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The PHARMO Institute Van Deventerlaan 30-40 3528 AE Utrecht
The Netherlands
Founded in 1999, the PHARMO Institute is an independent scientific research organisation dedicated to the study of epidemiology, drug utilisation, drug safety, health outcomes, and utilisation of healthcare resources.

Glossary

Rich data

Rich data

In-depth, detailed patient-level data, also known as rich or deep data. PHARMO is specialised in the collection and analysis of complex and rich longitudinal patient-centric data for better understanding of real-world clinical practice and to provide insights for future decision-making and innovation.

With our tailor-made and disease-specific cohorts, based on the cradle to grave principle and linkages with various healthcare databases, we give our clients a complete profile of a person’s medical history throughout their life and even starting from before birth: an overview of a patient’s journey.
Market authorisation

Market authorisation

Market authorisation includes the approval to market a medicine. All medicines must be authorised before they can be made available to patients. There are two different routes via which medicines can be authorised in the EU, a centralised route and a national route. Under the centralised authorisation procedure, pharmaceutical companies submit their marketing authorisation application to the European Medicines Agency (EMA). The Committee for Medicinal products for Human use (CHMP) carries out a scientific assessment of the application and gives a recommendation on whether the medicine should be marketed. Once approved by the EMA, the marketing authorisation holder (MAH) is allowed to market the medicine and make it available throughout the EEA.

When the national route is used for market authorisation, approval is only valid for that specific country. Each EU member state has its own national authorisation procedures. More information can be acquired via the national competent authorities.
Risk management plan

Risk management plan

A risk management plan (RMP) is a detailed description of the activities and interventions designed to identify, characterise, prevent or minimise risks relating to medicines. Pharmaceutical companies are required to submit a RMP to the EMA when applying for a marketing authorisation. RMPs include information on the safety profile of a medicine, risk minimisation plans or prevention of risk, plans for more studies to gain knowledge about the safety and efficacy of the medicine, risk factors for developing adverse reactions and assessment of the effectiveness of risk minimisation measures. RMPs can also be requested by national authorities whenever there is a concern about the medicine.

Studies using the PHARMO Database Network can provide information for different parts of a RMP, e.g. module SI – Epidemiology of the indication(s) and target population; module SV – Post-authorisation experience; or part V – Risk minimisation measures.
Pragmatic trial

Pragmatic trial

Results from traditional randomised clinical trials (RCTs) are difficult to translate to real-world clinical practice. Pragmatic trials are used to bridge the gap between RCTs and clinical practice and measure the effects of an intervention in daily practice (i.e. effectiveness). Pragmatic trials are sometimes referred to as current practice trials. Pragmatic trials are concerned with whether the intervention works under real-world conditions and whether it works in terms that matter to the patient. The design of a pragmatic trial reflects the variations between patients that occur in clinical practice.

PHARMO is currently developing its capabilities in the field of current practice trials.
Patient journey

Patient journey

The patient journey describes all the sequential steps in providing a patient’s care. Patient journeys are created in our Database Network by combining all longitudinal data from one patient identified in multiple data sources. This way PHARMO is able to monitor the medical history of a patient, but also the healthcare used after a disease period. With this information, we create insight in potential risk factors for disease or co-morbidities, as well as long-term effects of disease or therapy. During the use of medication we are able to monitor the adherence and/or persistence to medication by following the patient over time.

Please note that we only provide results on an aggregated level and will never disclose patient identifying information.

Registry

Registry

Registries are collections of data on individuals usually focussed around a specific disease. Registries can provide healthcare professionals and researchers with information about people with a specific diagnosis, both on a patient level or at group level, and over time. These data will provide new insights in the disease of interest, patient characteristics, treatments and more.

Registries are not sources or RWD because the data they hold are defined. However, registries can be used to enrich the PHARMO Database Network by linkage (e.g. cancer registry).

Real world evidence

Real world evidence

Real world evidence (RWE) is generated from RWD. With the expert analytical skills of the research team at PHARMO RWD can be converted into RWE to create an unique insight a wide variety of outcomes, including patient journeys, treatment patterns and healthcare utilisation.

Real world data

Real world data

Real-world data (RWD) are observations of effects based on what happens after a prescriptive (treatment) decision is made where the researcher does not or cannot control who gets what treatment and does not or cannot control the medical management of the patient beyond observing outcomes. The observations are made in real-life clinical practice.

There is an increasing demand for RWD both in pre- and post-approval settings. Before launch, RWD can be used to efficiently identify patients for randomised clinical trials; or to evaluate unmet clinical needs, describe healthcare/resource utilisation in preparing for a health technology assessment in the reimbursement process.

Post-approval RWD is used to describe the effectiveness and safety of a medicine in post-authorisation safety/effectiveness studies, which are performed either voluntary or upon request by the EMA. Furthermore, RWD can be used by pharmaceutical companies to monitor the uptake of their medicine and prescribing preferences.

The PHARMO Database Network consists of RWD collected from multiple sources, creating an unique insight in the patient journey. Our data fit both your pre- and post-approval questions and will help guide your decision-making throughout the product life cycle.

Predictive modeling

Predictive modeling

Predictive modeling is a process that uses large datasets and probability to forecast health outcomes. Each model is made up of a number of predictors, which are the variables that are likely to influence future results. The predictive value of these variables is assessed in relation to the outcome of interest. Subsequently, the relevant variables are incorporated in the final statistical model. PHARMO has developed models to predict severity of disease or to distinguish between sub-populations of patients for indications that have overlapping symptoms (e.g. in diabetes, COPD or cardiovascular disease).

Other applications of predictive models include:

defining at-risk populations who are likely to develop a specific health outcome
identifying patients who will gain most from a specific therapy
predict how the uptake of therapy will be

Pharmacovigilance

Pharmacovigilance

Much information on the safety of a medicine is collected in a controlled clinical trial setting. However, little is known about the safety of a medicine in the less controlled, real-world setting post-authorisation. By continuing to collect information once a medicine is available and taking action in response, regulators can continue to protect the public from emerging safety issues throughout a medicine’s life cycle, thereby ensuring its safe and effective use. Pharmacovigilance is defined by the EMA as the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine-related problem. The company that holds the marketing authorisation has legal obligations to continuously collect data, conduct pharmacovigilance and notify the authorities when there is a concern about the benefit-risk balance. Suspected adverse drug reactions which occur during development or post-authorisation, are reported and evaluated in EudraVigilance, a data processing network and management system. Subsequently the Pharmacovigilance Risk Assessment Committee (PRAC) provides recommendations on all aspects of pharmacovigilance and risk management.

PHARMO routinely collects data on medicines in a real-world setting through our healthcare providers. These data can form an essential part of your pharmacovigilance activities.

Non-interventional study

Non-interventional study

Non-interventional studies (NIS) are observational studies during which the investigator only observes, monitors and/or records treatment and/or outcomes, but does not actively interfere with the course of healthcare. Retrospective studies are by definition non-interventional as are databases in which the data collected is derived from routine clinical practice (e.g. the PHARMO Database Network). Additional collection of data and linkage of databases are also considered non-interventional, as long as the provided care is independent from inclusion in the study.

Medical Research Involving Human Subjects Act

Medical Research Involving Human Subjects Act

Medical/scientific research in which participants are subject to procedures or are required to follow rules of behaviour fall under the scope of the Medical Research Involving Human Subjects Act (Wet medisch-wetenschappelijk onderzoek met mensen (WMO)). These types of studies must undergo a prior review by accredited Medical Research Ethics Committees or the Central Committee on Research Involving Human Subjects (CCMO).

Retrospective database research, as usually performed at PHARMO, does not fall under the WMO. For this type of study it is important to comply with the Personal Data Protection Act. In order to comply with the Dutch privacy regulations, the separate foundation STIZON was established for the collection, linkage and anonymization of the data. PHARMO is only allowed to use the fully anonymised data for research.

Immortal time bias

Immortal time bias

Retrospective studies in databases with real-world data are inherently related to different types of bias. Immortal time bias arises when the period between cohort entry and date of first exposure to a drug, during which death has not occurred, is either misclassified or simply excluded and not accounted for in the analysis. Immortal bias can generate an illusion of treatment effectiveness and often occurs when studies compare against ‘non-users’.

Our in-house experts are familiar with the pitfalls of our data and will always correct for these flaws in the best possible way.

Healthy user bias

Healthy user bias

Healthy user bias can occur in clinical trials when people who are cautious about there health are more likely to participate in the study. When a study is subject to healthy user bias, its results may not be applicable to the general (less healthy) population.

At PHARMO we only use real-world data, which minimises the risk of healthy user bias and provides results that can be generalised to a broader population.

Health technology assessment

Health technology assessment

Health technology assessment (HTA) is part of the market access process, which also includes pricing and reimbursement. The WHO defined HTA as a systematic evaluation of properties, effects and/or impacts of an intervention. It is a multidisciplinary process that can be used to inform policy and decision-making in healthcare. Healthcare funds are limited and HTA helps to determine the allocation of these funds.

A HTA model consists of multiple domains, including health problem and current use of technology, safety and clinical effectiveness. Our data help create insight in these three domains in clinical practice.

Health economics and outcomes research

Health economics and outcomes research

Health economics and outcomes research (HEOR) is a discipline that quantifies the economic and clinical consequences of interventions to inform decision-making on access to them. To provide stakeholders with a reliable view of the value, real-world data is essential. With the PHARMO Database Network stakeholders will be able to get rich data from multiple data sources, which will help determine the true value of the product.

European Medicines Agency

European Medicines Agency

The EMA is the European Union body responsible for the protection and promotion of public and animal health, through the evaluation and supervision of medicines for human and veterinary use. The American counterpart of the EMA is the Food and Drug Administration (FDA).

The authorities may request post-authorisation studies to gain more insight in the benefit-risk balance. PHARMO is an experienced sparring partner with knowledge on how to develop, coordinate and execute observational studies as requested by the authorities.

Electronic medical records

Electronic medical records

Electronic medical records (EMR) are routinely filled by healthcare providers. The data from the EMRs are collected by STIZON and anonymised for research purposes. The research with these data is done by PHARMO. Since the data are collected from real-world sources, they can provide a unique insight in the actual properties of your drug or disease of interest.

Efficacy & Effectiveness

Efficacy & Effectiveness

While randomised controlled trials still are considered as the gold standard to determine the effect of an intervention, more and more attention is pointed at the question whether the effect is also present in an uncontrolled, real-world setting. For this reason, efficacy data are often complemented with effectiveness data. Efficacy tells you whether an intervention works under ideal circumstances, while effectiveness tells you whether an intervention works under real-world circumstances.

The data on the efficacy are available in the pharmaceutical industry, but in order to answer the effectiveness question, additional real-world data is needed. PHARMO is a rich source of real-world data from multiple healthcare providers and, besides, experienced in post-authorisation effectiveness studies.

Data mining

Data mining

Data mining is used to extract information from unstructured data such as free text in GP records. The coding of certain outcomes is not always accurate in electronic medical records (EMR). Using data mining enables us to identify more patients of interest and/or to obtain more information on specific outcomes of interest.

Cox proportional hazard ratio

Cox proportional hazard ratio

Cox regression models aim to estimate the hazard ratio (i.e. ratio of incidence rates) to model the effect of covariates on the hazard rate. However, the baseline hazard ratio remains unspecified. Using this type of analyses, one can determine the relative risk on a specific outcome per variable (e.g. risk factor).

Big data

Big data

Big data often is used to describe large electronic (health) databases. Here at PHARMO we have a lot of data, however, we would like to define our data as rich data. Due to the linkage of multiple databases (e.g. General Practitioner, In-patient Pharmacy, Clinical Laboratory) we can obtain in-depth information per patient with regard to multiple aspects of their healthcare use. However, the more databases linked, the smaller the study cohort will be.

Algorithm

Algorithm

Algorithms are procedures consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. PHARMO has developed several algorithms for the selection of patients and distinction between diseases.

For example, in the Out-patient Pharmacy Database patients with asthma and/or COPD are selected based on drug dispensings. Indication of use is not included in the dispensing records. However, the distinction between the indication asthma or COPD is possible by using a validated algorithm developed by PHARMO. The algorithm is based on specific medication use, age, gender, duration of use, prescriber and number of prescriptions.

Adaptive licensing

Adaptive licensing

Medicines adaptive pathways to patients (MAPPs) are part of the efforts from the EMA to improve timely access to new medicines for patients. Adaptive licensing is defined by the EMA as “a prospectively planned process, starting with the early authorisation of a medicine in a restricted patient population, followed by iterative phases of evidence gathering and adaptation of the marketing authorisation to expand access to medicine to broader patient populations”. The process consists of repeated phases of data gathering and regulatory evaluation resulting in few marketing authorisations submitted and approved throughout the drug development. The goal of this approach is to maximise the positive impact of new medicines on public health by balancing timely access with the need to provide adequate evolving information on benefits and risks. Real-world evidence may be used (in addition to clinical trial data) to build the evidence for adaptive licensing of a medicine.

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