Causality Assessment, Causality Categories for Reporting Adverse Events or Adverse Reactions

In clinical trials, safety reporting is a critical issue. This includes the reporting of safety information (adverse events or adverse reactions) from the investigational sites to the sponsor and then from sponsor to the regulatory agencies (FDA, EU) and distribution of the safety information to other investigational sites and IRBs. There have been many regulatory guidelines regarding the reporting of the safety information. However, none of the guidelines give clear instruction on how to categorize the causality for adverse events or adverse reactions.

In recent FDA’s guidance “Safety Reporting Requirements for INDs and BA/BE Studies”, it reiterates the concept of “reasonable possibility” for causality assessment, but does not provide the causality categories.

“Suspected adverse reaction means any adverse event for which there is a reasonable possibility that the drug caused the adverse event. For the purposes of IND safety reporting, ‘reasonable possibility’ means there is evidence to suggest a causal relationship between the drug and the adverse event. A suspected adverse reaction implies a lesser degree of certainty about causality than adverse reaction, which means any adverse event caused by a drug. Suspected adverse reactions are the subset of all adverse events for which there is a reasonable possibility that the drug caused the event. Inherent in this definition, and in the requirement to report suspected adverse reactions, is the need for the sponsor to evaluate the available evidence and make a judgment about the likelihood that the drug actually caused the adverse event. We consider the application of the reasonable possibility causality standard to be consistent with the discussion about causality in the International Conference on Harmonization (ICH) E2A Guideline (“ICH E2A guidance”)."

In FDA’s guidance “Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment”, they cited WHO, the Uppsala Monitoring Center, 2000, Safety Monitoring of Medicinal Product.

 “FDA does not recommend any specific categorization of causality, but the categories probable, possible, or unlikely have been used previously. If a causality assessment is undertaken, FDA suggests that the causal categories be specified and described in sufficient detail to understand the underlying logic in the classification.”

The regulatory guidance leaves the sponsor to decide what to be considered as ‘reasonable possibility’. The sponsor can design the data collection form (SAE form or adverse event case report form) using various categories for causality assessment/reporting.

For data collection purpose, what categories should be collected for causality assessment?

In ICH E2A CLINICAL SAFETY DATA MANAGEMENT: DEFINITIONS AND STANDARDS FOR EXPEDITED REPORTING”, there is a paragraph regarding the causality assessment and causality categories.  

“Many terms and scales are in use to describe the degree of causality (attributability) between a medicinal product and an event, such as certainly, definitely, probably, possibly or likely related or not related. Phrases such as "plausible relationship," "suspected causality," or "causal relationship cannot be ruled out" are also invoked to describe cause and effect. However, there is currently no standard international nomenclature. The expression "reasonable causal relationship" is meant to convey in general that there are facts (evidence) or arguments to suggest a causal relationship. “

In FDA guidance for reviewer “Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review, the following categories are referenced:

Causality assessment by investigator (definitely; probably; possibly; unlikely related)

In ICH E2B “MAINTENANCE OF THE ICH GUIDELINE ON CLINICAL SAFETY DATA MANAGEMENT: DATA ELEMENTS FOR TRANSMISSION OF INDIVIDUAL CASE SAFETY REPORTS “, the following causality categories are referenced in the examples provided in the guidelines. It turns out that these categories seem to be the most commonly used in practice.

The CDISC, CDASH considered causality assessment (relationship to study treatment) as a sponsored defined field. However, it recommends the categories listed in ICH E2B example:

“Sponsored-defined terminology will be used to indicate the relationship between the AE and the study treatment (e.g. ICH E2B examples: Not Related, Unlikely Related, Possibly Related, Related). “

EU regulations regarding the safety report are generally consistent with the ICH guidance (specifically ICH EB2) and do not provide any specific guidance on specific causality categories to use.

EMA Guideline on Good Pharmacovigilance Practice (GPV) recommends the following:

Different methods may be applied for assessing the causal role of a medicinal product on the reported adverse event (e.g. WHO-UMC system for standardised case causality assessment). In this situation, the levels of causality, which correspond to a reasonable possibility of causal relationship, should be established in advance in order to determine when an adverse event is considered as an adverse reaction.

Some sponsors used the causality categories from WHO 2000 Safety Monitoring of Medicinal Product that used the following categories. Definitely Probably Possibly Unlikely Conditional Related Related Related Related. For example, A FDA communication with Cangene Corporation indicated the following AE causality categories are used: Definitely related, Probably related, Possibly related, Unlikely related, and Conditional. This is the same as the above-mentioned WHO-UMC system for standardised case causality assessment.

CAUSALITY CATEGORIES

The causality categories described by the Uppsala Monitoring Centre are as follows. However, the following categories may be modified in the practical use. 

1. Certain: a clinical event, including laboratory test abnormality, occurring in a plausible time relationship to drug administration, and which cannot be explained by concurrent disease or other drugs or chemicals. The response to withdrawal of the drug (dechallenge) should be clinically plausible. The event must be definitive pharmacologically or phenomenologically, using a satisfactory rechallenge procedure if necessary.
2. Probable/Likely: a clinical event, including laboratory test abnormality, with a reasonable time sequence to administration of the drug, unlikely to be attributed to concurrent disease or other drugs or chemicals, and which follows a clinically reasonable response on withdrawal (dechallenge). Rechallenge information is not required to fulfil this definition.
3. Possible: a clinical event, including laboratory test abnormality, with a reasonable time sequence to administrations of the drug, but which could also be explained by concurrent disease or other drugs or chemicals. Information on drug withdrawal may be lacking or unclear.
4. Unlikely: a clinical event, including laboratory test abnormality, with a temporal relationship to drug administration which makes a causal relationship improbable, and in which other drugs, chemicals or underlying disease provide plausible explanations.
5. Conditional/Unclassified: a clinical event, including laboratory test abnormality, reported as an adverse reaction, about which more data is essential for a proper assessment, or the additional data is under examination.
6. Unassessable/Unclassifiable: a report suggesting an adverse reaction which cannot be judged because information is insufficient or contradictory, and which cannot be supplemented or verified.

Some sponsors may use the following categories and definitions for AE causality. For example, an NDA review memo from FDA indicated that the sponsor used the following causality categories: probable, possible, unlikely and not assessable. The instructions for the AE causality assessment are explained below:

Relationship to Investigational ProductThe assessment of the relationship of an adverse event to the administration of study drug (none, unlikely (remote), possible, probable, not assessable) is a clinical decision based on all available information at the time of the completion of the case report form.
None – includes: (1) the existence of a clear alternative explanation (e.g. mechanical bleeding at surgical site); or (2) non-plausibility (e.g., the patient is struck by an automobile at least where there is no indication that the drug caused disorientation that may have led to the event; cancer developing a few days after drug administration).
Unlikely (remote) – a clinical event, including lab abnormality, with an improbable time sequence to drug administration and in which other drugs, chemicals or underlying disease provide plausible explanation.
Possible – a clinical event including lab abnormality, with a reasonable time sequence to administration of the drug, which could also be explained by concurrent disease* or other drugs or chemicals.  Information on drug withdrawal may be lacking or unclear.
Probable – a clinical event including lab abnormality, with a reasonable time sequence to administration of the drug, unlikely to be attributed to concurrent disease* or other drugs or chemicals, and which follows a clinically reasonable response on withdrawal (dechallenge).
Not assessable – a report of an AE which cannot be judged because information is insufficient or contradictory, and which cannot be supplemented or verified.

Table below summarized various causality categories:

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

Due to the fact that there is no clear regulatory guidance on the use of the causality categories, different sponsors could use different causality categories. The consequence is that the so-called drug–related adverse events (or the new term adverse drug reactions) can not be directly compared across the studies by different sponsors. The biases can arise if we attempt to compare the results from different studies conducted by different sponsors where the different causality categories are used. 

Submit the Clinical Trial Datasets to FDA: Using the right .xpt file format

When we submit the clinical trial datasets to FDA, we need to convert the datasets in original format (.sas7bdat) to .xpt format. There are two ways to convert the SAS datasets to .xpt format. However, only the .xpt format generated with Proc Copy can be accepted by FDA.

In FDA’s Study Data Specifications, section 2 (dataset specifications) is very specific about the data format that FDA will accept. It emphasized that "SAS transport file processed by the CPORT SAS PROC cannot be processed or achieved by the FDA"

2.1 File Format

SAS XPORT Transport File format

SAS XPORT transport file format, also called Version 5 SAS transport format, is an open format published by the SAS Institute. The description of this SAS transport file format is in the public domain. Data can be translated to and from this SAS transport format to other commonly used formats without the use of programs from SAS Institute or any specific vendor.
Version

In SAS, SAS XPORT transport files are created by PROC XCOPY in Version 5 of SAS software and by the XPORT SAS PROC in Version 6 and higher of SAS Software. SAS Transport files processed by the CPORT SAS PROC cannot be processed or archived by the FDA.

Sponsors can find the record layout for SAS XPORT transport files through SAS technical support technical document TS-140. This document and additional information about the SAS Transport file layout can be found on the SAS World Wide Web page at http://www.sas.com/fda-esub.

Transformation of Datasets

SAS XPORT transport files can be converted to various other formats using commercially available off the shelf software.

SAS Transport File Extension

All SAS XPORT transport files should use .xpt as the file extension.

Compression of SAS Transport Files

SAS transport files should not be compressed. There should be one dataset per transport file.

On SAS’s FDA Standards for Electronic Submissions, the differences between two different .xpt file formats are explained in Question & Answer format:

Q. There are two SAS transport file formats. Which one is the FDA prepared to use?

A. FDA can accept data in the SAS XPORT Transport Format that is processed by the XPORT engine in Version 6 of SAS software and later, and by PROC XCOPY in Version 5. 

Q. What are the two SAS transport formats?

A. The XPORT Transport Format selected by the FDA, and the CPORT Transport Format. Both XPORT and CPORT are established mechanisms for data exchange that are well tested and well documented. They are not new or at-risk technology. The XPORT Transport Format is supported on all platforms and releases of the SAS System (it is machine and release independent) from Version 5 on. The CPORT Transport Format was invented in Version 6 and is supported from Version 6 on.

Q. Why did FDA choose the XPORT Transport Format over CPORT Transport Format?

A. XPORT is an open format, while CPORT is a proprietary format.

Q. What do you mean, the XPORT format is "open?"

A. Specifications for the XPORT transport format are in the public domain. Data can be translated to and from the XPORT transport format to other commonly used formats without the use of programs from SAS Institute or any specific vendor.

Q. Why does FDA want an open format?

A. By US law, the FDA must remain "vendor neutral." The FDA cannot endorse or require use of any specific vendor's product.

Q. What is the XPORT transport format, generally?

A. It is a text file, with record length = 80 columns. It looks and feels so much like a text file that it is a good idea to avoid using ".txt" as a file name extension so that the operating system won't treat it as a text file.

When submitting the SAS datasets to FDA, we need to be sure that the .xpt files are generated using PROC COPY. After the .xpt files are generated, we should test if the files can be opened using JMP and SAS Viewer. If the .xpt files are generated correctly, they should be automatically opened if we click the file names.
According to SAS document on PROC COPY, it is very easy to use PROC COPY to create .xpt file.

libname source 'SAS-library-on-sending-host';

                     /*indicating where the original SAS data sets (in .sas7bdat format) are*/

libname xptout xport 'filename-on-sending-host'; 

                      /*indicating where the .xpt files to be stored */
proc copy in=source out=xptout memtype=data;
run;

Alternatively, .xpt files can be created using PROC CPORT. However, .xpt files created by PROC CPORT can not be opened with JMP and can not be opened in SAS Viewer. .xpt file created by PROC CPORT must be opened and retrieved by using PROC CIMPORT.

Since FDA will only accept the .xpt files generated using PROC COPY, I see no reason to use PROC CPORT or PROC CIMPORT.

Placebo Mean Imputation (PMI)

A while ago, I discussed several simple imputation methods in LOCF, BOCF, WOCF, and MVTF. Recently, I noticed another simple imputation method Placebo Mean Imputation (PMI). This simple imputation method of PMI seems to be used Only for the purpose of sensitivity analyses, not for the primary analysis. It is also true that this approach is mainly used in certain therapeutic areas such as analgesic drug (pain medication) and anti-bacterial drug.  

 

In FDA's Clinical Review document for a chronic pain medication in 2011,  the Placebo mean imputation is described:

 "Placebo mean imputation (PMI): the missing pain measurements for each day after discontinuation were replaced by the mean of all available pain intensity scores for all placebo-treated patients who completed treatment. Therefore if a patient discontinued treatment or recorded their last pain score at Week 8 of the Maintenance Period, the pain intensity score at Week 12 was imputed using the Week 12 mean pain intensity score for all placebo-treated patients who completed treatment. Also a placebo missing pain score at some time-point was imputed by the observed placebo group mean pain intensity at the same time-point"

In FDA's Statistical Review for the same indication, FDA statistician assessed that PMI is not an appropriate approach

"Since the estimated treatment effect is only influenced by the data from patients completing the study, the PMI method is similar to an analysis of completers. Analyzing completers is
problematic since the outcome of patients completing the study may not represent the outcome of patients not completing the study. In the placebo group, patients completing the study are likely to be the less severely afflicted patients; while in the NUCYNTA group, patients completing the study are likely to be the more severely afflicted patients. As a result, the PMI method assigned good scores from the placebo completers to patients dropping out due to adverse events in the treatment group. Based on these reasons, I conclude that the PMI method is not appropriate."

In a briefing document for FDA anti-infective drug advisory committee meeting for a cystic fibrosis drug in 2012, PMI approach was used by FDA statistical reviewer:

“…In this sensitivity analysis, missing values were imputed using the placebo group mean of -0.57% (i.e. the minimum of 0 and the least favorable group mean) as performed in the Reviewer’s primary analysis and other Reviewer analyses.”

The use of these simple imputation approaches is mainly driven by the perception that these approaches provide conservative estimate of the treatment effect (people have shown that this is not always true). This fits into the Intention-to-treat principle to be conservative in estimating the treatment effect in superiority studies. While it is ok to perform the sensitivity analyses using various simple imputation approaches such as LOCF, BOCF, WOCF, and PMI, these imputation methods should not be used as the primary analysis. 

Further Reading: 

• Gual et al (2013) A randomised, double-blind, placebo-controlled, efficacy study of nalmefene, as-needed use, in patients with alcohol dependence 
• Carpenter and Kenward (2007) Missing data in randomised controlled trials — a practical guide
• The National Academy Press (2010) The Prevention and Treatment of Missing Data in Clinical Trials