Patient Selection in Clinical Trials

Transcription

1 by Arno Klaassen August 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

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3 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). Keywords: Clinical trial, subject selection, study objective, urology 2

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5 Table of Contents LIST OF FIGURES... 6 LIST OF TABLES INTRODUCTION CLINICAL TRIALS - CURRENT PROBLEMS IN PROTOCOL DESIGN Clinical trials Phases of clinical trials Clinical trial protocols Problems in protocol design PROTOCOL DESIGN SYSTEM (PROSYS) PROJECT DEFINITION SUMMARY DETERMINATION OF THE INCLUSION AND EXCLUSION CRITERIA FOR A NEW TRIAL INTRODUCTION PROSYS-PART KNOWLEDGE REPRESENTATION - THE DEVELOPMENT OF AN ONTOLOGY An extended ontology to model domain knowledge needed for the development of selection criteria AN INFERENCE MECHANISM FOR DEVELOPING SELECTION CRITERIA Preparation for the inference mechanism Step 1: Deriving criteria based on the Study Objective Step 2: Developing criteria based on inference steps Step 3: Trying to guarantee completeness of selection criteria SUMMARY SCDS: A SYSTEM FOR DEVELOPING SELECTION CRITERIA DATABASE DESIGN THE INFERENCE MECHANISM EVALUATION ACCEPTANCE TEST SUMMARY DISCUSSION AND CONCLUSION APPENDIX A: PSM APPENDIX B: BASIC ONTOLOGY FOR A CLINICAL TRIAL APPENDIX C: PERFORMANCE STATUS CRITERIA APPENDIX D: HYDRA APPENDIX E: LISA-D APPENDIX F: INITIAL POPULATION FOR THE INFERENCE MECHANISM GLOSSARY REFERENCES

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7 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 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 Figure 7: Graphical representation of construction selection criteria...23 Figure 8: Ontology as developed by [d Hollosy 1995] Figure 9: PSM model of the study objective...32 Figure 10: PSM model of the Patient Characteristics Figure 11: PSM model of the Disease Characteristics criteria Figure 12: PSM model of the Environment Characteristics and Safety Criteria...40 Figure 13: PSM model for the development of selection criteria Figure 14: Hydra syntax

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9 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 Table 4: ECOG performance status...70 Table 5: Karnofsky performance status

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11 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 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] 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: Phase 2: Phase 3: 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. 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. 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 10

12 Phase 4: 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]. 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 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 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. 11

13 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 EORTC 1 cancer clinical trial protocols [Staquet 1980], the guidelines of the EEC 2 [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 European Economic Community. 12

14 User Other PROSYS-part PROSYS-part User Other PROSYSpart Other PROSYSpart Figure 1: Information flow diagram Introduction Miscellaneous Study objectives Administration Subject selection PROTOCOL Statistical aspects Ethical aspects Evaluation Study design Treatment(s) 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 13

15 Information topic Treatment(s) Evaluation Statistical aspects Administration Miscellaneous Information blocks 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) 16. Study variables and measuring methods 17. Measurement schedule 18. Forms and procedures for data collection 19. Statistical method 20. Significance level 21. Sample size 22. Study duration 23. Randomisation method 24. Stratification method 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)). 28. Quality control 29. Additional information (e.g., finance, insurance) 30. References Table 1: The information items in a clinical trial protocol 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 Figure 3: Partial representation of dependencies 14

16 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]. 15

17 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 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 See appendix C 16

18 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 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 17

19 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. This problem can be resolved in three ways: 18

20 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 4: Patient recruitment in a clinic that consistently performed at goal rate. 19

21 Figure 5: Patient recruitment in a clinic that started slowly and then performed a greater than goal rate. Figure 6: Patient recruitment in a clinic that performed poorly. 20

22 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. 21

23 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 Hydra 4. The PROSYSpart 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. = trigger = dataflow A = task B = database 4 See Appendix D. 22

24 Developing selection criteria PROSYS-part User Knowledge Base Inference Mechanism 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 ontology 5. 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 ]. 23

25 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-scheme 6 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 PSM (Predicator set model) is an extension of NIAM. For more information see Appendix A. 24

26 PO1 being_part_of comprises being_kind_of KO1 Anatomical Referent (AR-name) being_generalisation_of having_function_towards occurring_in comprises being_part_of HFT2 having_function_towards occurring_in PO4 being_kind_of HFT1 Material KO2 being_generalisation_of can_be_found_in_state FI1 being_state_of being_kind_of uses US3 having_variable being_used_by being_part_of PO3 comprises being_used_by KO3 being_generalisation_of Treatment Method (TM-name) HV1 being_variable_of having_variable HV2 US4 uses being_part_of comprises being_variable_of uses US1 being_used_by PO5 causes Evaluation Method (EM-name) being_kind_of KO4 being_kind_of KO5 being_generalisation_of Instrument (IN-name) CS2 being_used_by being_caused_by uses US2 being_caused_by can_be_found_in_state CS1 FI2 causes being_state of being_part_of PO2 comprises being_generalisation_of evaluates EB1 being_evaluated_by causes being_caused_by CS3 causes CS4 being_evaluated_by being_defined_by being_caused_by EB2 evaluates DB2 defines being_kind_of causes being_defined_by defines KO7 being_generalisation_of State (ST-name) CS5 being_caused_by having_variable DB1 being_variable_of Variabele (VAR-name) being_kind_of KO6 being_generalisation_of HV3 Figure 8: Ontology as developed by [d Hollosy 1995]. 25

27 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: Description: Examples: Argumentation: Study Type (STP) 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. 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]. 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: Description: Examples: Argumentation: Study Objective (SO) One needs to know which hypothesis has to be proven to support the development of selection criteria. Selection criteria will depend on this hypothesis. If the study objective is to observe a certain disease in children, then a logical selection criterion would be age < 18. 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: Description: Selection Criteria 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 26

28 Examples: Argumentation: Concept class: Description: Examples: Argumentation: Concept class: Description: Examples: Argumentation: designed, altered or removed by the inference mechanism. Age > 18 (PC), has fever (DC), lives in Holland (EC), has signed informed consent (EC), absent deep tendon reflexes (SF). 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. Selection Criteria Set 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. { age > 18, Age < 65, signed informed consent, proven Renal Cell Carcinoma } 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. Value (VAL) 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). Age numbers (Nr). Occupation text (String), birth date (Date). 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: Explanation: influences (INF) 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 27

29 Relations: Study Type influences Study Objective when creating the selection that are criteria based on the study objective. e.g., Study Type Phase I influences Study Objective Safety of paracetamol. Relation class: Explanation: having value (HVAL) 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: Explanation: Relations: State is subject of Study Objective Treatment Method is subject of Study Objective Variable is subject of Study Objective is subject of (ISO) 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. e.g., State Renal Cell Carcinoma being subject of Study Objective Evaluating quality of life having Renal Cell Carcinoma. 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. e.g., Variable quality of life being subject of Study Objective Evaluating quality of life having Renal Cell Carcinoma. Relation class: Explanation: Relations: Having Value is restriction of Study Objective is restriction of (RVAR) 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.. e.g., Having Value (Variable Age, Operator >, Value 18 ) being_restriction_of Study Objective Evaluating quality of life of adults having Renal Cell Carcinoma. 28

30 Relation class: Explanation: Relations: State history brought forward Disease Characteristics Treatment Method history brought forward Disease Characteristics history brought forward (HBF) 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. 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. 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: Explanation: Relations: Disease Characteristics brought forward by State Disease Characteristics brought forward by Treatment Method Disease Characteristics brought forward by Having Value brought forward by (BF) 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. e.g., Disease Characteristics Patient should have Renal Cell Carcinoma brought forward by State Renal Cell Carcinoma. e.g., Disease Characteristics Patient should have been medicated using surgical resection brought forward by Treatment Method surgical resection. e.g., Fever is defined by body temperature greater than 37. Thus Disease Characteristics Patient should have body temperature > 29

31 Disease Characteristics brought forward by Evaluation Method Patient Characteristics brought forward by Having Value Patient Characteristics brought forward by Material Environment Criteria brought forward by Having Value Safety Criteria brought forward by Having Value 37 brought_forward_by Having Value (Variable body temperature, Operator >, Value 37 ). e.g., Disease Characteristics Patient should have been examined using no x-ray s brought_forward_by Evaluation Method xray s. e.g., Patient Characteristics Patient should be of gender female brought forward by Having Value (Variable gender, Operator =, Value female ). e.g., Patient Characteristics Patient should be of no hypersensitive against B brought forward by Material B. e.g., Environment Characteristics Patient should have residence Holland brought forward by Having Value (Variable Residence, Operator =, Value Holland ). 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: Explanation: Relations: State is exclusion of Study Objective Treatment Method is exclusion of Study Objective Treatment Method is exclusion of Treatment Method State is exclusion of Treatment Method is exclusion of (EX) 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. e.g., State Fever being exclusion of Study Objective Renal Cell Carcinoma and not having fever. e.g., Treatment Method chemotherapy being exclusion of Study Objective Patients being treated with surgical resection and not having undergone chemotherapy. e.g., Treatment Method chemotherapy being exclusion of Treatment Method radiation. 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: Relations: Selection Criteria is part of Selection Criteria Set is part of (PO) e.g., Selection Criteria PC1 is part of Selection Criteria Set { PC1, DC1, DC2 }. 30

32 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: State: Study Type: Variable to be measured: Target: Treatment Method Paracetamol Treatment Method A State Headache no State Migraine Study Type Phase III Variable Efficacy 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. 31

33 Treatment Method (TM-name) ISO2 Study Type (STP-name) Variable (VAR-name) being_subject_of applying_to ISO3 influences applying_to being_subject_of having_value EX2 being_exclusion_of applying_to_exclusion INF being_influenced_by RVAR HVAL being_operator_of Operator (OP-name) being_subject_of ISO1 applying_to with_restriction being_restriction_of being_value_of State (ST-name) Study Objective (SO-name) Value being_exclusion_of applying_to_exclusion EX1 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 32