Copernicus and Elegant Models

Nicolaus Copernicus (1473-1543)

In 1543, the year of his death,  Nicolaus Copernicus published De Revolutionibus Orbium Coelestium. In English, it roughly translates to mean On The Revolution of Celestial Spheres.  Now considered his greatest scientific achievement, it established that the Sun as the center of our solar system and that all the planets including Earth, revolve around it. We now call this the heliocentric model of our solar system, or simply heliocentrism.

The prevailing model at the time was that the Earth was at the center of the universe. Also accepted was that the Earth was stationary and other spheres. including the sun, moved around it. This geocentric model prevailed for well over a millennium: since the 2nd century A.D. , and is most often attributed to Ptolemy,  a Greek philosopher, astronomer and mathematician from what is now Alexandria, Egypt.

The transition to the new model didn't occur quickly or easily. Those opposed to Copernicanism often did so for religious or philosophical reasons, but eventually it became clear that the Copernican model more accurately, and more simply, predicted the movements of the heavenly bodies across the sky.  It had become clear some time before that the Ptolemaic model could not explain certain observations, such as the fact that planets often seemed to stop moving completely or sometimes travel backwards across the sky. For this reason, fudge

The Ptolemaic Model of the Solar System

factors were added to the Ptolemaic model such as epicycles, deferents, and equants so that the model more closely aligned with reality.

An epicycle was a circle whose center travels along the circumference of a larger circle, the latter of which is known as a deferent. Together they helped explain why planets sometimes move backwards across the sky. An equant is the point from which each body sweeps out equal angles along the deferent in equal times. The center of the deferent is midway between the equant and Earth.

These adjustments, or corrections, were important to make the most use of the Ptolemaic model. In addition, church opposition to Copernicanism was often fierce, and initially its acceptance was slow. The argument went something like this:   

How can God the Almighty not place Earth at the center of the universe, since Earth was the most important celestial body of them all? 

But eventually astronomers retired the Ptolemaic model and Copernicanism prevailed. This ushered in  a golden age of astronomy during which scientific discoveries quickly took off and our understanding of the solar system greatly increased.

Fundamentals of the Ptolemaic Model

The take home lesson from this 16th century story is clear. Sometimes you have to retire older, inaccurate models for newer ones that are both simpler to implement, and more accurate. Modelers call these elegant models. So let's fast forward to the 21st century. Could we be experiencing a similar moment with regard to clinical trials data models? I think we are.

Today, we regard the experimental subject in a clinical trial to be the most important entity of a trial. In fact he/she is the most important entity....and all the effort to collect and understand the effects of new therapeutic agent exist with this entity in mind. The Subject is and has been for decades the center of the clinical trials universe, with all other concepts being divided, misunderstood, sometimes neglected, in the model's periphery. But there is a problem with subjects at the center. Subjects are unpredictable. Odd, unexpected things happen to them and often new, unexpected data are collected but they have no room in the subject-centric model. New data often don't fit in this model and have nowhere to go. New constructs are developed to contain and explain them, things like Supplemental Qualifiers, Related Records, and Findings About domains (see Study Data Tabulation Model). These are the epicycles, deferents, and equants of the subject-centric clinical trials universe. In addition, we must recognize that the current model for observations (being made up of findings, interventions, and events) to be fundamentally flawed. We need to fix this in the same way the Ptolemaic model was fundamentally flawed and needed to be redone (see my previous post on this topic).  When we do so, the need for these fudge factors disappears as you will see below.

"Copernican" View of Study Data

So what is the answer? We remove Subjects from the center of a clinical trial universe and replace them with Study Activities (an activity-centric model).  Study activities are, after all, critical for a study's success, and are easily linked to the subject to which the activity pertains.  They become the unit of record for any study.  Study Activities may be observations, assessments, or administrative activities (informed consent, randomization), Study Activities are more predictable, the metadata needed to fully describe the activities are generally more stable, and, by placing them at the center of the data model, we make them easier to represent, understand, and analyze. We also correct mistakes in how observations are modeled. The end result is a model that is not only simpler to implement, but more capable of representing study data and their true meaning. Yes, we have a more elegant model that is closer to reality. 

But clinical trial data aren't spheres that orbit the sun. How do we make this more practical and implementable? The answer is we describe these new definitions and relationships as an ontology that is understandable to both subject matter experts and information systems. It goes something like this.

At the center of our universe is the Study Activity, defined as any activity associated with the planning, conduct, analysis, and interpretation of a study.   We recognize different sub-types, such as study design activities (Arms, Epochs, Visits, etc.), administrative activities (informed consent, randomization), intervention activities (clinical observations, therapeutic interventions), analysis activities. One highly important type of study activity is Assessments, and they need to be added to the model. More on this later. Activities can be defined, protocol specified, planned, scheduled, performed. Every activity has an outcome, which in the case of observations, is the result. Activities also have start rules that link it to other activities (e.g. High Dose Drug administration activity begins on the same day that the Randomization activity is completed and the activity outcome equals High Dose group. Or Blood Pressure Activity begins 3 minutes after completion of Change in Body Position activity from supine to sitting.) Activities may also have sub-activities. For example, a simple Blood Pressure is made up of a Systolic BP measurement and a Diastolic BP measurement.

So our model, viewed as a mind map, starts looking like this. The core classes are shown along with the important relationships.

Core Study Ontology

The next step is to correct the current modeling of observations, which in the Study Data Tabulation Model includes findings, interventions, and events. The following new definitions are proposed, which I believe align more closely with the way these terms are used in clinical medicine.  The definitions are written as Aristotelian Definitions whenever possible to facilitate development of machine readable/executable software.

Intervention:  A Study Activity that involves an interruption to the study subject's normal daily routine for the purpose of observing the subject's physical, psychological, or physiological state, and/or mitigating the effects of a medical condition.

There are three main sub-types of Interventions: 

(Clinical) Observation:   An Intervention whose intent is to measure the physical, physiological, or psychological state of a Study Subject.

Please see my previous post for more thoughts about Observations.

Therapeutic Intervention:   An Intervention whose intent is to have an effect (e.g. treat, cure, mitigate, prevent) on a Medical Condition. (A drug administration is an example of a therapeutic intervention.)

There are other types of Interventions, but for the sake of brevity they are not covered here. Then we have a markedly updated definition of Events:

Event: An occurrence; something that happens. An event persists in time.

Followed by important types of Events:

Medical Condition:  An event that is a disease, injury, disorder, or transient physiologic state that interferes or may interfere with well-being. A medical condition persists in time.

Adverse Event:  A Medical Condition that emerges or worsens following a Medical Intervention, including the use of a drug. Note: there is no presumption of causality. Please refer to a previous post for more information about modeling Adverse Events.

There are other types of events, such as Diagnoses, Indications, Underlying Conditions, but for the sake of brevity we can stop here.

As previously stated, Assessments are an extremely important Study Activity that is notably missing from today's models, and which is sorely needed:

Assessment:  An Examination or Analysis of one or more clinical Observations to identify and/or characterize a Medical Condition.  (When formalized, this is also called an Adjudication.) 

One can easily spend a great deal more time discussing assessments and how to distinguish them from observations, but instead I refer you to a previous post on this topic.

These critically important concepts are modeled as follows in our core ontology. We must recognize that Adverse Events are not Observations. We collect observations and then perform an assessment to determine whether an Adverse Event (a Medical Condition) is related to or helps explain the presence of the AE.  Often this assessment is not captured in writing... it is done by the subject via self assessment, or by the Investigator in the clinic, but an assessment must occur. Not having a place in the current model is a notable omission, in my opinion. So the new high level model looks something like this:

Modeling Observations and Assessments

Consider a Subject who has a measured Blood Pressure in the clinic of 160/110 (a clinical observation). Does this mean that the Subject has a Medical Condition (Essential Hypertension) that requires treatment? No. Additional observations need to be recorded and analyzed (Assessed) to make that link. Is the person obese and the wrong BP cuff size used? Is the person nervous and has transient elevated BP due to anxiety? Very often, serial blood pressures in various settings can help sort this out. If the BP was recorded shortly after taking an experimental treatment, is this an Adverse Event of the treatment? No, for the same reasons. An Assessment needs to be done.  Read more about Assessment in a previous post.

By moving to an activity-centric model, expressing a protocol in a machine-readable/executable format is fairly simple. The protocol essentially becomes a list of all protocol-specified activities, each of which is richly linked to entities and other activities. For example, consider a simple trial consisting of the following screening activities: informed consent, serum RPR (test for syphilis), and fasting blood sugar (FBS). The protocol states that informed consent comes first, and once it is obtained, the RPR and  FBS are obtained within 3 days but not to exceed 7 days. The informed consent activity is linked to a Default Start Rule saying that it starts anytime. The RPR and FBS each have a start rule that is linked to the successful completion of the informed consent activity. Appropriate delays (3 days) and maximum delays (7 days) are captured in the start rule. Epochs and other study design activities are defined by start and stop rules of the sub-activities within each study design concept. In the case of the Screening Epoch, it begins when Informed Consent begins, and ends when yet another activity: Eligibility Determination activity is conducted, Those who are eligible (Eligibility Determination outcome equals true) can begin the Randomization activity. The Eligibility Determination Activity in essence is the Start Rule for the Randomization activity, and so forth. Using this approach, complex protocol design features such as adaptive designs, interim analyses and early termination can be defined using precise start rules and can lead to automated assessments of study conduct and automated identification of protocol violations. For more details on this approach, see a previous post about StudyActivity Start Rules.

By taking a historical perspective, we must commit to retire antiquated, inaccurate models for clinical trials and transition to an elegant activity-centric model for clinical trials data. It would solve many of the problems we continue to encounter and can enable much more powerful techniques to automate the clinical trials enterprise. In the same manner that we retired epicycles, deferents, and equants in astronomy almost 500 years ago, it's time to retire Supplemental Qualifiers, Related Records, and Findings About domains in our quest for a more elegant model for clinical trials data.

In closing, I invite you to review and dissect and comment on this new model for study data, which is written in OWL (Web Ontology Language) and available for download on GitHub. Although it is still a draft, the core fundamental classes and relationships have remained quite stable throughout its development. Special thanks go to Tim Williams and PhUSE, who continue to play significant roles and provide tremendous support in the development of this ontology.

I look forward to hearing from you.

Assessments (revisited)

Today I'd like to focus on Assessments in clinical medicine, including clinical research, with an emphasis on how to represent Assessment information optimally for data exchange. I've written about Assessments before. I'd like to revisit this topic again because, as standards continue to evolve and improve, we as an industry continue to fumble how we handle assessment information. I think this creates unnecessary challenges and limitations in how we document and exchange assessment information. I continue to see widespread confusion between what is an assessment vs. what is an observation. It remains common to see a 'schedule of assessments' in a study protocol when we really mean a schedule of observations. True assessments are not observations and yet they are critically important in understanding and analyzing adverse event reports or clinical trials. Standardizing assessment information remains a critical need in data standardization efforts to support automation.

First some working definitions. I use Aristotelian definitions whenever possible.

• A (Clinical) Observation is an Intervention whose intent is to measure the physical, physiological, or psychological state of a Person. 

• An Assessment is an Analysis of one or more Observations to identify and/or characterize (e.g, severity assessment) of a Medical Condition. 

• A Medical Condition is an Event that is a Disease, Injury, Disorder, or transient physiologic state (e.g. Pregnancy) that interferes or may interfere with well-being. 

These definitions lead to various corollaries.

• An Observation is a finding (as defined by CDISC) but it is also an intervention, since the observer must intervene in a person's normal routine to make and record the observation. Sometimes the details of the intervention (device used, method, etc) can affect the observation results, so are therefore worth recording. 
• An Adverse Event is not an Observation (as defined by CDISC). It is instead a Medical Condition that is temporally related to a therapeutic intervention, such as a drug administration. The relationship between an AE and an Observation is as follows. One or more observations may support the presence of an AE after a proper assessment. I note that the U.S. regulations used the words "occurrence," which is my opinion is synonymous with an event. 

Getting back to Assessments, physician's are trained from the earliest days in medical school that first one observes, then one assesses (i.e. interprets the observations) before deciding whether to treat. This makes up the familiar template of a patient encounter known as SOAP (Subjective observations, Objective observations, Assessments, Plan).

Often times Assessments are not formally conducted or documented in clinical trials, so there are no assessment data to standardize. A temperature reading of 39C (an observation) is almost always assumed to be a fever (assessment result-->medical condition), unless there are additional data to suggest something else (faulty digital thermometer, or evidence of the bizarre Munchausen Syndrome). Sometimes Assessment information is critical to ensure proper diagnosis and treatment, and in the case of clinical trials, treatment with the appropriate investigational drug. The Assessment, when formalized and standardized in the protocol, is called an Adjudication. (note: this is not to be confused with the process of ensuring an accurate observation by having, say, an independent blinded reader looking at an imaging study or pathological specimen to determine the accuracy of the observation result. The latter is simply a feature of an observation method to ensure quality measurements).

Speaking of independent observers and assessors, often times third party assessors (e.g. radiologists, pathologists) provide an independent assessment of certain observations (e.g. x-ray, lab results) when the investigator is not qualified to make his or her own assessment of the findings. These reports typically contain two sections. The first one describe the findings/observations (e.g. brain histology showing cortical atrophy, neurofibrillary tangles, amyloid plaques) followed by the assessment: findings are consistent with Alzheimer's Disease.

Because we currently confuse observations and assessments, we have no standard way to report assessment information. Currently sponsors use three possible approaches:

1. Include assessment information in findings domains
2. Include assessment information in supplemental findings domain
3. Include assessment information in custom domains

As one can imagine, the variability in reporting Assessment information currently is a significant problem. At the very least, we need an Assessment domain where one can find at a minimum:

• an Assessment ID
• what observations were used in the assessment
• who is/are the assessor(s) with a link to their qualifications
• date/time of assessment
• outcome of the assessment: i.e. cause of the observations, usually a Medical Condition, with a link to a medical conditions domain. 
• severity of the medical condition at the time of the assessment
• what method was used for the assessment (e.g. diagnostic criteria)
• what method/scale was used to document severity

It is important to note that one can have multiple assessments for the same set of observations. In health care this is known as a second opinion. Sometimes that second assessment uses different assessment criteria that the assessor believes is more relevant. A clearer picture now emerges of a Person/Subject having one or more medical conditions that are identified via an assessment of observations (some assessments are well documented, others not so much or at all), and changes in those medical conditions are documented via multiple observations and assessments at different points in time. This paradigm applies equally to the indication (i.e. the disease/condition being treated in the clinical trial) but also to new Medical Conditions (e.g. adverse events) that arise during the trial itself.

And let's remember, Death is not an adverse event, rather one of various possible outcomes for an  adverse event as the medical condition evolves over time. 

As an immediate short-term solution, SDTM should add an Assessment domain which links Assessment information to the observations that were used in the assessment. This would be a big step forward.

Activity Start Rules in Study Protocols

Imagine a software tool that reads a study protocol, can analyze the data collected on any subject to date and determines what activity to perform next. Imagine all of the data collection errors and protocol violations that can be avoided by following the instructions from such a tool. From a regulatory perspective, imagine running an analysis that describes in sufficient detail whether a protocol was conducted properly and can automatically determine where protocol violations occurred. This is possible if we have machine-readable start rules for study activities.

I recently wrote about Study Rules. Today I continue that discussion by looking at Start Rules and how to represent them computationally in the RDF (Resource Description Framework)  to enable the tools described above.

The start rules are rules that determine if an StudyActivity can begin or not.  The start rule describes the condition(s) present in the collected data that must be met for a StudyActivity to begin. If the condition is met, the StartRuleOutcome is true and the activity may begin. If the condition is not met, then the outcome is false.

All study activities have start rules, although they may not be explicitly stated in the protocol.  Often the start rule can be restated more explicitly. It is also useful to consider a "default" start rule which is always true (i.e. the condition to start the activity is always automatically met) and therefore the Activity can begin anytime. The very first activity in a study, usually "ObtainInformedConsent" can begin anytime. Its start rule is the default start rule. The start of the "ObtainInformedConsent" activity also marks the start of the Screening epoch (activity). Screening is a composite activity (i.e. made up of multiple sub-activities) whose start rule says begin Screening when the ObtainInformedConsent activity begins.

Start Rules can describe not only whether the activity can begin, but when it can or cannot begin relative to another activity. There are different types of start rules.

1. A "Prerequisite Start" Rule (PRST) allows the target activity to begin when the prerequisite activity has started. 
2. A "Prerequisite Complete" Rule (PRCO) allows the target activity to begin when the prerequisite activity is completed, regardless of the outcome. 
3. A "Prerequisite Outcome" Rule (PROUT) allows the target activity to begin when the Prerequisite activity is complete and has a certain outcome or result. 

Start rules may be associated with a protocol-specified delay.

1. A delay = 10 mins means wait 10 minutes after the start condition is met before performing the activity. 
2. A maximum delay = 10 mins means wait no longer than 10 minutes before starting the activity. 
3. A minimum delay = 10 mins means wait at least 10 minutes before performing the activity.

Certain planned activities can be skipped altogether if a certain condition exists. For example, if the sex data collection activity results in Sex=M, then a Pregnancy Test can be skipped. This could be captured in a separate "Skip Rule;" a topic for a future post (or better yet, a start rule for a start rule!). In this case, the start rule can be skipped and the activity becomes a "logically skipped"activity per protocol, and there is no violation.

So a start rule has the following properties/predicates
:prerequisite (links to the prerequisite activity)
:prerequisiteExpectedOutcome (the expected outcome/result for the prerequisite activity.)
:prerequisiteExpectedStatus (the expected status for the prerequisite activity.)
:skipActivity (link to the activity that determines if the target activity can be skipped)
:skipOutcome (the expected outcome of the skip activity that allows the target activity to be skipped
(note: this may be better coded in a separate skip rule)
:ruleDescription (a short textual description of the rule)
:ruleDescriptionLong (a long description)
:subRule (a link to another subordinate rule)
three timing attributes: :delay, :delayMin; delayMax (as discussed above).

How does this look using RDF (the Resource Description Framework)?

I have created a "dummy" clinical trial for migraine prevention that starts with the InformedConsent activity and ends up with a randomized treatment activity. During screening, subjects undergo [1] recording the sex of the subject, [2] an RPR test for syphilis, and [3] a pregnancy test, if female.  To be eligible for continued participation, one must have a negative RPR and a negative pregnancy test, if female. Those that are eligible are Randomized to two treatments: LowDose 10mg daily or HighDose 20mg daily.  Those randomized are given study medication to prevent their migraines.

First we link the activities to the subjects:

data:Person_1  
        rdf:type smm:Person ;

        smm:participatesIn       
                  data:Observation_Sex_1 , 
                  data:Observation_RPRTest_1 ,               
                  data:randomization_BAL2_1 , data:ProductAdministration_1 , 
                  data:InformedConsent_ADULT_1 , data:Observation_PregnancyTest_1. 

Then we link a start rule for each activity. In the table below the predicate (not shown) is smm:startRule.

Except for the default start rule, which says the activity may begin at anytime, each start rule has a prerequisite activity (one that must take place before the target activity. In our example, the rule for the pregnancy test checks to see if the patient is Female (i.e. does the Sex recording activity document Sex = F? If yes, then the rule is triggered (outcome = true) and the test is tagged for execution.  If the Sex = M then the rule outcome is "not applicable" since the test is not typically performed on male subjects.

Notice that the RPR Test and the Sex observation Test both have the same start rule, which requires that InformedConsent be complete with Outcome InformedConsent_GRANTED. Both tests become eligible for execution simultaneously.

Once all the screening activities have been conducted, the next activity "Eligibility Determination" is triggered and has a binary outcome: True (subject is eligible to continue) or False (subject may not continue). This Eligibility Determination activity is in fact the Start Rule for the Randomization activity. This web of inter-related activities is ideally represented in a graph and makes it quite easy to manage study conduct and monitor the trial for protocol violations.