IMI-GetReal Glossary of Key Terms
Definitions of key terms relevant to the IMI-GetReal Consortium – a precurser to the GetReal Institute
The table below contains definitions of key terms relevant to the IMI-GetReal Consortium.
The GetReal glossary working group acknowledges there is a debate around many of these terms, and in some cases no clear consensus has been reached in the international community. However, for the purpose of IMI-GetReal these are the proposed definitions
Please note: the definitions presented here are the result of several rounds of consultation. More specifically, definitions in previous versions of the glossary have been subjected to: an internal GetReal consultation round for consortium members in 2015, a public consultation round for external stakeholders in 2015, as well as second internal GetReal consultation round in 2016. For readers interested in the comments received during these rounds as well as authors’ replies, please see the full IMI-GetReal Glossary Report here.
Term | Definition | References |
Adaptive clinical trial | A clinical trial that evaluates patients’ response and reaction to an intervention at pre-determined intervals, beginning with evaluation at an early stage in the clinical trials and subsequently modifying the trial according to findings generated by interim analyses. By means of an adaptive design, researchers thus have the opportunity to modify the trial procedures at different stages on the basis of analysing interim data from study subjects. Such modifications may include, but are not limited to: selected drug dosages, sample size, and patient selection criteria. (see also: “clinical trial”) (Adapted from CHMP 2007 and FDA, 2010). | Committee for Medicinal Products for Human Use. (2007). Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. London: EMEA; FDA (2010) Guidance for industry. Adaptive design clinical trials for drugs and biologics. Obtained on 5 March 2014. URL: http://www.fda.gov/downloads/Drugs/…/Guidances/ucm201790.pdf |
Administrative claims data | Data arising from a person’s use of the healthcare system and reimbursement of healthcare providers for that care (Strom 2005) | Strom, 2005; FDA GUIDANCE ON PHARMACOEPIDEMIOLOGIC SAFETY STUDIES (MAY 2013) |
Adverse event | Any untoward medical occurrence in a patient or clinical investigation subject administered a health intervention, and which does not necessarily have a causal relationship with this treatment. (ICH, 1994) | ICH (1994). Clinical Safety Data Management: defnitions and standards for expedited reporting. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Effic acy/E2A/Step4/E2A_Guideline.pdf |
Aggregate (Study) Data | Summary data of the results of a study (e.g. on subjects in a trial), as opposed to Individual Patient Data which represents the raw data of each study subject. Summary data is derived from individual patient data using a (statistical) method of aggregation (Adapted from Lyman, 2005) | Lyman, G. H., & Kuderer, N. M. (2005). The strengths and limitations of meta-analyses based on aggregate data. BMC medical research methodology, 5(1), 14. |
Aggregate Data Drug Information System (ADDIS) | Software designed to aid evidence-based decision-making in the healthcare setting. This software does so by offering on-demand features such as: pair-wise-network analysis, meta-analysis, and multiple-criteria decision analysis. (van Valkenhoef, 2013) | Gert van Valkenhoef, Tommi Tervonen, Tijs Zwinkels, Bert de Brock, Hans Hillege, ADDIS: A decision support system for evidence-based medicine, Decision Support Systems, Volume 55, Issue 2, May 2013, Pages 459-475, ISSN 0167-9236, http://dx.doi.org/10.1016/j.dss.2012.10.005. |
Alternative study design | This refers to all scientific studies investigating health interventions whose design does not follow the design of a randomised controlled clinical trial (see also:”randomised controlled clinical trial (RCT)”) (Freemantle, 2010) | Nick Freemantle, Thomas Strack, Real-world effectiveness of new medicines should be evaluated by appropriately designed clinical trials, Journal of Clinical Epidemiology, Volume 63, Issue 10, October 2010, Pages 1053-1058, ISSN 0895-4356, |
Bayesian Methods | Statistical methods based upon Bayes’ Theorem, which shows how to update prior knowledge in the light of new data (i.e. posterior probability ∝ likelihood x prior probability). Prior knowledge is defined in terms of probability distributions and can be based on subjective opinion, or on objective evidence, such as the results of previous research, or both, and is explicitly included in consequent calculations. Statistical inference is then based on suitable summaries from the posterior probability distribution. (Adapted from Rothman, 2008 and HTA glossary) | |
Bias | Systematic (non-random) errors in values of parameters that are the object of study. Errors in estimations can result from, for example, improper study design or analysis, and implicitly affect the internal validity and generalisaibility of study results. There are three main categories of bias: selection bias, information bias and confounding bias. (Adapted from Rothman, 2008 and Delgado-Rodriguez, 2004) | Rothman, K.J., Greenland S., Lash T.L. (2008) Modern Epidemiology. Lippincott Williams & Wilkins. ISBN: 978-0-7817-5564-1; Delgado- Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. |
Bridging Study | A study, supplemental to a randomised controlled clinical trial, designed to provide additional clinical data on the safety, efficacy, dose and regimen, thus allowing for the extrapolation of external trial data to a new subjects population with (possibly) different population characteristics. An ethnicity briding study is one example of such a supplemental study. (Adapted from ICH, 1998) | ICH (1998) ICH Topic E5 (R1) Ethnic Factors in the Acceptability of Foreign Clinical Data. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Effic acy/E5_R1/Step4/E5_R1 Guideline.pdf |
Case-control study | A study in which the exposure of a group that have experienced a certain outcome (cases) is compared to the exposure of a group who have not (yet) experienced the outcome (controls). Case-control studies can be retrospective or prospective. (Adapted from Rothman, 2008 and of life (HRQoL). (Grobbee & Hoes, 2015) | Rothman, K.J. Greenland, S. Lash, T.L. (2008). Modern Epidemiology. Lippincott Williams & Wilkins. 88.; Grobbee, D. E., & Hoes, A. W. (2014). Clinical epidemiology. Jones & Bartlett Publishers. |
Clinical endpoint/ outcome | An aspect of a subject’s clinical or health status that is measured to assess the benefit or harm of an intervention. A clinical endpoint describes a valid measure of clinical benefit due to intervention: the impact of the intervention on how a subject feels, functions and survives. It is clinically relevant, sensitive (responsive to change) and is both accepted and used by physicians and patients. Clinical endpoints may be a clinical event (e.g. mortality,) a composite of several events, a measure of clinical status (e.g. blood pressure), or health related quality (Adapted from EunetHTA, 2013) | EUnetHTA (2013) Methodology Guidelines. Endpoints used for relative effectiveness assessment of pharmaceuticals: Clinical Endpoints. http://www.eunethta.eu/sites/5026.fedimbo.belgium.be/files/Clinical%20endp oints.pdf |
Clinical Guideline/ Medical Guideline | A document produced with the intention to guide decisions and inform criteria with regards to disease prevention, disease diagnosis, disease management, and treatment in the routine clinical setting. (Adapted from Council of Europe, 2001) | |
Clinical significance | The practical importance of and benefit from a treatment. It describes whether and to which extent the intervention has a real genuine, palpable, noticeable effect on daily life. Clinical significance is usually informed by effect size and statistical significance. (See also “statistical significance”) (Adapted from Kazdin, 1999 and Redmond, 2001). | Kazdin, A.E., The Meanings and Measurement of Clinical Significance. Journal of Consulting and Clinical Consulting 67 (3): 332–9. doi:10.1037/0022-006x.67.3.332, 1999. |
Clinical Trial | Any investigation in human subjects intended to discover or verify the effects of an intervention, such as clinical, pharmacological and/or other pharmacodynamic effects, and/or to identify any adverse reactions to an intervention. (Adapted from ICH, 2001) | Guideline, I. H. T. (2001). Guideline for good clinical practice. J Postgrad Med, 47(1), 45-50. |
Cohort study | A study in which a group of subjects, sampled from a certain source population, are classified according to their exposure or determinant status and followed over time to ascertain the occurrence of a certain outcome. (Adapted from Rothman, 2008) | Rothman, K.J. Greenland, S. Lash, T.L. (2008). Modern Epidemiology. Lippincott Williams & Wilkins. 88. |
Comparative effectiveness research | The conduct and/or synthesis of research comparing different benefits and harms of alternative interventions and strategies to prevent, diagnose, treat, and monitor health conditions in routine clinical practice (i.e. the real world setting). Comparative effectiveness research includes both primary data collection and secondary analyses (such as systematic literature reviews, meta-analyses and economic evaluations). (IOM, 2009 & Sox, 2009) | .aspx; Sox, H.C. and S. Greenfield, Comparative effectiveness research: a report from the Institute of Medicine. Ann Intern Med, 2009. 151(3): p. 203-5. |
Comparator | Reference intervention to which safety, efficacy and/or effectiveness of a health intervention (e.g. pharmaceutical product) are compared. In the case of clinical trials for pharmaceutical products, comparators can comprise a placebo treatment (placebo-control trials), available standard of care, and/or a licensed medication (active-control trial) (Adapted from ICH, 2001) | Guideline, I. H. T. (2001). Guideline for good clinical practice. J Postgrad Med, 47(1), 45-50. |
Conditional marketing authorisation | A one-year marketing authorization within the European Union with annual review by the European Medicines Agency (EMA), and which applies in specific cases: (a) Seriously debilitating or life-threatening diseases; (b) Emergency threats determined by the WHO, or the EU Commission; (c) Orphan medicinal products. Accelerated drug approval is the near equivalent of conditional marketing authorisation in the USA. (Boon, 2011) | Boon, H., Conditional Marketing Authorisations in the European Union, in FDA ODAC meeting. 2011: Silver Spring, MD; Goozner, M., Accelerated drug approval: FDA may get tougher; companies cite hurdles. J Natl Cancer Inst, 2011. 103(6): p. 455-7 |
Confounder | An extraneous variable in a statistical model that correlates with both the dependent variable (e.g. outcome) and the independent variable (e.g. intervention). Lack of consideration of confounders in statistical analyses can lead to spurious statistical relationships between the dependent and independent variables. (Adapted from Greenland, 2001) | Greenland, S., & Morgenstern, H. (2001). Confounding in health research. Annual review of public health, 22(1), 189-212. |
Confounding bias | Systematic error that occurs when the estimate of a measure of association between exposure (e.g. healthcare intervention) and outcome (e.g. health status) is distorted by the effect of one or several extraneous variables (confounding factor(s)) that are independently related to the exposure and outcome (Adapted from Strom, 2006) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd. |
Covariate | A variable that may be predictive of the outcome under study. Alternative terms are explanatory variable, independent variable, or predictor. Depending on its causal impact on the outcome under study, a covariate may be of direct interest, or act as a confounder or effect modifier (Adapted from J. M. Last, 2001). | Last, J.M., (ed.). A Dictionary of Epidemiology [4th ed.]. Oxford UP. ISBN 0- 19-514168-7, 2001. |
Coverage decisions | Decisions taken by healthcare payers/ insurers to determine allocation of resources with regards to which health interventions to reimburse, and the extent of reimbursement associated with the interventions covered by the payment package. (Steiner, 1996) | Steiner C.A., Powe N.R., Anderson G.F., Das A (1996). The review process used by U.S. health care plans to evaluate new medical technology for coverage. Journal of General Internal Medicine; 11(5); pp 294-302. |
Cross-design evidence synthesis | Evidence synthesis comprising the pooling and analysis of data from different studies. The studies from which data is combined can differ in relation to their design types, clinical setting, outcome measures, study interventions, study parameters, or patient population. (See also: “meta-analysis”) (Athanasiou, 2011) | Athanasiou T., Darzi A. (2011). Evidence Synthesis in Healthcare: A practical Handbook for Clinicians. Springer, ISBN 978-0-85729-175-8, doi: 10,1007/978-0-85729-206-3 |
Cross-sectional study | A study in which one ascertains exposure status and outcome status for the observed population at one specific point in time. (Adapted from Rothman, 2008) | Rothman, K.J. Greenland, S. Lash, T.L. (2008). Modern Epidemiology. Lippincott Williams & Wilkins. 88. |
Direct treatment comparisons | The comparison of the relative effect(s) of several interventions for a particular therapeutic indication in a trial setting. The basis for comparison can vary (e.g. clinical endpoints, adverse effect rates, or drug adherence rates) and the trial type may vary (e.g. randomised controlled clinical trial, pragmatic clinical trial or observational study). (Adapted from Hetland et al, 2009) | Hetland, M. L., Christensen, I. J., Tarp, U., Dreyer, L., Hansen, A., Hansen, I. T., … & Østergaard, M. (2010). Direct comparison of treatment responses, remission rates, and drug adherence in patients with rheumatoid arthritis treated with adalimumab, etanercept, or infliximab: results from eight years of surveillance of clinical practice in the nationwide Danish DANBIO registry. Arthritis & Rheumatism, 62(1), 22-32. |
Discrete-event simulation model | The key components of a discrete-event simulation (DES) model are the units that are simulated, the type of events that can occur, and the rules that govern the progress from one to the next event. Units are typically individual patients, the events could for example be stroke or increase or decrease of blood pressure in cardiovascular disease. Individual level RCT data are used to specify the functional form of the attributes that characterize each unit. Such data also serve to set up the rules that govern the changes in the attributes. DES provides a framework for individual level stochastic simulation: first, a virtual patient is generated, then the time of occurrence and type of the first event are simulated, the event rates and probabilities are updated in the light of the event, the second event is simulated using the new rates and probabilities, and so on. This scheme is repeated until a pre-defined stopping rule is met (for example the virtual patient reaches the end of the follow-up period).Events thus affect the variables that characterize the patient and his or her disease state. These variables, in turn, influence the rates of future events. Through this process, the patient’s full trajectory over the time span of the simulation is generated. This is repeated for a large number of patients. At the end, the outcome variables are summarized. | Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4. Value Health. 2012 Sep-Oct;15(6):821-7. doi: 10.1016/j.jval.2012.04.013. J. J. Caro, “Pharmacoeconomic analyses using discrete event simulation,” PharmacoEconomics, vol. 23, no. 4, pp. 323–332, 2005. J. Banks, J. S. Carson, and B. L. Nelson, Discrete-event system simulation. Prentice Hall, 1996. |
Drivers of effectiveness | Non-drug variables (i.e. related to the healthcare system, the disease, the patient, or the actual use of drug) for which a variation in distribution between real-world settings, as opposed to the setting in RCTs, would impact the outcome of a treatment. In turn, the effectiveness of the drug can be understood as the result of the modification of the pharmacological efficacy by the distributions of these drivers of effectiveness. (See also “effectiveness” and “efficacy”) (GetReal, 2016) | IMI-GetReal Glossary Workgroup, 2016 |
Drug utilisation | Defined by the World Health Organisation as the the marketing, distribution, prescription and use of drugs in a society, with special emphasis on the resulting medical, social, and economic consequences. This definition contains 2 aspects: the process of drug utilisation, that is the movement of drugs along the drug chain in society, and how drug utilisation relates to the effects of drug use (Baksaas, 1981; Lee & Bergman 1989) (See also “drug utilisation studies”) (Sacristén, 1994) | Sacristén, J. A., & Soto, J. (1994). Drug utilisation studies as tools in health economics. Pharmacoeconomics, 5(4), 299-312. |
Drug utilisation studies | Research designed to investigate drug utilisation. (See also: “drug utilisation”) (Adapted from Sacristén, 1994) | Sacristén, J. A., & Soto, J. (1994). Drug utilisation studies as tools in health economics. Pharmacoeconomics, 5(4), 299-312. |
Effect modification | Occurs when the magnitude of the effect of the primary exposure on an outcome (i.e., the association) differs depending on the level of a third variable. (Adapted from VanderWeele, 2009) | VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871. |
Effectiveness | The extent to which an intervention does more good than harm when provided under the usual circumstances of health care practice. (See also “ideal vs.usual conditions” (High Level Pharmaceutical Forum, 2008) | High Level Pharmaceutical Forum 2005-2008. Final Conclusions and Recommendations of the High Level Pharmaceutical Forum. URL: http://ec.europa.eu/pharmaforum/docs/final_conclusions_en.pdf |
Effectiveness studies | Clinical studies investigating health interventions whose design does not follow the design of a randomised controlled clinical trial and aims to reflect health intervention effectiveness in routine clinical practice. Effectiveness studies do not typically include randomisation of trial subjects, but there are exceptions (e.g. pragmatic clinical trials). For the purposes of GetReal, effectiveness studies include, but are not limited to, the following: pragmatic clinical trials, non-interventional/ observational studies, drug utilisation studies, post-authorisation efficacy/safety studies. (See also: “real-world studies”, “drug utilisation study”,”pragmatic clinical trial” and “non-interventional/ observational study” (IMI-GetReal, 2014) | IMI-GetReal Glossary Workgroup, 2016 |
Efficacy | The extent to which a healthcare intervention does more good than harm as compared to another healthcare intervention under ideal conditions. (See also “ideal vs.usual conditions”) (High Level Pharmaceutical Forum, 2008) | High Level Pharmaceutical Forum 2005-2008. Final Conclusions and Recommendations of the High Level Pharmaceutical Forum. URL: http://ec.europa.eu/pharmaforum/docs/final_conclusions_en.pdf Accessed 7 March 2014 |
Efficacy study | A study aiming to measure the effect of a drug under highly controlled conditions (i.e. in the setting of randomised controlled clinical trials). The study serves to prove the causal relationship between an intervention and an outcome, thus answering the question “can it work?” in an ideal world. | Luce, B.R., et al., EBM, HTA, and CER: clearing the confusion. Milbank Q, 2010. 88(2): p. 256-76. |
Efficacy- effectiveness gap | The observed discrepancy between effects of a health intervention in routine clinical practice as compared with the effects demonstrated in randomised controlled clinical trials. (Adapted from Eichler et al., 2011) | Eichler, H. G., Abadie, E., Breckenridge, A., Flamion, B., Gustafsson, L. L., Leufkens, H., … & Bloechl-Daum, B. (2011). Bridging the efficacy–effectiveness gap: a regulator’s perspective on addressing variability of drug response. Nature Reviews Drug Discovery, 10(7), 495- 506. |
Electronic health/medical record (EHR/EMR) | An electronic record of health-related information on an individual that can be created, gathered, managed, and consulted by authorized clinicians and staff within one healthcare organization. Patient health- related information may include all of the key administrative clinical data relevant to that person’s care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports. (Adapted from FDA 2013 and IOM, 2013) | Centers for Medicare en Medicaid Services http://www.cms.gov/Medicare/E- Health/EHealthRecords/index.html?redirect=/ehealthrecords/; FDA (2013). Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data. |
Electronic healthcare data | An organized set of healthcare data or collection of files available by computer through electronic format. It is derived from a raw electronic healthcare database. Electronic healthcare data include administrative claims data and electronic medical record (EMR) data. (Adapted from Hartzema, 2008 and FDA, 2013) | Hartzema, Tilson, and Chan. Pharmacoepidemiology and Therapeutic Risk Management. Cincinnati: Harvey Whitney Books, 2008.; FDA (2013). Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data. |
(Clinical) Equipoise | An ethical criterion for recommending subject participation in clinical trials that states that a subject can only be referred to study participation if there is genuine collective professional uncertainty from the medical community regarding the best health intervention for that subject. (Adapted from Miller, 2003) | Miller, F. G., & Brody, H. (2003). A critique of clinical equipoise: therapeutic misconception in the ethics of clinical trials. Hastings Center Report, 33(3), 19-28. |
External Validity/ Generalisability/ Applicability | Whether the results of a study can be reasonably applied to a definable group of patients in a particular clinical setting in routine practice (Adapted from Rothwell, 2005) | Rothwell, P. M. (2005). External validity of randomised controlled trials:“to whom do the results of this trial apply?”. The Lancet, 365(9453), 82-93. |
Health economic model | A logical mathematical framework demonstrating the quantitative relationship between a defined set of variables (e.g. cost, effectiveness, net benefit) based upon an explicit set of parameters and assumptions. The purpose of modeling is to structure evidence on clinical and economic outcomes in a form that can help to inform decisions about clinical practices and health-care resource allocations. (Adapted fromWeinstein, 2003). | Weinstein, M. C., O’Brien, B., Hornberger, J., Jackson, J., Johannesson, M., McCabe, C., & Luce, B. R. (2003). Principles of Good Practice for Decision Analytic Modeling in Health‐Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value in health, 6(1), 9-17. |
Health survey | Questionnaires designed to collect descriptions of health status and well- being, healthcare utilization, treatment patterns, and health-care expenditures from patients, providers, or individuals in the general population. (Garrisson, 2007) | Garrison et al. (2007). Using Real-World Data for Coverage and Payment Decisions: The ISPOR Real-World Data Task Force Report. Value in Health 10:5, 2007. |
Health Technology Assessment | The systematic evaluation of the properties and effects of a health technology, addressing the direct and intended effects of this technology, as well as its indirect and unintended consequences, and aimed mainly at informing decision-making regarding health technologies (HTAi, 2014) | HTAi – HTA glossary (http://htaglossary.net/health+technology+assessment+%28HTA%29) |
Heterogeneity (meta-analysis) | The variability between studies included in an evidence synthesis, due to differences in population, outcomes, interventions, or study design. Such variability may lead to differences in the observed treatment effects that exceed what is expected by chance. In that case, there is statistically detectable heterogeneity. (See also “meta-analysis” and “cross-design evidence synthesis”) (Adapted from Higgings, 2008 and Dias, 2013) | Higgins, J.P.T, Green,S. (eds.), Cochrane handbook for systematic reviews of interventions [Part 2, Chapter 9.5]. Vol. 5. Chichester: Wiley-Blackwell, 2008. Dias, S., Sutton, A.J., Welton, N.J., Ades, A.E., Evidence synthesis for decision making 3 heterogeneity—subgroups, meta-regression, bias, and bias-adjustment, Medical Decision Making 33.5: 618-640, 2013. |
Heterogeneity (network meta- analysis) | An assumption sometimes made in network meta-analysis that the heterogeneity parameters are equal across all possible treatment comparisons , i.e. the variance of the random effects is the same for all treatment comparisons in the network. Such an assumption can be useful when the data are sparse (e.g. few studies per comparison), and simplifies the estimation in the presence of multi-arm studies (Adapted from Higgins,1996, Salanti, 2008 and Dias, 2013) | Higgins, J.P.T., Whitehead, A., 1996. Borrowing Strength from External Trials in a Meta-Analysis. Stat. Med. 15, 2733–2749. doi:10.1002/(SICI)1097-0258(19961230)15:24<2733::AID- SIM562>3.0.CO;2-0. ; Salanti, G., Higgins, J.P., Ades, A.E., Ioannidis, J.P., 2008a. Evaluation of networks of randomized trials. StatMethods MedRes 17, 279–301. doi:10.1177/0962280207080643; Dias, S., Sutton, A.J., Welton, N.J., Ades, A.E., Evidence synthesis for decision making 3 heterogeneity—subgroups, meta-regression, bias, and bias-adjustment, Medical Decision Making 33.5: 618-640, 2013. |
Hierarchical model | A generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from an underlying distribution. For example, in a random-effects meta-analysis, the relative treatment effect parameters in the individual studies are drawn from a random effects distribution, which allows for statistical heterogeneity between studies. (See also “meta-analysis” and “heterogeneity (meta-analysis)”) (Adapted from Gelman, 2014) | Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis [3rd ed., Part 1, Chapter 5]. Boca Raton, FL, USA: Chapman & Hall/CRC. |
Homogeneity (meta-analysis): | The absence of heterogeneity between studies included in an evidence synthesis. (See also “heterogeneity (meta-analysis)”) (IMI-GetReal, 2016) | IMI-GetReal Glosssary Workgroup, 2016 |
Ideal vs. Usual conditions | The context in which a treatment is being studied consists of the mode of administration, side-effects and their treatment, diet, auxiliary care, associated treatments, etc. All the characteristics of this context can be called contextual factors or extraneous factors, i.e. characteristics of the population (e.g. genetics, behaviour towards the drug, co-morbidities), the healthcare delivery system (e.g. physician behaviour of prescription, guidelines etc.), the actual use of the drug (compliance, health beliefs, co-medication, etc.). The ideal condition refers to the situation where the setting is experimental and controlled, and where the contextual factors are fixed and equalized in the two (or more) therapeutic groups through randomization, blinding and/or standardisation. For instance, the intervention status will be set at the beginning of the trial, and emphasis is put on not to change intervention status overtime, etc. Often, the design is optimised to show the most benefit of the investigated interventions. The usual condition (or routine clinical practice) refers to what really happens when an intervention is prescribed by a physician, to a patient. In routine clinical practice, the contextual factors are not fixed: they vary according to the physician’s usual habits, the disease severity, patients’ preferences, etc.
| Schwartz, D. and J. Lellouch, Explanatory and pragmatic attitudes in therapeutical trials. J Chronic Dis, 1967. 20(8): p. 637-48. |
Indirect treatment comparisons | The comparison of several (i.e. 2 or more) interventions (e.g. A and B) for a particular therapeutic indication in the absence of a trial that directly compares the interventions in question. This implies that comparison is done based upon a third intervention (e.g. C) against which A and B have been directly compared in a trial setting (Adapted from ISPOR, 2011) | Hoaglin, D. C., Hawkins, N., Jansen, J. P., Scott, D. A., Itzler, R., Cappelleri, J. C., … & Barrett, A. (2011). Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value in Health, 14(4), 429-437. |
Individual patient data (IPD) / Patient- Level Data | The raw data for subjects included in a study, as opposed to aggregate data (summary data for the comparison groups in a study) (Adapted from Bandolier, 2014) | Bandolier Glossary – IPD; http://www.medicine.ox.ac.uk/bandolier/booth/glossary/individual.html |
Information bias | A flaw in measuring exposure, covariate, or outcome variables that results in different quality (accuracy) of information between comparison groups. The occurrence of information biases may not be independent of the occurrence of selection biases. (See also: “bias”, “selection bias”) (Porta, 2008) | Porta, M., ed. (2008). A Dictionary of Epidemiology (Fifth ed.). New York: Oxford University Press. p. 128. ISBN 978-0-19-531449-6. |
Informative prior | A prior distribution used to incorporate prior knowledge into a Bayesian analysis. This is useful when some parameters might otherwise not be identifiable, such as the random effects variance when only a small number of studies is available. Informative priors can be based on external sources of evidence or expert judgment. A strongly informative prior may have a noticeable influence on analysis results, and therefore could invalidate results if it is biased. (Adapted from Gelman, 2014) | Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis [3rd ed., Part 1, Chapter 2.4]. Boca Raton, FL, USA: Chapman & Hall/CRC. |
Internal validity | The extent to which study attributes (e.g. study design) keep the possibility of systematic errors (i.e. biases) to a minimum. (See also “Bias” (Adapted from Rothwell, 2005) | Rothwell, P. M. (2005). External validity of randomised controlled trials:“to whom do the results of this trial apply?”. The Lancet, 365(9453), 82-93. |
Large simple trials | Large simple trials are pragmatic, randomised clinical trials with minimal data collection protocols that are narrowly focused on clearly defined outcomes that are important to patients as well as clinicians. Their large sample size provides the adequate statistical power to detect a small difference in effects between treatments in a situation where a moderate difference in an important outcome may be important. Additionally, LST’s include follow-up that mimics normal clinical practice. LST’s are by definition pragmatic clinical trials. (See also “pragmatic clinical trials”) (Adapted from Stroup 2011, Peto 1995) | Stroup, T.S., What can large simple trials do for psychiatry? Am J Psychiatry, 2011. 168(2): p. 117-9.; Peto, R., R. Collins, and R. Gray, Large- scale randomized evidence: large, simple trials and overviews of trials. J Clin Epidemiol, 1995. 48(1): p. 23-40; Clinical Trials Transformation Initiative. Large Simple Trials: Facilitating the Use of Large |
Longitudinal study | A study in which subjects are followed over time with continuous or repeated monitoring of risk factors or health outcomes, or both (Adapted from BMJ, 2014) | BMJ, Longitudinal studies http://www.bmj.com/about-bmj/resources- readers/publications/epidemiology-uninitiated/7-longitudinal-studies |
Marketing authorisation | A licence given by a regulatory authority to a pharmaceutical manufacturer allowing for the marketing of a specific product within the jurisdiction of the regulatory agency. The decision for granting a marketing authorisation is based primarily on the quality, safety and efficacy of the new medicinal product. (Adapted from MHRA, 2014) | MHRA – Marketing Authorisations (http://www.mhra.gov.uk/Howweregulate/Medicines/Licensingofmedicines/M arketingauthorisations/index.htm#l3) |
Markov model | Stochastic multi-state transition models. Transition between different states in the model are based upon pre-defined conditional probabilities that depend only on the current states (i.e. future evolution depends only on the current state, not the past states). (See also: “multi-state transition model” (Adapted from Briggs, 1993) | A. Briggs and M. Sculpher, “An introduction to Markov modelling for economic evaluation,” PharmacoEconomics, vol. 13, no. 4, pp. 397–409, Apr. 1998. Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. Med Decis Making. 1993 Oct-Dec;13(4):322-38.
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Medicine Adaptive Pathways to Patients (MAPP’s) | A prospectively planned, flexible approach to regulation of drugs and biologics. It starts with the early authorisation of a medicine in a restricted patient population, followed by iterative phases of evidence- gathering and the adaptation of the marketing authorisation to allow broader patient populations to access the medicine. Adaptive pathways are sometimes known as ‘staggered approval’ or ‘progressive licensing’. (Adapted from Eichler, 2012 and EMA, 2014). | Eichler, H. G., Oye, K., Baird, L. G., Abadie, E., Brown, J., Drum, C. L., … & Hirsch, G. (2012). Adaptive licensing: taking the next step in the evolution of drug approval. Clinical Pharmacology & Therapeutics, 91(3), 426-437.; http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/general/gen eral_content_000601.jsp&mid=WC0b01ac05807d58ce |
Meta-analysis | Statistical methods allowing for the combination of study outcomes from several studies, while also accounting for uncertainties resulting from the combination of data from varying sources. (See also “evidence synthesis”) (Adapted from Greenland S., 2008) | Greenland S, O’ Rourke K: Meta-Analysis. Page 652 in Modern Epidemiology, 3rd ed. Edited by Rothman KJ, Greenland S, Lash T. Lippincott Williams and Wilkins; 2008. |
Meta- regression | Assessment of the extent of the effect of moderator variables on outcomes presented by a model in the context of meta-analysis. This is achieved through the use of regression techniques, and can be based on both aggregate and individual patient level data. Data for the regression analysis may originate, and be synthesised, from several studies. (See also: “meta-analysis”) (Adapted from Stanley, 1989) | T.D. Stanley and Stephen B. Jarrell, (1989). Meta-regression analysis: A quantitative method of literature surveys. Journal of Economic Surveys, 19(3) 299-308. |
Misclassificatio n bias | Possibility of error associated with the classification of study participants with regards to their exposure status, outcome status, or disease status. Random occurrence of information bias is known as ‘non-differential misclassification’ while non-random occurence is known as ‘differential misclassification’. Missclasification bias falls within the category of information bias. (See also: “Bias” and “Information bias”.) (Adapted from Strom, 2006 and Delgado-Rodriguez, 2004) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd.; Delgado-Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. |
Multi-state transition model | Multi-state transition models are used to model prognosis for clinical problems with ongoing risks. The model assumes that the patient is always in one of a finite number of clinical conditions, referred to as states. All events of interest are modelled as transitions from one state to another. Multi-state transition models can be either discrete or continuous time models. For the former the transitions are only possible at certain time points. The time interval from one time point to the next one is referred to as a cycle. The net probability of making a transition from one state to another during a single cycle is called transition probability. This transition probability can be constant over time or not. Multi-state transition models can model a cohort of patients or individuals. In the latter case they are called microsimulation models. A prominent example is the illness-death model with three states representing health, illness and death. (Adapted from Sieber et al, 2012) | Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens DK, Cohen DJ, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. State- transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3. Value Health. 2012 Sep-Oct;15(6):812-20. doi:10.1016/j.jval.2012.06.014. |
Network meta- analysis (NMA) | An extension of meta-analysis, allowing for the comparison of the relative effects of multiple treatments, either with or without the presence of a common comparator against which all interventions are studied. NMA methods take into account Direct and Indirect Treatment Comparisons and pairwise meta-analysis. (see also: “transitivity assumption”, “direct treatment comparisons” and “indirect treatment comparison”) (Adapted from Athanasiou, 2011 & Hawkins, 2011) | Athanasiou T., Darzi A. (2011). Evidence Synthesis in Healthcare: A practical Handbook for Clinicians. Springer, ISBN 978-0-85729-175-8, doi: 10,1007/978-0-85729-206-3; Hoaglin, D. C., Hawkins, N., Jansen, J. P., Scott, D. A., Itzler, R., Cappelleri, J. C., … & Barrett, A. (2011). Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value in Health, 14(4), 429-437. |
Non- informative prior | A prior distribution that does not contribute at all to the posterior estimation of a Bayesian analysis. Such priors are often “improper”, in that they are not true probability distributions. Although they let the data “speak for themselves”, they have the disadvantage that models using non-informative priors may not be identifiable or difficult to estimate. It is, however, increasingly acknowledged that non-informative may not exist, since all priors contain some information. (Adapted from Gelman, 2014 and Lambert, 2005) | Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B., Bayesian data analysis [3rd ed., Part 1, Chapter 2.8]. Boca Raton, FL, USA: Chapman & Hall/CRC, 2014; Lambert, P.C., Sutton, A.J., Burton, P.R., Abrams, K.R., Jones, D.R., 2005. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med 24, 2401–2428. doi:10.1002/sim.2112 |
Non- interventional study / Observational study | A study where the investigator does not interfere with choice of the prescribed health intervention i.e. interventions are prescribed in the usual manner in accordance with clinical practice. The assignment of the patient to a particular therapeutic strategy is not decided in advance by a trial protocol but falls within current practice and the administration of the intervention is clearly separated from the decision to include the patient in the study. No additional diagnostic or monitoring procedures are applied to the patients and epidemiological methods are used for the analysis of collected data. (See also “real world study”) (Adapted from EC, 2001) Footnote: Depending on local regulations, something considered to be interventional in one country (blood tests and patient questionnaires) may not be in others.
| European Commission (2001). DIRECTIVE 2001/20/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. Official Journal of the European Cmmunities. |
Observational Data | Data collected from populations as present in the routine setting of healthcare (i.e. outside the setting of a randomised controlled trial). Sources of observational may data include: routine clinical practice, patient registeries, hospital claims databases/administrative data, health surveys, electronic health records, medical chart reviews and post-marketing safety studies. (Adapted from Machin, 2007) | Machin D., Campbell M.J., Walters S.J. (2007). Medical Statistics: A Textbook for the Health Sciences (4th ed.). Wiley, Chapter 12. |
Patient- Reported Outcome (PRO) | A measurement based on a report that comes directly from the patient (i.e., study subject) about the status of a patient’s health condition without amendment or interpretation of the patient’s response by a clinician or anyone else. A PRO can be measured by self-report or by interview provided that the interviewer records only the patient’s response. (FDA, 2009) | FDA (2009) – Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. |
Phase 2 trials | Trials in which the primary objective is to explore therapeutic efficacy and safety in patients (NIH, 2001) | |
Phase 3 trials | Trials implemented to study the efficacy of the biomedical or behavioral intervention in large groups of human subjects (from several hundred to several thousand) by comparing the intervention to other standard or experimental interventions as well as to monitor adverse effects, and to collect information that will allow the intervention to be used safely. (NIH, 2001) | |
Phase 4 trials | Studies implemented in a post-marketing setting that are designed to monitor effectiveness of the approved intervention in the general population and to collect information about any adverse effects associated with widespread use. (NIH, 2001) | |
PICOT(S) | PICOT is an acronym for a method used in evidence based medicine to frame and answer a clinical quesion. “P” stands for patient, problem or population, “I” for intervention, “C” for comparison, control or comparator, “O” for outcomes and “T” for time. Alternative versions also include “S” for study design. (Riva, 2012) | Riva, J. J., Malik, K. M. P., Burnie, S. J., Endicott, A. R., & Busse, J. W. (2012). What is your research question? An introduction to the PICOT format for clinicians. The Journal of the Canadian Chiropractic Association, 56(3), 167–171. |
Post- authorisation | The period after market authorization of a specific pharmaceutical/ medical device product. (Adapted from Rang, 2006) | Rang H.P. (2006). Drug Discovery and Development: technology in transition. Elsevier press (2006). |
Post- authorisation efficacy studies | Studies conducted within the authorised therapeutic indication to complement available efficacy data in the light of well-reasoned scientific uncertainties on aspects of the evidence of benefits that should be, or can only be, addressed post-authorisation( EU Pharmacovigilance Directive). Footnote: Although the term refers to “efficacy”, PAES studies collect data in a setting that reflects general clinical practice rather than a randomised controlled trial. Therefore it may be more appropriate to think of these studies as providing “effectiveness” data rather than “efficacy” data. | EMA (2015) – Scientific guidance on post-authorisation efficacy studies: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guidelin e/2015/11/WC500196379.pdf. |
Post- authorisation safety studies | Any study relating to an authorised medicinal product conducted with the aim of identifying, characterising or quantifying a safety hazard, confirming the safety profile of the medicinal product, or of measuring the effectiveness of risk management measures. Such studies may be aimed at collecting data to enable the assessment of safety of medicinal products in everyday medical practice. (EU, 2010) | EU Pharmacovigilance Directive 2010/84/EU |
Post-marketing | The period after launch of a specific pharmaceutical/ medical device product in the market. This follows the post-authorisation phase (Wyeth et al., 2013) | Pragmatic clinical trial | A study comparing health interventions among a randomised, diverse population representing clinical practice, and measuring a broad range of health outcomes. To ensure generalizability, pragmatic trials should represent the intended patients to whom the treatment will be applied as best as possible. For instance, inclusion criteria would be broad (e.g. allowing co-morbidity, co-medication, wider age range, etc.), the follow- up would not be (or not much) interventional and allowing for treatment switching etc. (See also “large simple trials” and “real-world studies”) (Adapted from Schwartz, 1967, Tunis, 2003 & Roland, 1998) | Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of chronic diseases, 20(8), 637-648.; Tunis, S. R., Stryer, D. B., & Clancy, C. M. (2003). Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. Jama, 290(12), 1624-1632.; Roland, M. and D.J. Torgerson, What are pragmatic trials? BMJ, 1998. 316(7127): p. 285. |
Pre- authorisation | The period prior to the granting of the marketing authorisation (“approval”) / license for a specific pharmaceutical/ medical device product. (see also: “pre-authorisation drug development” (Rang H.P., 2006) | Rang H.P. (2006). Drug Discovery and Development: technology in transition. Elsevier press (2006). |
Pre- authorisation drug development | Research and development performed in order to discover new active substances and subsequently demonstrate safety and efficacy of a new drug in order to gain marketing authorisation. This includes the discovery phase, pre-clinical studies (Phase I) and clinical studies (Phase II-III). (Rang H.P., 2006) | Rang H.P. (2006). Drug Discovery and Development: technology in transition. Elsevier press (2006). |
Predictive modelling | The activity of developing, validating or adapting models which relate a dependent variable with a set of independent variables in a manner similar to multiple regression analysis. For the purposes of the IMI- GetReal project, this predictive modelling is desinged to predict the (relative) effectiveness of a medical intervention from available clinical and trial-based efficacy/effectiveness data. (Wilson, 2004) | Wilson, A. M., Thabane, L., & Holbrook, A. (2004). Application of data mining techniques in pharmacovigilance. British journal of clinical pharmacology, 57(2), 127-134. |
Propensity score | The conditional probability of assignment to a particular intervention given a vector of observed covariates. As such, the propensity score summarizes information of multiple confounders and can be used to adjust for confounding by means of matching, weighting or regression adjustment. (Adapted from Rosenbaum, 1981) | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. |
Protopathic bias | Protopathic bias arises when the initiation of an intervention (exposure) occurs in response to a symptom of the (at this point, undiagnosed) disease under study (outcome). This sort of bias falls within the category of information bias. (Adapted from Strom, 2006 and Delgado-Rodriguez,2004) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd.; Delgado-Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. |
Randomisation | The process of assigning trials participants to treatment or control groups, using an element of chance to determine the assignments. Randomization is a tool for providing comparable participant groups regarding all measurable and unmeasurable characteristics, apart from the intervention (“active” or control). It ensures the initial comparability of patients between the intervention groups. By randomization, one hopes to equalize the distributions of confounding factors, whether they are known or unknown. (Adapted from Strom, 2006) | Strom, B. and S.E. Kimmel, Textbook of Pharmacoepidemiology. 2006: John Wiley & Sons, Ltd. |
Randomised Controlled clinical Trial (RCT) | Clinical trial designed to test the efficacy of a medical intervention within a population of selected subjects. Subjects are subjected to rigorous inclusion and exclusion criteria, and upon inclusion, are randomised into separate treatment and control groups. Considered the gold standard for clinical trials. (See also “clinical trial” (Adapted from Solomon, 2009) | Solomon P., Cavanaugh M.M., Draine J. (2009). Randomoized Controlled Trials: design and implementation for community-based psychosocial interventions. Oxford Press. |
Real World Data (RWD) | An umbrella term for data regarding the effects of health interventions (e.g. safety, effectiveness, resource use, etc) that are not collected in the context of highly-controlled RCT’s. Instead, RWD can either be primary research data collected in a manner which reflects how interventions would be used in routine clinical practice or secondary research data derived from routinely collected data. Data collected include, but are not limited to, clinical and economic outcomes, patient-reported outcomes (PRO) and health-related quality of life (HRQoL). RWD can be obtained from many sources including patient registries, electronic medical records, and claims databases. (See also “randomised controlled clinical trial”, “real-world evidence” and “real-world study”) (Adapted from Garrisson, 2007 and GetReal, 2016) | Garrison et al. (2007). Using Real-World Data for Coverage and Payment Decisions: The ISPOR Real-World Data Task Force Report. Value in Health 10:5, 2007.; IMI-GetReal Glossary Workgroup 2016. |
Real World Evidence (RWE) | Real World Evidence (RWE) is the evidence derived from the analysis and/or synthesis of real-world data (RWD). (See also “real-world data”, “real-world study”) (GetReal, 2016) | IMI-GetReal Glossary Workgroup, 2016 |
Real World Study (RWS) | Studies investigating health interventions whose design does not follow the design of a highly-controlled RCT and aims to reflect health intervention effectiveness in routine clinical practice. Real world studies do not typically include randomisation of trial subjects, but there are exceptions (e.g. pragmatic clinical trials). For the purposes of GetReal, real-world studies include, but are not limited to, the following: pragmatic clinical trials, non-interventional/ observational studies, drug utilisation studies, post-authorisation efficacy/safety studies. RWS, by definition, generate RWD, which can subsequently be analysed and/or synthesised to produce RWE. (See also: “real-world data”, “real-world evidence”, “effectiveness study”, “drug utilisation study”, “pragmatic clinical trial” and “non-interventional/ observational study”) (GetReal, 2016) | IMI-GetReal Glossary Workgroup, 2016 |
Referral bias | Occurs if reasons for referring a patient to a health intervention/ health care institute are related to the drug exposure status e.g. when the use of the drug contributes to the diagnostic process. This sort of bias falls within the category of selection bias. (See also “Selection bias” (Adapted from Strom, 2006 and Delgrado-Rodriguez, 2004) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd.; Delgado-Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. | Registry | Database resulting from prospective, observational cohort studies of patients who have a particular disease and/or are receiving a particular treatment or intervention. (Garrisson, 2007) | Garrison et al. (2007). Using Real-World Data for Coverage and Payment Decisions: The ISPOR Real-World Data Task Force Report. Value in Health 10:5, 2007. |
Relative effectiveness | The extent to which an intervention does more good than harm, when compared to one or more alternative interventions for achieving the desired results and when provided under the routine setting of health care practice. (See also “ideal vs.usual conditions”) (High Level Pharmaceutical Forum, 2008) | High Level Pharmaceutical Forum. High Level Pharmaceutical Forum 2005- 2008. Final Conclusions and Recommendations of the High Level Pharmaceutical Forum. URL: http://ec.europa.eu/pharmaforum/docs/final_conclusions_en.pdf Accessed 7 March 2014 |
Relative efficacy | The extent to which an intervention does more good than harm when compared to one or more alternative interventions under ideal conditions. (See also “ideal vs.usual conditions”) (High Level Pharmaceutical Forum, 2008) | High Level Pharmaceutical Forum. High Level Pharmaceutical Forum 2005- 2008. Final Conclusions and Recommendations of the High Level Pharmaceutical Forum. URL: http://ec.europa.eu/pharmaforum/docs/final_conclusions_en.pdf Accessed 7 March 2014 |
Risk-sharing agreements | Agreements concluded by payers and pharmaceutical companies to diminish the impact on the payer’s budget of new and existing medicines brought about by either the uncertainty of the value of the medicine and/or the need to work within finite budgets. (Adamski, 2010) | Adamski, J., Godman, B., Ofierska-Sujkowska, G., Osińska, B., Herholz, H., Wendykowska, K., … & Gustafsson, L. (2010). Risk sharing arrangements for pharmaceuticals: potential considerations and recommendations for European payers. BMC health services research, 10(1), 153.
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Safety | The presence or absence of an adverse effect as a direct or indirect result of treatment intervention. (See also “adverse event” (ICH, 1994) | ICH (1994). Clinical Safety Data Management: defnitions and standards for expedited reporting. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Effic acy/E2A/Step4/E2A_Guideline.pdf |
Scientific advice | Advice given by a regulatory/ reimbursement authority to a manufacturer on appropriate tests and studies to be performed during product development/ application for product reimbursement, in order to avoid major objections being raised during evaluation of the marketing authorisation application/ reimbursement application. (Adapted from EMA, 2009) | EMA – Scientific advice and protocol advice. http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/general/gen eral_content_000049.jsp&mid=WC0b01ac05800229b9 |
Selection bias | A systematic error introduced due to the selective inclusion of subjects who differ in characteristics from the target population, or selective drop- out of subjects in a study. (Adapted from Strom, 2006 and Delgrado-Rodriguez, 2004) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd.; Delgado-Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. |
Self-selection bias | An error occuring when study participants decide themselves to particpate in or to leave a study based on both drug exposure and change in health status. This sort of bias falls within the category of selection bias. (See also “Selection bias”) (Adapted from Strom, 2006 and Delgrado-Rodriguez, 2004) | Strom B.L., Kimmel S.E. (2006). Textbook of pharmacoepidemiology. John Wiley & Sons, Ltd.; Delgado-Rodríguez, M., & Llorca, J. (2004). Bias. Journal of epidemiology and community health, 58(8), 635-641. |
Stakeholder | For the purposes of the IMI-GetReal project, a stakeholder is an individual, organisation or initiative that participates in, is involved with, influences the outcomes of, or is influenced by the outcomes of, or the implications of, the IMI-GetReal project (Adapted from Varvasovszky, 2000 and Freeman, 2010) | Varvasovszky, Z., & Brugha, R. (2000). A stakeholder analysis. Health policy and planning, 15(3), 338-345.; Freeman, R. E. (2010). Strategic management: A stakeholder approach. Cambridge University Press. |
Standard of care | Care delivered by a healthcare provider for a specific patient which should correspond to the care that an averagely competent physician in the same field would provide under similar circumstances. (Adapted from Strauss, 2009) | Strauss D.C., Thomas J.M. (2009). What Does the Medical Profession Mean By “Standard of Care?” JCO Nov 10, 2009: e192-193; published online on September 21, 2009. |
Statistical significance | A measure that describes the extent to which conclusions from a statistical analysis are supported by the data. Alternatively, it can be defined as the conclusion that findings from statistical analysis are true (i.e. not true due to random chance) for the sample studied, thus expected to be highly reliable. Footnote: High statistical significance does not necessarily mean that the finding is of great importance or decision-making utility. (See also “clinical significance”) (Adapted from Redmond, 2001) | Redmond, C., Colton, T., Clinical significance versus statistical significance, Biostatistics in Clinical Trials. Wiley Reference Series in Biostatistics [3rd ed.]. West Sussex, United Kingdom: John Wiley & Sons Ltd. pp. 35–36. ISBN 0-471-82211-6, 2001. |
Sub-group analysis | Anlaysis conducted to assess whether, and how, observed endpoints in a study are sensitive to/ affected by characteristics attributable to a specific sub-group of the study population. Such characteristics can relate to, for example, age, gender, genotype, or patient history. (Adapted from ICH, 1998) | ICH (1998) – E9 Guideline: Statistical Princinples for Clinical Trials. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Effic acy/E9/Step4/E9_Guideline.pdf. Biomarkers Definitions Working Group.Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001 Mar;69 (3):89-95. |
Surrogate endpoint/ outcome | A variable that provides an indirect measurement of effect in situations where direct measurement of clinical effect is not feasible or practical. It is intended to replace a clinical endpoint of interest that cannot be observed in a trial. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence. (Adapted from ICH, 1998 and Biomarkers Definitions Working Group, 2001) | ICH (1998) – E9 Guideline: Statistical Princinples for Clinical Trials. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Effic acy/E9/Step4/E9_Guideline.pdf; Biomarkers Definitions Working Group.Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001 Mar;69 (3):89-95. |
Time horizon | Period of time within a pharmacoeconomic model over which costs and health outcomes are gathered during a pharmacoeconoimc evaluation (Ademi, 2013) | Ademi, Z., Kim, H., Zomer, E., Reid, C. M., Hollingsworth, B., & Liew, D. (2013). Overview of pharmacoeconomic modelling methods. British journal of clinical pharmacology, 75(4), 944-950. |
Transitivity assumption | If the safety, efficacy, effectiveness or HRQoL of health intervention A is quantitatively related to that of B, and that of B to C, then A is related to C for all A, B and C in the domain of the relation. This is a fundamental assumption of indirect treatment comparisons and network meta-analysis. (see also:”mixed treatment comparisons”) (Athanasiou, 2011 and Efthimiou, 2016) | Athanasiou T., Darzi A. (2011). Evidence Synthesis in Healthcare: A practical Handbook for Clinicians. Springer, ISBN 978-0-85729-175-8, doi: 10,1007/978-0-85729-206-3; Efthimiou O., Debray, T.P.A., van Valkenhoef G., et al. (2016). GetReal in network meta-analysis: a review of methodology. Wiley, Research Synthesis Methods 1759-2887, http://dx.doi.org/10.1002/jrsm.1195
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Vague (weakly informative) prior | A prior distribution that provides a very small amount of information that allows a model to be estimated, while not having a large influence on the posterior estimation of a Beysian analysis. For example, a vague prior might specify that heterogeneity will not cause more than five orders of magnitude differences in the odds ratio, or that a change in blood pressure lies in the -500 to +500 mmHg range with 95% probability. In practice, vague priors may offer a better compromise between objectivity and practicability. (Adapted from Gelman, 2014) | Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B., Bayesian data analysis [3rd ed., Part 1, Chapter 2.9]. Boca Raton, FL, USA: Chapman & Hall/CRC, 2014. |
Work package | A specific subset of a project assigned to the execution of a specific aim. Work packages are defined by brief statements of: Activity Description, Activity Resources of Skill and Expertise, Activity Estimates of Effort and Duration, Activity Schedule, Activity Risks, and Activity Budget. (Weiss, 1992) | Weiss J.W. (1992). 5-Phase Project Management: a practical planning & implication guide. Addison-Wesley, 1992. ISBN 0201563169, 9780201563160
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