ICH E9 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials" contained discussions about intercurrent events and strategies for handling intercurrent events. Intercurrent events were defined as:
Events occurring after treatment initiation that affect either the interpretation or the existence
of the measurements associated with the clinical question of interest. It is necessary to address
intercurrent events when describing the clinical question of interest in order to precisely define
the treatment effect that is to be estimated.
The terminal events are one kind of intercurrent event. ICH E9 Addendum did not provide the formal definition for 'terminal events', but gave examples of the terminal events:
Examples of intercurrent
events that would affect the existence of the measurements include terminal events such as
death and leg amputation (when assessing symptoms of diabetic foot ulcers), when these events
are not part of the variable itself.
In a paper by Siegel et al "The role of occlusion: potential extension of the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis for Time-to-Event oncology studies", the terminal events were described as the following:
The estimands guidance also introduces the concept of a terminal event. Terminal events prevent the possibility of subsequent measurement. "For terminal events such as death, the variable cannot be measured after the intercurrent event, but neither should these data generally be regarded as missing." There are two examples given in the guidance, death and leg amputation. These examples clarify that terminal events physically prevent subsequent measurement, for any estimand in any study.
Terminality is an objective property of an event which renders further observation physically impossible. If an event is terminal, it is impossible to devise a study that can look beyond it. Indeed there is no meaningful clinical question regarding the treatment effect that manifests after a terminal event.
Terminal events can be defined as events that make the outcome measures impossible and the events are not part of the outcome such as death and ankle amputation in a trial assessing ankle function). Sometimes, the outcome measure after the terminal events may still be possible, but the measures after the terminal events are not meaningful. For example, in clinical trials of pulmonary diseases with spirometry measure as the primary outcome, lung transplantation will be a terminal event. After the lung transplantation, the spirometry measure can still be performed, but the spirometry measure is a reflection of the transplanted lungs, not the intended measure of the clinical trial endpoint.
Terminal events should be separated as fatal (death, mortality) and non-fatal terminal events (may be called 'terminal events excluding mortality'). While they are all considered intercurrent events, the strategies for handling the fatal and non-fatal terminal events need to be different.
Strategies for Handling the Fatal Terminal Events
Treatment policy strategy can not be used for handling fatal terminal events (death events). ICH E9 Addendum mentioned the following:
In general, the treatment policy strategy cannot be implemented for intercurrent events that are terminal events, since values for the variable after the intercurrent event do not exist. For example, an estimand based on this strategy cannot be constructed with respect to a variable that cannot be measured due to death.
Composite strategies (or composite variable strategies) are particularly useful for handling fatal terminal
events (deaths). The occurrence of the fatal terminal intercurrent event is informative about the
effect of the treatment and so it is incorporated in the endpoint. In practice, the outcomes after the fatal terminal intercurrent event can not be observed, but need to be assumed to have the worst values.
With the composite
strategy, the terminal intercurrent events will be assigned a failed value. A failed value may be:
- Worse possible measure (for example, 0 for 6MWD and 0 for FEV1 or FVC measures)
- Worst observed value across all subjects at the endpoint visit
- Trimmed means (trimmed
means and quantiles were mentioned in ICH
E9 addendum training materials)
- The worst change (from
baseline) of all subjects plus a random error. The error can be randomly
drawn from a normal distribution with a mean of 0 and a variance equal to
the residual variance estimated from the mixed model for all observed
values of change from baseline
In FDA's guidance, "
Amyotrophic Lateral Sclerosis: Developing Drugs for Treatment Guidance for Industry", deaths were integrated into the functional measure by the ALS Functional Rating Scale-Revised (ALSFRS-R). The guidance said:
Functional endpoints can be confounded by loss of data because of patient deaths. To address
this, FDA recommends sponsors use an analysis method that combines survival and function into
a single overall measure, such as the joint rank test.
In pivotal clinical trials in ALS, the joint rank test is almost the default method for analyzing the primary efficacy endpoint of the ALSFRS-R. The Joint Rank statistic ranks study participants in each treatment group, first by survival and then by ALSFRS-R score. The Joint Rank can increase power relative to analysis of either ALSFRS-R or survival analysis alone in some circumstances, for example when mortality rates are high
.
Joint Rank test was described and used in the NEJM paper by Miller et al "
Trial of Antisense Oligonucleotide Tofersen for SOD1 ALS".
Strategies for Handling the Non-Fatal Terminal Events
It is acceptable to use hypothetical strategy to handle the non-fatal terminal intercurrent events. "Hypothetical strategies: A scenario is envisaged in which the intercurrent event would not occur: the value of the
variable to reflect the clinical question of interest is the value which the variable would have
taken in the hypothetical scenario defined."
The value of the variable to reflect the clinical question of interest is the
value which the variable would have taken in the hypothetical scenario
defined. The value to be considered would have been the one
collected if patients had not had the non-fatal terminal event. Outcomes after the non-fatal terminal events do not need to be measured. If the outcomes after the non-fatal terminal events are measured (for example, the spirometry measure after lung transplantation), the measures can be disregarded and not used in the analyses. The outcomes after the non-fatal terminal events cannot be observed, can be left as missing values, and usually need to be implicitly
or explicitly predicted/imputed.
