Monday, March 15, 2021

Drug-related AEs, AE Causality, AE relationship, and SUSAR

In pre-marketing clinical trials or post-marketing drug uses, adverse events (AEs) can always occur. When AEs are reported, assessments need to be made to judge if the reported AEs are caused by the study drug or study treatment. There are different terms to describe this assessment: drug-related, causality, relationship to the study drug, attributable to the study drug,...

In clinical trials, the causality assessment is made by the investigators. In post-marketing experience (surveillance or spontaneous reporting), all reported AEs are considered as related to the drug - so-called 'adverse drug reaction'. 

In an early post, I discussed "Causality Assessment, Causality Categories for Reporting Adverse Events or Adverse Reactions" and summarized various ways to categorize the AE causality: 

ICH E2B
Not Related, Unlikely Related, Possibly Related, Related)
CDISC
Not Related, Unlikely Related, Possibly Related, Related).
WHO
Certain, Probable/Likely, Possible, Unlikely, Conditional/Unclassified
Other options
None, Unlikely, Possible, Probable, Not assessable

In a recently published CDISC CDASH eCRF form for AEs, the causality is simply assessed as Yes, No.  


For statistical analyses of the AE data, drug-related AEs will usually be summarized and analyzed. Drug-related AEs are usually defined as the AEs with causality assessment at least probably related to the investigational study drug (including the placebo). For example, using ICH2B or CDISC criteria, AEs with causality assessment of 'possibly related', 'related', will be considered as 'drug-related. If CDISC CDASH eCRF is followed, the drug-related AEs will be those with 'Yes' answer to the 'Relationship to Study Treatment' question. Some of my European colleagues once argued that the 'unlikely' category should also be considered as 'drug-related - but I never found any regulatory guidance to support this.  

It is difficult to compare drug-related AEs across different studies that are conducted by different sponsors because the number of causality categories may be different in different studies. The number of categories can be anywhere between 2 (yes/no) to 5 categories. 

In some studies, additional causality assessments may be performed to judge if the AEs are related to the background therapies (especially in studies with add-on design) or related to the medical device (such as inhalation device and infusion pumps). The same AE could be assessed to be related to the study treatment, background therapy, and/or the medical device. 

For the past year, we have seen plenty of headlines about the AE causality assessment in actions in Covid-19 vaccine studies and in gene therapy studies. AE causality can be assessed on an individual case (the unexpected event in AZ's and J&J's Covid-19 vaccine trials) or on an aggregate level (in the case of blood clots by AZ's Covid-19 vaccine). 

Causality assessment based on the individual case:

"After a thorough evaluation of a serious medical event experienced by one study participant, no clear cause has been identified. There are many possible factors that could have caused the event. Based on the information gathered to date and the input of independent experts, the Company has found no evidence that the vaccine candidate caused the event."

Causality assessment on an aggregate level: 


"A careful review of all available safety data of more than 17 million people vaccinated in the European Union (EU) and UK with COVID-19 Vaccine AstraZeneca has shown no evidence of an increased risk of pulmonary embolism, deep vein thrombosis (DVT) or thrombocytopenia, in any defined age group, gender, batch or in any particular country.

So far across the EU and UK, there have been 15 events of DVT and 22 events of pulmonary embolism reported among those given the vaccine, based on the number of cases the Company has received as of 8 March. This is much lower than would be expected to occur naturally in a general population of this size and is similar across other licensed COVID-19 vaccines. The monthly safety report will be made public on the European Medicines Agency website in the following week, in line with exceptional transparency measures for COVID-19.
One type of AEs requires special attention and needs to be reported to the regulatory agencies and local IRB/ECs expeditiously. It is called Suspected Unexpected Serious Adverse Reaction (SUSAR). According to FDA guidance Safety Reporting Requirements for INDs and BA/BE Studies, SUSARs are those AEs meeting the following criteria: 

  • Serious (S)
  • Unexpected (U)
  • Suspected Adverse Reactions (SAR)

Fatal or life-threatening SUSAR should be reported to FDA no later than 7 days; Others SUSAR should be reported to FDA no later than 15 days.

We saw the news that Bluebird Bio temporarily suspended their gene therapy clinical trials due to a reported SUSAR of acute myeloid leukemia (AML).


Bluebird bio did their assessment and ruled that Gene Therapy for Sickle Cell Not Linked to Cancer (AML event)

"The company released a statement yesterday (March 10) claiming an investigation has found “it is very unlikely” the AML is related to the therapy and the firm is seeking approval from the US Food and Drug Administration (FDA) to resume the trials.

“VAMP4 has no known association with the development of AML nor with processes such as cellular proliferation or genome stability,” Bluebird’s Chief Scientific Officer Philip Gregory says in the press release. Furthermore, the patient’s cells had mutations in other genes, which are related to leukemia."
Some additional comments on AE causality assessment:
  • AEs that occurred prior to the first dose of study treatment (i.e., non-treatment-emergent AEs) should always have the causality 'unrelated' to the study treatment - an edit check should be in place to prevent the investigators to enter a non-TEAE as drug-related. 
  • In blinded studies, if a SUSAR event is reported, the individual patient's treatment assignment should be unblinded so that the sponsor can assess the causality and report the SUSAR event appropriately. 
  • While drug-related AEs are typically summarized, analyzed, and included in the clinical study report, FDA reviewers will focus their review on all AEs and all SAEs regardless of the causality. Drug-related AEs are usually not included in the drug-label. Instead, the drug label will list the most frequent AEs (whether or not they are drug-related).  
  • Sometimes, causality assessment by the investigator may be subjective and arbitrary to some degree. Important events (such as SUSAR and AESI (AE of special interest)) may be further reviewed by the sponsor, data monitoring committee, clinical event adjudication committee, and regulatory agencies. For example, some oncology drugs may induce pneumonitis/interstitial lung disease and these events of pneumonitis/interstitial lung disease can be reviewed and adjudicated by a committee. 
  • Statistical summary and analysis of AE causality are always based on the assessment by the investigator that is recorded in the clinical database. Causality assessment by the sponsor and other parties is not part of the clinical database and will be analyzed separately.
Additional References: 

Wednesday, March 10, 2021

Intention-to-Treat Principle versus Treatment Policy Estimand: Different Names, but Same Meaning?

ICH E9 "Statistical Principles for Clinical Trials" was finalized in February 1998. The E9 guidelines established the Intention-to-Treat principle for the design and analysis of clinical trials. With the intention-to-treatment principle, we are required to include all study participants (full analysis set) in the analyses. Here are the definitions for 'full analysis set' and 'intention-to-treat principle' from ICH E9. 



In 90's, it took a while for the people to understand and accept the concept of the intention-to-treat principle. We also see that the intention-to-treat principle was misused, over-used, or undercut by the use of practical intention-to-treat and modified intention to treat. I had a presentation (in 2004) about the misuse/overuse of intention-to-treat and modified intention-to-treat. What I said then is still applicable today. 

The strict definition of intention-to-treat can be traced back to the book chapter by Fisher, LD et al. Intention to treat in clinical trials in Statistical Issues in Drug Research and Development. Edited by Peace KE (1990). The intention-to-treat was defined as:

Includes all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol

The intention-to-treat principle includes all randomized subjects in the analyses and ignores what happens to the subjects after the randomization (whether or not the subject discontinued the study drug, took prohibited or rescue therapies, crossed over the alternate treatment,...), which is obviously not the best option in estimating the treatment effect in some situations.  This leads to the development of Addendum to ICH E9 "ICH E9 (R1) Estimands and Sensitivity Analysis in Clinical Trials". ICH E9 (R1) explained the issues with the intention-to-treat principle and introduced the new concept of estimands (including treatment policy estimand) and intercurrent events. 

This addendum clarifies and extends ICH E9 in respect of the following topics. Firstly, ICH E9 introduced the Intention-To-Treat (ITT) principle in connection with the effect of a treatment policy in a randomised controlled trial, whereby subjects are followed, assessed and analysed irrespective of their compliance to the planned course of treatment, indicating that preservation of randomisation provides a secure foundation for statistical tests. Multiple consequences arising from the ITT principle can be distinguished. Firstly, that the trial analysis should include all subjects relevant for the research question. Secondly, that subjects should be included in the analysis as randomised. Taken directly from the definition of the ITT principle (see ICH E9 Glossary), a third consequence is that subjects should be followed-up and assessed regardless of adherence to the planned course of treatment and that those assessments should be used in the analysis. It remains undisputed that randomisation is a cornerstone of controlled clinical trials and that analysis should aim at exploiting the advantages of randomisation to the greatest extent possible. However, the question remains whether estimating an effect in accordance with the ITT principle always represents the treatment effect of greatest relevance to regulatory and clinical decision making. The framework outlined in this addendum gives a basis for describing different treatment effects and some points to consider for the design and analysis of trials to give estimates of these treatment effects that are reliable for decision making. Secondly, issues considered generally under data handling and “missing data” (see Glossary) are re-visited. Two important distinctions are made. 

With the intention-to-treat principle, subjects who discontinued the study drug prematurely should continue to be followed up and the data after dose discontinuation should continue to be collected. However, in practice for many studies, the data collection was stopped for subjects who discontinued the study drug, or the data collected after subjects' discontinuation of study drug were collected, but not used in the analyses. To some extent, the intention-to-treat principle was not fully followed. That is why the FDA has issued its guidance "Data Retention When Subjects Withdraw from FDA-RegulatedClinical Trials" to encourage the data collection after the subjects withdraw from the study. As discussed in the guidance:

The validity of a clinical study would also be compromised by the exclusion of data collected during the study. There is long-standing concern with the removal of data, particularly when removal is non-random, a situation called “informative censoring.” FDA has long advised “intent-to-treat” analyses (analyzing data related to all subjects the investigator intended to treat), and a variety of approaches for interpretation and imputation of missing data have been developed to maintain study validity. Complete removal of data, possibly in a non-random or informative way, raises great concerns about the validity of the study. 

The addendum to ICH E9 introduced the concept of estimands and intercurrent events. Those events that occurred after the randomization were previously ignored even though the analyses were under the intention-to-treat principle. With the addendum, Those events that occurred after the randomization would be called 'intercurrent events'. Here is the official definition of the intercurrent events:

Intercurrent Events:
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.

Estimands can be classified based on the strategies of handling the intercurrent events. One way to handle the intercurrent events is the 'treatment policy' strategy - therefore, we have a treatment policy estimand. The treatment policy estimand under the addendum is almost identical to the intention-to-treatment principle under the original ICH E9. 

Treatment policy strategy
The occurrence of the intercurrent event is considered irrelevant in defining the treatment effect of interest: the value for the variable of interest is used regardless of whether or not the intercurrent event occurs. For example, when specifying how to address use of additional medication as an intercurrent event, the values of the variable of interest are used whether or not the patient takes additional medication.
If applied in relation to whether or not a patient continues treatment, and whether or not a patient experiences changes in other treatments (e.g. background or concomitant treatments), the intercurrent event is considered to be part of the treatments being compared. In that case, this reflects the comparison described in the ICH E9 Glossary (under ITT Principle) as the effect of a treatment policy.

The intention-to-treat and treatment policy estimand are two different names with the same meaning. If we have to differentiate them, we can say that the intention-to-treatment principle is more focused on which subjects should be included in the analyses while the treatment policy estimand is more focused on which data points should be included in the analyses

We have started to see that the ICH E9 addendum and the concept of estimands are gradually adopted, especially in EU countries. The adoption of the ICH E9 in the US is much slower than in EU countries. The concept of estimands and intercurrent events is still considered as the words invented by statisticians. It will take a while for non-statisticians to understand the concept and to accept these new terms. A presentation "Regulator’s experience with estimands" by Andreas Brandt from EMA summarized the challenges for the adoption and implementation of the ICH E9 Addendum. We will anticipate the difficulties ahead for non-statisticians and clinicians to accept the concept of estimand and intercurrent events. This is reflected in a paper by Min & Bain "Estimands in diabetes clinical trials"

During 2019 several type 2 diabetes trials results using the term estimand were published. This word will be unfamiliar to many clinicians (and to spellcheck) but given that regulatory bodies have endorsed its use, this word is likely to become a staple of medical jargon in the future.

ICH E9 Addendum described five different strategies for handling the intercurrent events: treatment policy strategy, hypothetical strategy, composite variable strategy, while on treatment strategy, and principle stratum strategy. However, in practice, the treatment policy estimand is used the vast majority of the studies where the estimand concept is mentioned. There are a few studies using the principle stratum strategy. The other three strategies (hypothetical strategy, composite variable strategy, while on treatment strategy) are rarely used in practice perhaps because they are relatively new, are uncertain with the regulatory acceptance, and because there is no available method to estimate the treatment difference for some estimands.  

If the vast majority of the estimand application is treatment policy strategy which is almost identical to the traditional intention-to-treat principle, we will question if it is worth revamping the entire ICH E9 to come up with an addendum for estimand and intercurrent event concept.