lot of unnecessary protocol deviations being recorded. If any outside time window is flagged/alerted during the data entry in EDC, it can be very annoying to the data entry person.

My experience is that we can keep the time windows in the study protocol, but not set the time windows too tight. The sites usually need to have some instructions about the time windows, not purely for the PK parameter calculations, but more for operations. The time windows can be set up as a suggestion and we can use the word ‘should’, not ‘must’ for following the time windows. In this way, not any out of window blood drawn will be automatically recorded as a protocol deviation.