by
Arno Klaassen
August 20 1996
Nr. 383
Information systems
Department of Computer Science
University of Nijmegen, The Netherlands
Urologic Informatics Center/BioMedical Engineering
Department of Urology
University Hospital Nijmegen, The Netherlands
Abstract
PROSYS is developed to support the development of clinical trial protocols. A part of this clinical trial protocol is subject selection. In this thesis an attempt will be made to develop an inference mechanism for the selection of patients for a clinical trial. First there is given an introduction into the medical science. After this a basic model for medical knowledge will be presented. This model will be adapted to create a model that is able to define all kinds of medical knowledge and to store selection criteria. After this an inference mechanism will be developed to store and develop the selection criteria needed according to a given study objective. This inference mechanism will be evaluated by creating a prototype for the development of selection criteria: SCDS (Selection Criteria Development System).
Table of Contents
LIST OF FIGURES... 6
LIST OF TABLES ... 8
1 INTRODUCTION ... 10
1.1 CLINICAL TRIALS - CURRENT PROBLEMS IN PROTOCOL DESIGN... 10
1.1.1 Clinical trials... 10
1.1.2 Phases of clinical trials... 10
1.1.3 Clinical trial protocols... 11
1.1.4 Problems in protocol design... 11
1.2 PROTOCOL DESIGN SYSTEM (PROSYS)... 12
1.3 PROJECT DEFINITION... 15
1.4 SUMMARY... 15
2 DETERMINATION OF THE INCLUSION AND EXCLUSION CRITERIA FOR A NEW TRIAL ... 16
2.1 INTRODUCTION... 16
2.2 PROSYS-PART... 22
2.3 KNOWLEDGE REPRESENTATION - THE DEVELOPMENT OF AN ONTOLOGY... 23
2.3.1 An extended ontology to model domain knowledge needed for the development of selection criteria ... 26
2.4 AN INFERENCE MECHANISM FOR DEVELOPING SELECTION CRITERIA... 46
2.4.1 Preparation for the inference mechanism ... 48
2.4.2 Step 1: Deriving criteria based on the Study Objective ... 50
2.4.3 Step 2: Developing criteria based on inference steps ... 52
2.4.4 Step 3: Trying to guarantee completeness of selection criteria ... 55
2.5 SUMMARY... 57
3 SCDS: A SYSTEM FOR DEVELOPING SELECTION CRITERIA ... 58
3.1 DATABASE DESIGN... 58
3.2 THE INFERENCE MECHANISM... 58
3.3 EVALUATION... 59
3.4 ACCEPTANCE TEST... 60
3.5 SUMMARY... 61
4 DISCUSSION AND CONCLUSION ... 62
APPENDIX A: PSM ... 64
APPENDIX B: BASIC ONTOLOGY FOR A CLINICAL TRIAL ... 66
APPENDIX C: PERFORMANCE STATUS CRITERIA ... 70
APPENDIX D: HYDRA ... 72
APPENDIX E: LISA-D ... 74
APPENDIX F: INITIAL POPULATION FOR THE INFERENCE MECHANISM ... 76
GLOSSARY ... 82
List of figures
Figure 1: Information flow diagram ... 13
Figure 2: Overview of the information topics described in a protocol... 13
Figure 3: Partial representation of dependencies... 14
Figure 4: Patient recruitment in a clinic that consistently performed at goal rate. ... 19
Figure 5: Patient recruitment in a clinic that started slowly and then performed a greater than goal rate... 20
Figure 6: Patient recruitment in a clinic that performed poorly. ... 20
Figure 7: Graphical representation of construction selection criteria ... 23
Figure 8: Ontology as developed by [d’Hollosy 1995]... 25
Figure 9: PSM model of the study objective... 32
Figure 10: PSM model of the Patient Characteristics. ... 35
Figure 11: PSM model of the Disease Characteristics criteria. ... 38
Figure 12: PSM model of the Environment Characteristics and Safety Criteria... 40
Figure 13: PSM model for the development of selection criteria. ... 44
List of tables
Table 1: The information items in a clinical trial protocol ... 14
Table 2: Items to consider as criteria for patient selection... 18
Table 3: An example study objective points of interest. ... 31
Table 4: ECOG performance status... 70
1
Introduction
In this introduction a brief overview of this master science project is given. This introduction consists of the following topics:
• Clinical Trials - Current problems in protocol design
• PROtocol design SYStem (PROSYS)
• Definition of this master science project
In clinical trial design a description of clinical trials is given. PROSYS, a knowledge based system to support the development of clinical trial protocols will be pointed out and the relation between PROSYS and clinical trials is described. After this the definition of this master science project is given.
1.1
Clinical Trials - Current problems in protocol Design
1.1.1
Clinical trials
Clinical research is performed to improve medical knowledge on for example the symptoms and course of diseases or to develop or improve treatments. One form of a clinical research study is a clinical trial. A clinical trial is an experimental study on medical products in human subjects to establish the efficacy and safety of these products by investigating treatments and comparing the outcomes in a group of patients treated with the treatment with those observed in a comparable group [d’Hollosy 1995, Meinert 1986].
1.1.2
Phases of clinical trials
A clinical trial is mostly conducted in different phases. These phases can be divided as follows [Pocock 1983, Spilker 1985]:
Phase 1: The first phase of a clinical trial is mainly focused on testing the safety of a new treatment. These tests are usually performed on a very small group of human volunteers, except when a treatment is tested with a high level of toxicity.
Phase 2: In the second phase the treatment is tested again, but as safe as possible, based on the experience obtained during the first phase. The goal is to demonstrate the effect of the treatment on a small group of patients and to collect more information on the safety of the treatment. In this phase, the risk is that a new
effective treatment does not show significant effects on the group examined
patients and the testing of the treatment will not be continued. This is called a Type II error, the probability of not detecting a significant difference while there is actually a difference.
Phase 3: The third phase is often the last phase to test a treatment. The treatment is tested on a large group of patients compared to a control treatment. The goal is to investigate the balance between safety and efficacy of the treatment on the short and long term. To some people the term ‘clinical trial’ is synonymous
with a full-scale phase III trial, which is the most rigorous and extensive type of scientific clinical investigation of a new treatment [Pocock 1983].
Phase 4: The fourth phase, which is not always performed, is to investigate the safety and efficacy of the treatment on the long term. In this phase the treatment is already an existing treatment for a particular disease. The term ‘phase 4 trials’ is sometimes used to describe promotion exercise aimed at bringing a new drug to the attention of a large number of clinicians [Pocock 1983]. If this is the case, this phase has limited scientific value and should not be considered as a part of clinical trial research.
1.1.3
Clinical trial protocols
Information on a clinical trial is fully described in a protocol. This includes the arguments, goals and design of the clinical trial. The purpose of creating a protocol for a clinical trial is to safeguard the testing of a new treatment and to do some standardisation on testing a treatment. Also it provides some anchor points, at which one can see if a certain protocol part has succeeded. The approval or ejection of a new trial by scientific and ethical committees is based on the ethical and scientific contents of this protocol. After approval, the protocol is used by people who conduct the clinical trial.
A protocol should contain at least the following information [d’Hollosy 1995]:
• Introduction • Study objectives • Subject selection • Ethical aspects • Study design • Treatment(s) • Evaluation • Statistical aspects • Administration
1.1.4
Problems in protocol design
The development of a clinical trial protocol is a difficult process. Problems that can arise are:
• Incoherence between different protocol parts.
• Ambiguity or incompleteness of information.
• Errors in statistical design of the trial.
Because the development of a clinical trial protocol is a difficult process the protocol will usually be evaluated more than once. For example a draft protocol is evaluated by colleagues. This colleague makes several certain changes to the draft protocol. When the protocol has been adapted the protocol must be evaluated once again before it can be sent to an ethical committee. These evaluations are time consuming, so developing a clinical trial protocol is quite a time consuming process.
1.2
PROtocol design SYStem (PROSYS)
In the previous paragraph problems with protocol design have been pointed out. There has made an attempt to reduce these problems, by developing an information system that supports protocol design. The UIC/BME has started the development of a knowledge-based system that should support the development of clinical trial protocols in the future. Support of this system should avoid as much as possible the problems mentioned in the previous paragraph. The name of this system is PROSYS (PROtocol design SYStem).
PROSYS is developed to computerise the development of clinical trial protocols. This support should lead to a complete and high quality protocol contains information that is:
• Complete
• Unambiguous
• Coherent
• Correct
The second aim of the development of PROSYS is to fasten the approval of new clinical trials. Nowadays a clinical trial is often evaluated more than once due to not satisfying one or more of the above mentioned constraints. This extends the time of approval of a clinical trial. The idea is that the support of a computerised system as PROSYS improves the quality of first version clinical trial protocols, which will fasten the approval of a new clinical trial. A new clinical trial can than be started as soon as possible.
Writing a research protocol for a new study is the development of this new study. If the protocol contents and the order in which the relevant information for these contents should be obtained are known then this order describes the framework of the protocol preparation process.
There are several organisations that provide guidelines to write a well designed and complete research protocol for a clinical trial. Based on the guidelines for the preparation of EORTC1 cancer clinical trial protocols [Staquet 1980], the guidelines of the EEC2 [GCP 1990] and two existing already approved protocol in urological research [Prot1 1992, Prot2 1994] an overview on the relevant contents of a clinical trial protocol has been made, which resulted in a list of 30 information blocks. The information blocks are concerned to only a few specific topics [Figure 2]. The list of information items is shown in table 1 This list of information blocks is used as foundation to develop PROSYS. The contents of this table are used to describe the preparation process of the clinical trial protocol, that starts with working out the research objectives. A part of this process is shown in Figure 3. PROSYS is divided into several parts, called PROSYS-parts. Each PROSYS-part is responsible for working out the process that results in the desired information. Each PROSYS-part can be seen as a stand alone information system. All processes generate trial information that depends on the incoming information of the involved information blocks. The incoming information can consist of information from users or from other PROSYS-parts. The outgoing information of an information block serves as input for other PROSYS-parts or for users [Figure 1].
1
European Organisation for Research and Treatment on Cancer.
2
Figure 1: Information flow diagram
Figure 2: Overview of the information topics described in a protocol
Information topic Information blocks
Introduction 1. Description and prognosis of the disease 2. Current treatments
3. Results of other, relevant, studies 4. Rationale of the study
Study objectives 5. Title
6. Research objectives
Subject selection 7. Inclusion and exclusion criteria Ethical aspects 8. Ethical study considerations
9. Informed consent
Study design 10. Study type (e.g., phase 2 study, phase 3 study, …) 11. Study design (e.g., double blind, cross-over, ..) 12. Endpoints of the study
Miscellaneous Introduction Study objectives Administration Statistical aspects Evaluation Subject selection Ethical aspects Study design Treatment(s) PROTOCOL User Other PROSYS-part Other PROSYS-part PROSYS-part Other PROSYS-part User
Information topic Information blocks
Treatment(s) 13. Detail description per treatment
14. Instructions to deal with adverse events (e.g., toxicities)
15. Instructions to deal with deviations from the protocol (e.g., patient withdrawal)
Evaluation 16. Study variables and measuring methods 17. Measurement schedule
18. Forms and procedures for data collection Statistical aspects 19. Statistical method
20. Significance level 21. Sample size 22. Study duration 23. Randomisation method 24. Stratification method Administration 25. Registration method
26. Administration with relation to the study participants (e.g., name and professional background, participating centres , addresses, phone numbers, function division in the study, co-ordination team, et cetera.) 27. Administration with relation to the study protocol (e.g., start date of the
trial, date(s) of protocol version(s), approval date(s)). Miscellaneous 28. Quality control
29. Additional information (e.g., finance, insurance) 30. References
Table 1: The information items in a clinical trial protocol
Figure 3: Partial representation of dependencies
Research objectives Study type
Endpoints of the study Study variables and measurement methods
Detailed description per treatment
Registration method Inclusion/exclusion criteria Measurement schedule
Forms and procedures for data collection
1.3
Project definition
The inclusion and exclusion criteria specify the human subjects from which data has to be collected. These inclusion and exclusion criteria are based on the research objectives and a detailed description for a treatment. The assignment of this master thesis is the following:
Defining and implementing of the knowledge and the reasoning process that should lead to the inclusion and exclusion criteria needed for new clinical trials.
This thesis consists of four chapters: 1. Introduction (this chapter)
2. Determination of the Inclusion and Exclusion Criteria for a new Trial 3. SCDS: a prototype for the development of selection criteria
4. Discussion and Conclusion
Chapter two describes the development of the inclusion and exclusion criteria and is showing the inference engine to develop these criteria.
Chapter three presents the prototype for the development of selection criteria. This prototype is called Selection Criteria Development System (SCDS).
1.4
Summary
In this chapter the world of clinical trials has been introduced to the reader. The aim of this chapter was to point out what a clinical trial is and to give an introduction into this master science project. For further information on clinical trials see [Meinert 1986, Pocock 1983, Sylvester 1995].
2
Determination of the Inclusion and Exclusion criteria for
a new Trial
2.1
Introduction
In clinical trials, new treatments are tested on human beings, mostly patients that are suffering from the disease a new treatment is intended to. Human subjects are included in a clinical trial only when satisfying a set of inclusion criteria and not satisfying any of the exclusion criteria. For each trial these criteria are developed to create the desired subject population.
Now an example is given of inclusion and exclusion criteria that are used in an existing Phase III trial [Win 122] to give an idea what is meant by inclusion and exclusion criteria. The objective of this study was to evaluate the value of the treatment Interleukin-2 in terms of disease free and overall survival of patients and their quality of life, after being treated against cancer. The inclusion and exclusion criteria that were used are:
Inclusion criteria:
Patients will be eligible for participation in the study provided all the following criteria are met:
• Histologically proven Renal Cell Carcinoma.
• Patients should have undergone surgical resection of the primary tumour and lymph nodes.
• Nodal status N 1 or 2.
• There should be no macroscopic residual disease.
• Ambulatory performance status (ECOG 0-1; Karnofsky ≥ 80%3 ). Age < 70 years old and a life expectancy greater than 3 months.
• WBC ≥ 4.000, platelets ≥ 120.000 and HCT ≥ 30%.
• Randomisation should occur within one month following surgery and treatment should start between 4-6 weeks after surgery.
Exclusion criteria:
Patients will be excluded from participation in the study if one of the following criteria are met:
• Any of the above criteria are not met.
• Unstable angina pectoris or recent (6 months) myocardial infarction.
• Evidence of active infections requiring antibiotic therapy.
• Patients with major organ allografts (Interleukin-2 increase T-cell mediated rejection and immunosuppressive agents are likely to reduce efficacy of Interleukin-2).
• Patients with signs or symptoms of systemic metastatic Renal Cell Carcinoma.
• Patients who require or are likely to require corticosteriods for intercurrent disease.
3
• Pregnant or lactating women.
• Patients with previous malignancies, except for basal cell carcinoma of the skin or cervical cancer.
• Patients who receive radiation or chemotherapy.
In literature the phrase ‘selection criteria’ is synonym for the phrase ‘inclusion and exclusion criteria’. In the remainder of this master thesis the phrase ‘selection criteria’ is used, because each exclusion criterion can be written as a denial and would then be an inclusion criterion. For example, the inclusion criteria in [Win 122] ‘There should be no macroscopic residual disease’ could be written as the exclusion criteria ‘Patient has macroscopic residual disease’. Selection criteria used in a clinical trial should satisfy the following constraints:
• The selection criteria should guarantee ethics for the patients.
• The selection criteria should guarantee complete safety for the patients.
• The selection criteria should ensure a the selected patient population that is a good reflection of the group for which the treatment is developed [Jeffcoat 1992].
• The definition of the selection criteria should be precise and unambiguous.
Based on these constraints, except for the last constraint, the selection criteria can be distinguished into several classes of criteria. These classes are [Spilker 1985]:
1) Characteristics of patients. In this class the characteristics of patients are defined. For example the age and life expectancy of the patient.
2) Characteristics of the disease and its treatment. In this class the characteristics of the patient‘s disease are recorded. For example, does the patient suffer from the disease or in what stage is the disease. Also the characteristics of the tested treatment and exclusion treatment’s are recorded.
3) Environmental and other factors. Sometimes special environment criteria are defined to detect a disease in a certain area. Also ethical criteria fall into this class. For example, has the patient signed the informed consent.
4) Safety criteria. In this class criteria are defined for examinations that are not clinically acceptable.
These classes of criteria are based on several points of interest. Spilker [Spilker 1985] states items that can lead to selection criteria for each class. Table 2 shows these items:
A. Characteristics of patients
1. Gender, e.g. patient should be of gender female. 2. Age, e.g. patient should have age older than 18. 3. Weight
4. Education
5. Race and/or ethic background 6. Social and economic status
7. Pregnancy and lactation, e.g. patient should have no pregnancy. 8. Use of tobacco; ingestion of caffeine and/or alcohol
9. Abuse of alcohol or drugs 10. Diet and nutritional status
11. Physiological limitation and genetic history 12. Surgical, anatomical, and/or emotional limitations
13. Hypersensitivity to a study drug or test 14. Other drug and nondrug allergies
B. Characteristics of the disease and its treatment 1. Disease being evaluated
2. Concomitant drugs
3. Previous drug and nondrug treatment
4. Washout period of nonstudy drugs or nondrug treatments 5. History of other diseases
6. Present clinical status 7. Previous hospilazations C. Environmental and other factors
1. Patient recruitment and co-operation, e.g., patient should have signed informed consent.
2. Participation in another dug study
3. Participation in another part of this study or in any other study using this study drug
4. Institutional or environmental status
5. Occupation, e.g., patient should have occupation doctor.
6. Geographical location, e.g. patient should have residence Holland 7. Litigation and disability
D. Safety criteria
1. Physical examination 2. Clinically acceptability
3. ECG, e.g. Patient should have passed ECG 4. EGG
5. Ophthalmologic and laboratory examinations
Table 2: Items to consider as criteria for patient selection.
Selection criteria are used to select patients for a clinical trial during a patient recruitment period. When selecting patients for a clinical trial, one must have in mind the number of patients that is needed to show the significance of the tested treatment or to reject the treatment based on the results of the clinical trial. This number of patients is called sample size and is computed on base of statistical aspects. There are four factors that play a role in computing the sample size [Collins 1984]:
• The outcome measures.
• Magnitude of clinically important differences between outcome measures.
• Amount of variation in the outcome measures in the study population.
• Drop-out rate, where drop-out rate is defined as the number of study patients who fail to complete the required follow-up for reasons that cannot definitely be attributed to treatment outcome in relation to the number of patients that entered the clinical trial. This drop-out rate is hardly to predict. Reasons for drop-out are usually that a patient’s condition has changed and continuing the trial may jeopardise the patient’s health or the patient does not want to co-operate anymore.
If the selection criteria are too strict, or the sample size is too high, problems can occur in creating the desired patient population and this mostly leads to the problem that fewer patients were recruited in the desired recruitment interval than defined by the sample size.
1) Extending the trial.
2) Adapting the trial protocol. 3) Terminating the trial.
The first method of solving the problem is the least severe. The most common reason a study is extended is that fewer patients enrolled than was expected (Figure 5 and Figure 6] after the patient recruitment period. It is usually necessary only to increase the allowable recruitment time and not to modify the protocol [Spilker 1985, Tu 1993]. This because there are normally enough patients to include in the clinical trial, but the designers underestimated the time needed to find these suitable patients.
When the patient recruitment period is extended and there are still not enough patients recruited the problem is mostly resolved by adapting the study protocol. If patient recruitment went much slower than expected, it is likely that it will be difficult to select enough patients for a trial [Figure 6]. It should be possible to make adjustments to the selection criteria to render patients eligible for the clinical trial. Confirmation of eligibility may require more than one evaluation, due to changes in a patient’s condition. In this situation, identifying an eligible participant for a clinical trial is a dynamic and time-consuming process [Tu 1993].
The third, last and most severe method of resolving the problem is an early termination of the trial. The clinical trial is than terminated due to the small number of patients included in the clinical trial. Sometimes a clinical trial can start with fewer patients than specified by the sample size, but the risk is that this trial will lose its value due to the lacking significance of the results of the clinical trial.
In Figure 4, Figure 5, Figure 6 three illustrations are presented of patient recruitment. These figures are based on the Beta-blocker Heart Attack Trial [Friedman 1985]. In Figure 4 an illustration of a clinical trial is given that was well designed; the patient recruitment went according to the clinical trial protocol. In Figure 6 an illustration is presented of patient recruitment that had a bad start, but after this went above goal ratio and an illustration is shown of a poorly designed patient recruitment.
Figure 5: Patient recruitment in a clinic that started slowly and then performed a greater than
goal rate.
If the chosen selection criteria are too relaxed, the clinical trial will be less reliable and less generalizable due to variation in patient characteristics of the selected patients. Thus, the optimal set of selection criteria is the set of criteria that is as broad as possible to permit adequate enrolment and generalizability, but narrow enough to exclude those who are unlikely to be affected by the intervention.
Summarised, in the development of selection criteria there are thus two extremes [Spilker 1985]. In this section these extremes will be pointed out and their advantages and disadvantages will be displayed. The first extreme is the highly restricted selection criteria.
Advantages:
• This set provides more precise comparison of the test and control treatments.
• The results of the trial are less likely to be effected by the population variability. Disadvantages:
• This set increases cost and time required for patient recruitment.
• This set limits generalizability of the study findings, because there will be a very homogeneous group of the patients and the characteristics of the patients will not much differ.
The other extreme is minimally restrictive selection criteria. Advantages:
• This set makes patient recruitment easier
• This set provides a base for wider generalisation of findings. Disadvantages:
• This set may obscure treatment effects because of variability in composition of the study population.
• The results of a trial may be confusing, especially if an observed effect appears to be associated with a subgroup of patients in the study and the subgroup is too small to yield a reliable treatment comparison.
• Potentially more eligible patients may be overlooked, due to the wide variety in patient characteristics.
In this master thesis a knowledge based system is developed to support the development of selection criteria. These criteria are developed for a first draft protocol. This is done because it is allowed to add selection criteria after the clinical trial protocol has been approved by a committee, so it is recommended to create a set of selection criteria that is not too strict. The user can alter these criteria or add new ones to create a stricter set of selection criteria when the conductor of the clinical trial sees that there are many potential study objects and wants to add criteria to assure a more homogeneous sample population.
2.2
PROSYS-part
The UIC/BME has started the development of a knowledge based information system, called PROSYS (see section 1.2). PROSYS should support the development process of clinical trial protocols to improve the quality of first draft protocols. This should lead to a decrease in time needed to develop such protocols.
PROSYS consists of several partitions called PROSYS-parts. These parts can be seen as a module of PROSYS or as a stand-alone information system. This master thesis will be focussed on the development of the PROSYS-part for the support of the development of the selection criteria. This PROSYS-part will use information to develop selection criteria. This method of information development is best described as a knowledge based information system and thus the PROSYS-part for the support of the development of selection criteria will be a knowledge based information system.
The development of a knowledge based system consists of several stages. First sample knowledge is created and the relations in this sample knowledge must be discovered. According to these relations, a knowledge model is developed to define the structure of these relations. After this the real knowledge must be collected and then this model can be populated using the knowledge from daily practice. After this, the inference mechanism has to be formulated. By using this knowledge model a knowledge base of selection criteria can be build. This database is used by the PROSYS-part for supporting the development of selection criteria, but can also be used by the user. This database is dynamically build due to the fact that each time when selection criteria are constructed, new selection criteria are added. These criteria can be added, altered or removed by the inference engine or the user. This process of building the selection criteria database is graphically illustrated in Figure 7 using Hydra4 . The PROSYS-part for supporting the development of the selection criteria for a specified trial is based on this database and the inference mechanism to develop these criteria. Criteria could be based on the above mentioned classes of criteria [Table 2], on the knowledge that is recorded in the knowledge base or based on inferences of the inference mechanism.
In the next section the knowledge that is needed to support the development of the selection criteria is modelled. This knowledge is modelled using a conceptual modelling scheme that should lead to a knowledge base that should be used by the PROSYS-part for supporting the development of selection criteria. For a more detailed description see appendix D.
4 See Appendix D. = trigger = task A = dataflow B = database
Figure 7: Graphical representation of construction selection criteria
2.3
Knowledge representation - the development of an ontology
In this master thesis the following definitions are used [Webster 1983]:
• Data: Data consist of facts such as words, numbers, etc. Used for reasoning,
discussion or calculation.
• Information: When data is given a certain meaning the data with meaning is called
information. For example, when one concludes from a body temperature of 39 Celsius that a patients has fever, than body temperature of 39 Celsius is called information.
• Knowledge: When information is used to create other information, the information that
was used to create this new information is called knowledge.
A knowledge base is a conceptual model that is populated with knowledge. Such a conceptual model is called an ontology5 . By using an ontology knowledge can be represented in knowledge base [Gruber http]. To develop the knowledge base that is needed by the inference mechanism to construct the selection criteria an ontology is needed to conceptualise the domain knowledge.
Certain decisions have to be made during the development of an ontology. Here, these decisions are based on the following criteria [Mars 1991, d’Hollosy 1995]:
• Expressiveness: It should be possible to represent all possible knowledge that is
needed in the application domain.
• Economy: It should be possible to represent all possible knowledge with as few as
possible concept classes and relations.
• Efficiency: It should be possible to perform the inference rules on the knowledge as
efficient as possible.
5
The term ontology is borrowed from philosophy, in which it refers to the subject of existence. In Artificial Intelligence the term ontology is a description (like a formal specification of a program) of concept classes and relation classes that are used to conceptualise knowledge [Gruber 1993-2].
Developing selection criteria
Knowledge Base User PROSYS-part Inference MechanismInference Mechanism
• Flexibility: It should be possible to add, modify and remove knowledge easily.
• Uniformity: The naming of concept classes should join the common terminology of the
application domain.
An ontology defines the structure of knowledge. Such a structure is defined by defining concept classes and relation classes between these concept classes. This structure can graphically be represented using a modelling scheme. One can take for example a PSM-scheme6 for representing an ontology.
The base for the ontology of this application is the ontology as developed by [d’Hollosy 1995]. This ontology has an overlap with the construction of selection criteria due to the fact that the ontology of [d’Hollosy 1995] already covers concept classes as State and Treatment Method. So the decision is made to adapt this ontology to a knowledge model for the development of selection criteria. This basic and adapted ontology will be modelled using PSM. In PSM the concept classes are called entity types and the relation classes are called fact types. In Figure 8 the ontology developed by [d’Hollosy 1995] is modelled. For the a formal description of this ontology see Appendix B.
6
Figure 8: Ontology as developed by [d’Hollosy 1995]. Anatomical Referent (AR-name) comprises being_part_of PO1 being_kind_of being_generalisation_of KO1 having_function_towards occurring_in HFT2 Material KO2 being_generalisation_of being_kind_of PO4 being_part_of comprises Treatment Method (TM-name) Evaluation Method (EM-name) Instrument (IN-name) Variabele (VAR-name) State (ST-name) being_kind_of KO3 being_generalisation_of being_kind_of KO5 being_generalisation_of being_kind_of KO7 being_generalisation_of being_kind_of KO4 being_generalisation_of being_kind_of KO6 being_generalisation_of PO3 comprises being_part_of PO5 comprises being_part_of PO2 comprises being_part_of HFT1 having_function_towards occurring_in US1 uses being_used_by US2 uses being_used_by US3 uses being_used_by US4 uses being_used_by CS1 causes being_caused_by CS2 causes being_caused_by CS3 being_caused_by causes CS4 causes being_caused_by being_caused_by causes CS5 EB1 evaluates being_evaluated_by EB2 evaluates being_evaluated_by HV1 having_variable being_variable_of FI2 can_be_found_in_state being_state of HV2 having_variable being_variable_of FI1 can_be_found_in_state being_state_of DB1 defines being_defined_by DB2 being_defined_by defines HV3 being_variable_of having_variable
2.3.1
An extended ontology to model domain knowledge needed for
the development of selection criteria
In Figure 8, the ontology as developed by [d’Hollosy 1995] is presented. This model will be extended now. To model knowledge on selection criteria, several things like characteristics of a disease, or the study type of a clinical trial should be known, because certain selection criteria depend of this information (see 2.1). To model this knowledge 6 new concept classes and 7 new relation classes are added to the basic ontology.
The 6 new concept classes are:
Concept class: Study Type (STP)
Description: For the development of the selection criteria it is necessary to know for what phase the clinical trial is. This is always one of the following phases: Phase I, Phase II, Phase III or Phase IV.
Examples: In Phase I the first experiments in human subjects are primarily concerned with drug safety, not efficacy, and are usually performed on volunteers, so selection criteria on disease characteristics are mostly not needed here. After studies in normal volunteers, the initial trials in patients will also be of the Phase I type [Pocock 1983]. Argumentation: The reason of adding the concept class ‘Study Type’ is that when
conducting a clinical trial the selection criteria depend on the study type. In a Phase I clinical trial there are normally only safety criteria, because a new treatment is tested on volunteers and the aim of the study is not to establish efficacy, but to test the safety of this new treatment. Thus, the study type influences the construction of the selection criteria and is therefore added to the knowledge model.
Concept class: Study Objective (SO)
Description: One needs to know which hypothesis has to be proven to support the development of selection criteria. Selection criteria will depend on this hypothesis.
Examples: If the study objective is to observe a certain disease in children, then a logical selection criterion would be ‘age < 18’.
Argumentation: A clinical trial is created according to the study objective that states the hypothesis that one wants to prove when conducting the trial. The construction of the selection criteria is aimed at creating a homogeneous group of patients that is suitable to test the hypothesis. Thus the study objective influences the construction of the selection criteria and is therefore added to the knowledge model.
Concept class: Selection Criteria
Description: Selection criteria are used to include or exclude a potential trial subject in or from a clinical trial. As mentioned above there are four different classes of selection criteria (see Table 2). These classes are subclasses of the concept class Selection Criteria and will be modelled accordingly; patient characteristics criteria (PC), disease
characteristics criteria (DC), Environment characteristics criteria (EC) and Safety criteria (SF). These Selection Criteria can be
designed, altered or removed by the inference mechanism.
Examples: Age > 18 (PC), has fever (DC), lives in Holland (EC), has signed informed consent (EC), absent deep tendon reflexes (SF).
Argumentation: The reason of adding this concept class is that when selecting patients for a clinical trial, this is done by means of selection criteria. These selection criteria are developed by the inference engine or the user, thus there must be a concept class to store these developed selection criteria in.
Concept class: Selection Criteria Set
Description: This class is the super class of the concept class Selection Criteria. This class contains (non empty) sets of selection criteria that are used in a protocol.
Examples: {‘age > 18’, ‘Age < 65’, ‘signed informed consent’, ‘proven Renal Cell Carcinoma’}
Argumentation: Selection Criteria are used to include or exclude a patient in a clinical trial. A part of the protocol is the patient recruitment. Patients are recruited by matching their characteristics to a specified set of selection criteria that should guarantee the safety of the patients and a sample population that reflects the hypothesis being tested.
Concept class: Value (VAL)
Description: This concept class is only connected to the concept class Variable. Each variable must have a value connected to that variable. There are three kinds of values: numbers, dates and text. Boolean values can be represented using zero for false and for true. To support this, the concept class Value consists of three subclasses: Text (String),
Date-code (Date) and Number (Nr).
Examples: Age numbers (Nr). Occupation text (String), birth date (Date). Argumentation: When dealing with variables, one must have in mind that a variable
can have different values, therefore the concept class Variable must be connected with a class of values called Value.
Concept class: Operator (OP)
Description: This concept class is only connected to the concept class Variable. Each variable must have a value connected to that variable. To represent something like: age > 18, the concept class Operator is introduced.
Examples: >, <.
Argumentation: When dealing with variables, one must have in mind that a variable is often a limit and therefore the variable must be attached to some kind of operator.
There are also seven new relation classes. These relation classes are needed to represent the relations between the concept classes.
Relation class: influences (INF)
Explanation: The way in which a study objective is created is influenced by the study type. In the reasoning process this study type must be taken into account
when creating the selection that are criteria based on the study objective.
Relations:
Study Type influences Study Objective
e.g., Study Type ‘Phase I’ influences Study Objective ‘Safety of paracetamol’.
Relation class: having value (HVAL)
Explanation: The concept class Value was developed to provide values to the concept class Variable. The relation class having value connects a variable with its associated value.
Relations:
Variable having value Value e.g., Variable ‘Gender’ having operator ‘=’, having value Value ‘Female’.
Relation class: is subject of (ISO)
Explanation: The study objective is a sentence that can be divided in several points of interest. This deviation is done by attaching subjects to the study objective. These subjects are called points of interest. To connect these points of interest with its associated study objective, the relation class is subject of is developed.
Relations:
State is subject of Study Objective
e.g., State ‘Renal Cell Carcinoma’ being subject of Study Objective ‘Evaluating quality of life having Renal Cell Carcinoma’.
Treatment Method is subject of Study Objective
e.g., Treatment Method ‘surgical resection’ being subject of Study Objective ‘Evaluating the quality of life after having treated Renal Cell Carcinoma with surgical resection’. Variable is subject of Study
Objective
e.g., Variable ‘quality of life’ being subject of Study Objective ‘Evaluating quality of life having Renal Cell Carcinoma’.
Relation class: is restriction of (RVAR)
Explanation: The study objective is a sentence that can be divided in several points of interest. This deviation is done by attaching subjects to the study objective. One of these subjects can also be a restriction on the population, for example age. This relation class is added to represent these restrictions..
Relations:
Having Value is restriction of Study Objective
e.g., Having Value (Variable ‘Age’, Operator ‘>’, Value ‘18’) being_restriction_of Study Objective ‘Evaluating quality of life of adults having Renal Cell Carcinoma’.
Relation class: history brought forward (HBF)
Explanation: In the model for developing selection criteria, a primitive history function has been built in. When the user specifies an additional criterion for a disease or treatment, this criterion is stored by the relation class history brought forward. To relate these criteria to the specified disease or treatment, the related disease or treatment must be taken into account and thus creating a tertiary relation class is necessary. So all the specified criteria by the user are defined in this class.
Relations:
State history brought forward Disease Characteristics
e.g., State ‘Renal Cell Carcinoma’ being subject of State ‘Fever’ history bringing forward Disease Characteristics ‘Patient should have Fever’. When developing criteria for a state Renal Cell Carcinoma it was previously defined that state fever should be present, so the criteria ‘Patient should have Fever’ should be added.
Treatment Method history brought forward Disease Characteristics
e.g., Treatment Method ‘surgical resection’ being subject of Treatment Method ‘chemotherapy’ history bringing forward Disease Characteristics ‘Patient should have chemotherapy’. When developing criteria for a treatment surgical resection it was previously defined that treatment chemotherapy should have been performed on the patient, so the criteria ‘Patient should have chemotherapy’ is added.
Relation class: brought forward by (BF)
Explanation: To develop selection criteria there has to be a connection between a class of selection criteria and the concept on which this criteria is based. This connection is established by introducing the relation brought forward by. In this class all the criteria which were developed by the inference engine are stored.
Relations:
Disease Characteristics brought forward by State
e.g., Disease Characteristics ‘Patient should have Renal Cell Carcinoma’ brought forward by State ‘Renal Cell Carcinoma’.
Disease Characteristics brought forward by Treatment Method
e.g., Disease Characteristics ‘Patient should have been medicated using surgical resection’ brought forward by Treatment Method ‘surgical resection’.
Disease Characteristics brought forward by Having Value
e.g., Fever is defined by body temperature greater than 37°. Thus Disease Characteristics ‘Patient should have body temperature >
37°’ brought_forward_by Having Value (Variable ‘body temperature’, Operator ‘>’, Value ‘37°)’.
Disease Characteristics brought forward by Evaluation Method
e.g., Disease Characteristics ‘Patient should have been examined using no x-ray’s’ brought_forward_by Evaluation Method ‘x-ray’s’.
Patient Characteristics brought forward by Having Value
e.g., Patient Characteristics ‘Patient should be of gender female’ brought forward by Having Value (Variable ‘gender’, Operator ‘=’, Value ‘female’).
Patient Characteristics brought forward by Material
e.g., Patient Characteristics ‘Patient should be of no hypersensitive against B’ brought forward by Material ‘B’.
Environment Criteria brought forward by Having Value
e.g., Environment Characteristics ‘Patient should have residence Holland’ brought forward by Having Value (Variable ‘Residence’, Operator ‘=’, Value ‘Holland’).
Safety Criteria brought forward by Having Value
e.g., Safety Criteria ‘Patient should have deep tendon reflexes present’ brought forward by Having Value (Variable ‘deep tendon reflexes’, Operator ‘=’, Value ‘present’).
Relation class: is exclusion of (EX)
Explanation: To develop criteria based on an exclusion treatment or state, an exclusion should be introduced. The decision was made to create a relation class just for exclusions instead of creating a concept class. The advantage of this approach is that it is simple to model criteria based on exclusion states or treatments.
Relations:
State is exclusion of Study Objective
e.g., State ‘Fever’ being exclusion of Study Objective ‘Renal Cell Carcinoma and not having fever’.
Treatment Method is exclusion of Study Objective
e.g., Treatment Method ‘chemotherapy’ being exclusion of Study Objective ‘Patients being treated with surgical resection and not having undergone chemotherapy’.
Treatment Method is exclusion of Treatment Method
e.g., Treatment Method ‘chemotherapy’ being exclusion of Treatment Method ‘radiation’. State is exclusion of Treatment
Method
e.g., State ‘Fever’ being exclusion of Treatment Method ‘surgical resection’.
Also a new relation to an already existing relation class is added:
Relation class: is part of (PO)
Relations:
Selection Criteria is part of Selection Criteria Set
e.g., Selection Criteria ‘PC1’ is part of Selection Criteria Set {‘PC1’, ’DC1’, ‘DC2’}.
Due to the complexity of the extension, this extension will be presented in three phases:
• First, the concept class Study Objective will be explained and the relation between Study Objective and other concept classes will be explained. After this, an example of a population for this model will be given.
• Secondly, the subclasses of the concept class Selection Criteria are explained and the relation between these subclasses and other concept classes will be explained. These models will present a model of constructing the selection criteria for that subclass. After this, example populations for these models are given.
• The concept classes Study Type and Value are integrated in the concept classes Study Objective and Selection Criteria and will not be modelled independently. The concept class Study Objective and the subclasses of the concept class Selection Criteria have been explained and an overview of the complete extension will be given.
Construction of the concept class Study Objective:
In considering study objectives, the study objective can be divided in several points of interest. For example:
Comparing the efficacy of a treatment for headaches ,not having migraine, using paracetamol or a placebo drug A on female humans younger than 18 in a clinical Phase III trial.
This objective can be divided into the following points of interest:
Treatment: Treatment Method ‘Paracetamol’
Treatment Method ‘A’
State: State ‘Headache’
no State ‘Migraine’
Study Type: Study Type ‘Phase III’
Variable to be measured: Variable ‘Efficacy’
Target: Variable ‘Gender’ having Value ‘female’
Variable ‘Age’ having Value < ‘18’
Table 3: An example study objective points of interest.
For the construction of the selection criteria it is not necessary to know the relation between the different points of interest of the study objective, because the criteria are based on the treatment methods and disease used for the study objective. These points cannot be derived automatically, due to the lacking technology of natural language recognition. Thus, these points of interest have to be supplied by the user or by another PROSYS-part. In Table 3 points of interest to the study objective of an example trial are mentioned. According to this table, a study objective can be divided in several parts. Now these several parts and the study objective will be modelled using PSM and an example population will be given.
Figure 9: PSM model of the study objective.
As an example the following study objective used in Table 3 is used (SO for short). This study objective could be defined in terms of the PSM model and leads to the following population of the PSM model:
Pop(State) = ‘Headache’
‘Migraine’
Pop(Study Objective) = ‘SO’
Pop(Treatment Method) = ‘Paracetamol’ ‘A’
Pop(Study Type) = ‘Phase III’
Pop(Variable) = ‘Age’ ‘Gender’ Pop(Value) = ‘18’ ‘female’ Pop(Operator) = < =
Pop(ISO1) = applying_to being_subject_of
‘SO’ ‘Headache’
Pop(ISO2) = applying_to being_subject_of
‘SO’ ‘Paracetamol’ Study Type (STP-name) Value State (ST-name) Variable (VAR-name) Treatment Method (TM-name) Study Objective (SO-name) INF ISO3 HVAL ISO2 ISO1 applying_to being_subject_of influences being_influenced_by applying_to applying_to having_value being_value_of being_subject_of being_subject_of EX2 applying_to_exclusion being_exclusion_of EX1 applying_to_exclusion being_exclusion_of Operator (OP-name) being_operator_of RVAR with_restriction being_restriction_of
‘SO’ ‘A’
Pop(ISO3) = applying_to being_subject_of
‘SO’ ‘Efficacy’ Pop (RVAR) = with_ restriction being_restriction_of
having_value being_operator_of being_value_of
‘SO’ ‘Gender’ = ‘female’
‘SO’ ‘Age’ < ‘18’
Pop(HVAL) = having_value being_operator_of being_value_of
‘Gender’ = ‘female’
‘Age’ < ‘18’
Pop(INF) = being_influenced_by influences
‘SO’ ‘Phase III’
Pop(EX1) = ∅
Pop(EX2) = with_exclusion being_exclusion_of
‘SO’ ‘Migraine’
Now, by means of a formal model, the text of a study objective can be transformed into formal parts. In the according PROSYS-part the information on the treatment methods, states and variables should be obtained from another PROSYS-parts or from the user.
Construction of the concept class Selection Criteria:
Table 2 showed the items that could lead to selection criteria for each class. These classes were [Spilker 1985]:
1) Characteristics of patients.
2) Characteristics of the disease and its treatment. 3) Environmental and other factors.
4) Safety criteria.
These classes are described independently due to differences in developing selection criteria for each class.
When dealing with knowledge based systems it is useful when the system uses knowledge that was defined in the past. When a criterion is defined by a user, this criterion is linked to a state or treatment. When in another objective this state or treatment is used, this criterion will be added automatically.
If a knowledge based system uses a kind of history function, the proposed selection criteria would be better in the future due to a kind of learning function. This function should record criteria that were defined by the user and these criteria should be evaluated after the protocol has been approved by a committee. The criteria that were actually used should than be recorded
for future use. The model for developing selection criteria should thus be able to record some kind of history of old selection criteria. To provide knowledge that is necessary to implement such a kind of history, the user will be able to add criteria. The user is able to add all kinds of criteria. When a future objective is based on the same treatment or state, these defined criteria could then be used.
Construction of the concept class Patient Characteristics:
In the concept class Patient Characteristics selection criteria are developed based on the characteristics of patients that one wants to include in the clinical trial. From the study objective mentioned in Table 3, the following points of interest to the patient characteristics can be concluded:
• Variable ‘Gender’ having Value ‘female’ (PC1).
• Variable ‘Age’ having Value < ‘18’ (PC2).
• Treatment Method ‘A’ uses Material ‘B’ (PC3).
These points of interest should lead to the following patient characteristics criteria:
• Patient should be of gender female.
• Patient should be of age < 18.
• Patient should be of not hypersensitive against B.
According to the mentioned example, the patient characteristics criteria are based on the following points on interest:
• Variable: If a point of interest is Age greater than 18, then this leads to the criteria: Age greater than 18.
• Material: A point of interest to the study objective is the treatment method that is going to be evaluated. In most case a treatment method uses materials. If the patient has hypersensitivity towards this material he should be excluded from the trial because the results of the screenings could be inaccurate, or the patient's health could be in jeopardy.
Figure 10: PSM model of the Patient Characteristics.
Now the PSM model of the patient characteristics criteria will be populated using the following patient characteristics criteria PC1, PC2 and PC3 and the study objective used in Table 3 and the extra information that a treatment A uses material B:
Pop(Material) = ‘B’
Pop(Study Objective) = ‘SO’ Pop(Treatment Method) = ‘A’
Pop(Variable) = ‘Age’ ‘Gender Pop(Value) = ‘18’ ‘female’ Pop(Patient Characteristics) = ‘PC1’ Study Objective (SO-name) Patients Characteristics (PC-name) Material (MA-name) Treatment Method (TM-name) Value Variable (VAR-name)
having_value HVAL being_value_of
BF4 brought_forward_by bringing_forward BF3 brought_forward_by bringing_forward ISO2 US3 uses being_used_by being_subject_of applying_to ISO3 being_subject_of applying_to Operator (OP-name) being_operator_of EX2 being_exclusion_of with_exclusion
‘PC2’ ‘PC3’
Pop(Operator) = <
=
Pop(ISO2) = applying_to being_subject_of
‘SO’ ‘A’
Pop(ISO3) = applying_to being_subject_of
‘SO’ ‘Gender’
‘SO’ ‘Age’
Pop(US3) = uses being_used_by
‘A’ ‘B’
Pop(HVAL) = having_value being_operator_of being_value_of
‘Gender’ = ‘female’
‘Age’ < ‘18’
Pop(EX2) = with_exclusion being_exclusion_of
‘SO’ ‘Migraine’
Pop(BF3) = brought_ forward_
by
bringing_forward
having_value being_operator_of being_value_of
‘PC1’ ‘Gender’ = ‘female’
‘PC2’ ‘Age’ < ‘18’
Pop(BF4) = brought_forward_by bringing_forward
‘PC3’ ‘B’
According to this example, it is possible to formally represent patient characteristics criteria base on the points of interest defined by the study objective.
Construction of the concept class Disease Characteristics:
In the concept class Disease Characteristics selection criteria are constructed based on the disease for which the treatment is intended. This disease is also a point of interest of the study objective.
For example the protocol described in [Win 122] uses the following disease characteristics criteria:
• Histologically proven Renal Cell Carcinoma.
• Patients should have undergone surgical resection of the primary tumour and lymph nodes.
• No unstable angina pectoris or recent (6 months) myocardial infarction.
• No patients with signs or symptoms of systemic metastatic Renal Cell Carcinoma.
• No patients who require or are likely to require corticosteriods for intercurrent disease.
• No patients who receive radiation or chemotherapy.
From these disease characteristics criteria, the following points of interest can be concluded:
• State ‘Renal Cell Carcinoma’, no ‘systemic metastatic Renal Cell Carcinoma’, no ‘macroscopic residual disease’, no ‘unstable angina pectoris’, no ‘recent (6 months) myocardial infarction’.
• Treatment Method ‘surgical resection’, no ‘corticoids’, no ‘radiation’, no ‘chemotherapy’.
These points of interest could then be written as disease characteristics criteria using standard sentences:
• Patient should have Renal Cell Carcinoma (DC1).
• Patient should have no systemic metastatic Renal Cell Carcinoma (DC2).
• Patient should have no macroscopic residual disease (DC3).
• Patient should have no unstable angina pectoris (DC4).
• Patient should have no recent (6 months) myocardial infarction (DC5).
• Patient should have been medicated using surgical resection (DC6).
• Patient should have been medicated using no corticoids (DC7).
• Patient should have been medicated using no radiation (DC8).
• Patient should have been medicated using no chemotherapy (DC9)
According to this example, the selection criteria for the concept class Disease Characteristics are based on the following points of interest:
• Treatment: This point of interest covers the treatment at which the clinical trial is aimed. Exclusion treatments could be defined as Study Objective with exclusion Treatment ‘A’. This could lead to automatic development of exclusion criteria for treatments that could influence the result of the clinical trial.
• State: The disease that is being evaluated by conducting a clinical trial influences the development of Disease Characteristics criteria. Here could also exclusion states be defined as Study Objective with exclusion State ‘State C’.
Figure 11: PSM model of the Disease Characteristics criteria.
Now the PSM model of the disease characteristics criteria will be populated using the above mentioned study objective and disease characteristics criteria.
Pop(Study Objective) = ‘SO’
Pop(Treatment Method) = ‘surgical resection’
‘corticoids’ ‘radiation’ ‘chemotherapy’
Pop(State) = ‘Renal Cell Carcinoma’
‘systemic metastatic Renal Cell Carcinoma’
‘macroscopic residual disease’ ‘unstable angina pectoris’
‘recent (6 months) myocardial infarction’ Pop(Disease Characteristics) = ‘DC1’ ‘DC2’ ‘DC3’ ‘DC4’ ‘DC5’ ‘DC6’ ‘DC6’ ‘DC7’ ‘DC8’ ‘DC9’
Pop(ISO1) = applying_to being_subject_of
Disease Characteristics (DC-name) Study Objective (SO-name) State (ST-name) Treatment Method (TM-name) brought_forward_by brought_forward_by BF1 BF2 ISO1 ISO2 bringing_forward bringing_forward applying_to applying_to being_subject_of being_subject_of with_exclusion being_exclusion_of EX2 with_exclusion being_exclusion_of EX1
‘SO’ ‘Renal Cell Carcinoma’
Pop(ISO2) = applying_to being_subject_of
‘SO’ ‘surgical resection’
Pop(EX1) = with_exclusion being_exclusion_of
‘SO’ ‘systemic metastatic Renal Cell Carcinoma’
‘SO’ ‘macroscopic residual disease’
‘SO’ ‘unstable angina pectoris’
‘SO’ ‘recent (6 months) myocardial infarction’
Pop(EX2) = with_exclusion being_exclusion_of
‘SO’ ‘corticoids’
‘SO’ ‘radiation’
‘SO’ ‘chemotherapy’
Pop(BF1) = brought_forward_by bringing_forward
‘DC1’ ‘Renal Cell Carcinoma’
‘DC2’ ‘systemic metastatic Renal Cell Carcinoma’
‘DC3’ ‘macroscopic residual disease’
‘DC4’ ‘unstable angina pectoris’
‘DC5’ ‘recent (6 months) myocardial infarction’
Pop(BF2) = brought_forward_by bringing_forward
‘DC6’ ‘surgical resection’
‘DC7’ ‘corticoids’
‘DC8’ ‘radiation’
‘DC9’ ‘chemotherapy’
This example and the PSM model for the development of the disease characteristics criteria illustrate how the PSM model can be populated using disease characteristics criteria.
Construction of the concept class Environment Characteristics:
In the concept class Environment Characteristics, selection criteria are developed based on the environmental factors of patients that can influence the results of the clinical trial. These environment characteristics are often only defined by an informed consent, because usually the environment of the patient is not of concern to the study objective, because in most cases the trial is intended to test a treatment and not to discover the background of the disease.
The following points of interest to the environment characteristics can be concluded:
• Variable ‘Informed Consent’ having Value ‘signed’ (EC1).
• Variable ‘Residence’ having Value ‘Holland’ (EC2).
These points of interest should lead to the following environment characteristics criteria:
• Patient should have Informed Consent signed,
• Patient should have residence Holland.
• Patient should have occupation doctor.
According to the mentioned example, the environment characteristics criteria are based on the following point of interest:
• Variable: Environment characteristics can be seen as a variable having a certain value. If the study objective is to research cancer in Holland than an environment characteristics criterion would be: Patient having residence Holland.
Figure 12: PSM model of the Environment Characteristics and Safety Criteria.
Now the PSM model of the environment characteristics criteria will be populated using the study objective described in Table 3 and the environment characteristics criteria:
Pop(Study Objective) = ‘SO’
Pop(Variable) = ‘Informed Consent’
‘occupation’ ‘residence’ Pop(Value) = ‘Signed’ ‘doctor’ ‘Holland’ Pop(Operator) = =
Pop(Environment Characteristics) = ‘EC1’ ‘EC2’ Environment Characteristics (EC-name) Variable (VAR-name) Value Study Objective (SO-name) BF5 brought_forward_by bringing_forward ISO3 applying_to being_subject_of HVAL having_value being_value_of Safety Criteria (SF-name) BF6 being_restriction_of bringing_forward Operator (OP-name) being_operator_of RVAR with_restriction bringing_forward
‘EC3’ Pop(Safety Criteria) = Pop(ISO3) = Pop (RVAR) = with_ restriction being_restriction_of
having_value being_operator_of being_value_of
‘SO’ ‘Informed
consent’
= ‘signed’
‘SO’ ‘occupation’ = ‘doctor’
‘SO’ ‘residence’ = ‘Holland’
Pop(HVAL) = having_value being_operator_of being_value_of
‘Informed consent’ = ‘signed’
‘occupation’ = ‘doctor’ ‘residence’ = ‘Holland’ Pop (BF5) = brought_for ward_by bringing_forward
having_value being_operator_of being_value_of
‘SO’ ‘Informed
consent’
= ‘signed’
‘SO’ ‘occupation’ = ‘doctor’
‘SO’ ‘residence’ = ‘Holland’
According to this example, it is possible to formally represent environment characteristics criteria based on the points of interest defined by the study objective.
Construction of the concept class Safety Criteria:
Most patients receive a physical examination prior to entry into a clinical study [Spilker 1985]. The selection criteria may list specific findings that are not acceptable for entry, e.g. the patient's health could be jeopardised by participating in the trial. For example, the following safety criteria can be used:
• Variable ‘deep tendon reflexes’ having Value ‘present’ (SF1).
• Variable ‘EGG’ having Value ‘normal’ (SF2).
These points of interest should lead to the following safety criteria:
• Patient should have deep tendon reflexes present.
• Patient should have EGG normal.
According to this example, the safety criteria are based on variables having a certain value or a certain range. Figure 12 shows the PSM model for the development of the safety criteria.
Now the PSM model of the safety criteria will be populated using the study objective used in Table 3 and the safety criteria:
Pop(Study Objective) = ‘SO’
Pop(Variable) = ‘deep tendon reflexes’ ‘EEG’ Pop(Value) = ‘present’ ‘yes’ Pop(Operator) = = Pop(Environment Characteristics) = Pop(Safety Criteria) = ‘SF1’ ‘SF2’
Pop(ISO3) = applying_to being_subject_of
‘SO’ ‘deep tendon reflexes’
‘SO’ ‘EEG’ Pop (RVAR) = with_ restriction being_restriction_of
having_value being_operator_of being_value_of
‘SO’ ‘Informed
consent’
= ‘signed’
‘SO’ ‘occupation’ = ‘doctor’
‘SO’ ‘residence’ = ‘Holland’
Pop(HVAL) = having_value being_operator_of being_value_of
‘deep tendon reflexes’ = ‘present’
‘EEG’ = ‘passed’
Pop(BF6) = brought_forward_by bringing_forward
‘SF1’ ‘deep tendon reflexes’
‘SF2’ ‘EEG’
According to this example, it is possible to formally represent safety criteria based on the points of interest defined by the study objective.
A model for developing selection criteria
In the previous paragraphs, models have been presented for each subclass of the concept class Selection Criteria. In this paragraph these models will be combined and the history function will be added to these models. Figure 13 shows a model for the development of selection criteria. This model has the following capabilities:
• To store selection criteria of any of the described selection criteria classes [Table 2].
• To store disease character