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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

5.6. Pragmatic trials and large simple trials

 

5.6.1. Pragmatic trials

 

RCTs are considered the gold standard for demonstrating the efficacy of medicinal products and for obtaining an initial estimate of the risk of adverse outcomes. However, as is well understood, these data are often not necessarily indicative of the benefits, risks or comparative effectiveness of an intervention when used in clinical practice populations. The IMI GetReal Glossary defines a pragmatic clinical trial (PCT) as ‘a study comparing several health interventions among a randomised, diverse population representing clinical practice, and measuring a broad range of health outcomes.’ PCTs are focused on evaluating benefits and risks of treatments in patient populations and settings more representative of routine clinical practice. To ensure generalisability, pragmatic trials should represent the patients to whom the treatment will be applied, for instance, inclusion criteria would be broad (e.g. allowing co-morbidity, co-medication, wider age range), the follow-up would be minimized and allow for treatment switching etc. Monitoring safety in a phase III real-world effectiveness trial: use of novel methodology in the Salford Lung Study (Pharmacoepidemiol Drug Saf. 2017;26(3):344-352) describes the model of a phase III pragmatic RCT where patients were enrolled through primary care practices using minimal exclusion criteria and without extensive diagnostic testing and where potential safety events were captured through patients’ electronic health records and in turn triggered review by the specialist safety team.

 

Pragmatic explanatory continuum summary (PRECIS): a tool to help trial designers (CMAJ 2009; 180: E45-57) is a tool to support pragmatic trial designs and helps define and evaluate the degree of pragmatism. The PRECIS tool has been further refined and now comprises nine domains each scored on a 5 point Likert scale ranging from very explanatory to very pragmatic with an exclusive focus on the issue of applicability (The PRECIS-2 tool: designing trials that are fit for purpose. BMJ 2015; 350: h2147).  A checklist and additional guidance is also provided in Improving the reporting of pragmatic trials: an extension of the CONSORT statement (BMJ 2008; 337 (a2390): 1-8).

 

5.6.2. Large simple trials

 

Large simple trials (LSTs) are pragmatic RCTs with minimal data collection protocols that are narrowly focused on clearly defined outcomes important to patients as well as clinicians. Their large sample size provides adequate statistical power to detect even small differences in effects. Additionally, LST's include a follow-up time that mimics normal clinical practice.

 

LSTs are particularly suited when an adverse event is very rare or with a delayed latency (with a large expected attrition rate), when the population exposed to the risk is heterogeneous (e.g. different indications and age groups), when several risks need to be assessed in the same trial or when many confounding factors need to be balanced between treatment groups. In these circumstances, the cost and complexity of a traditional RCT may outweigh its advantages and LSTs can help keep the volume and complexity of data collection to a minimum.

 

Outcomes that are simple and objective can also be measured from the routine process of care using epidemiological follow-up methods, for example by using questionnaires or hospital discharge records. LST methodology is discussed in Chapters 36 and 37 of the book Pharmacoepidemiology (Strom BL, Kimmel SE, Hennessy S. 5th Edition, Wiley, 2012), which includes a list of conditions appropriate for the conduct of a LST and a list of conditions which make a LST feasible. Examples of published LSTs are Assessment of the safety of paediatric ibuprofen: a practitioner based randomised clinical trial (JAMA 1995;279:929-33) and Comparative mortality associated with ziprasidone and olanzapine in real-world use among 18,154 patients with schizophrenia: The Zodiac Observational Study of Cardiac Outcomes (ZODIAC) (Am J Psychiatry 2011;168(2):193-201).

 

Note that the use of the term ‘simple’ in the expression ‘LST’ refers to data structure and not data collection. It is used in relation to situations in which a small number of outcomes are measured. The term may therefore not adequately reflect the complexity of the studies undertaken.

 

5.6.3. Randomised database studies

 

Randomised database studies can be considered a special form of an LST where patients included in the trial are enrolled in a healthcare system with electronic records. Eligible patients may be identified and flagged automatically by the software, with the advantage of allowing comparison of included and non-included patients. Database screening or record linkage can be used to detect and measure outcomes of interest otherwise assessed through the normal process of care. Patient recruitment, informed consent and proper documentation of patient information are hurdles that still need to be addressed in accordance with the applicable legislation for RCTs. Randomised database studies attempt to combine the advantages of randomisation and observational database studies. These and other aspects of randomised database studies are discussed in The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials (Health Technol Assess. 2014;18(43):1-146) which illustrates the practical implementation of randomised studies in general practice databases.

 

There are few published examples of randomised database studies, but this design could become more common in the near future with the increasing computerisation of medical records. Pragmatic randomised trials using routine electronic health records: putting them to the test (BMJ 2012;344:e55) describes a project to implement randomised trials in the everyday clinical work of general practitioners, comparing treatments that are already in common use, and using routinely collected electronic healthcare records both to identify participants and to gather results.

 

A particular form of randomised databases studies is the registry-based randomised trial, which uses an existing registry as platform for the identification of cases, randomisation and follow-up. The editorial Randomized Registry Trial — The Next Disruptive Technology in Clinical Research? (N N Engl J Med 2013; 369:1579-1581 ) introduces the concept. This hybrid design tries to achieve both internal and external validity by using a robust design (RCT) in a data source with higher generalisability (registries). Other examples are the TASTE trial that followed patients long-term using data from a Scandinavian registry (Thrombus aspiration during ST-segment elevation myocardial infarction. N. Engl J Med. 2013;369(17):1587-97) and A registry-based randomized trial comparing radial and femoral approaches in women undergoing percutaneous coronary intervention: the SAFE-PCI for Women (Study of Access Site for Enhancement of PCI for Women) trial (JACC Cardiovasc Interv. 2014 Aug). A potential limitation of randomised registry trials is the routine collection of data on outcomes needed for the trial, such as information on surrogate markers and adverse events.

 

 

Individual Chapters:

 

1. Introduction

2. Formulating the research question

3. Development of the study protocol

4. Approaches to data collection

4.1. Primary data collection

4.1.1. Surveys

4.1.2. Randomised clinical trials

4.2. Secondary data collection

4.3. Patient registries

4.3.1. Definition

4.3.2. Conceptual differences between a registry and a study

4.3.3. Methodological guidance

4.3.4. Registries which capture special populations

4.3.5. Disease registries in regulatory practice and health technology assessment

4.4. Spontaneous report database

4.5. Social media and electronic devices

4.6. Research networks

4.6.1. General considerations

4.6.2. Models of studies using multiple data sources

4.6.3. Challenges of different models

5. Study design and methods

5.1. Definition and validation of drug exposure, outcomes and covariates

5.1.1. Assessment of exposure

5.1.2. Assessment of outcomes

5.1.3. Assessment of covariates

5.1.4. Validation

5.2. Bias and confounding

5.2.1. Selection bias

5.2.2. Information bias

5.2.3. Confounding

5.3. Methods to handle bias and confounding

5.3.1. New-user designs

5.3.2. Case-only designs

5.3.3. Disease risk scores

5.3.4. Propensity scores

5.3.5. Instrumental variables

5.3.6. Prior event rate ratios

5.3.7. Handling time-dependent confounding in the analysis

5.4. Effect measure modification and interaction

5.5. Ecological analyses and case-population studies

5.6. Pragmatic trials and large simple trials

5.6.1. Pragmatic trials

5.6.2. Large simple trials

5.6.3. Randomised database studies

5.7. Systematic reviews and meta-analysis

5.8. Signal detection methodology and application

6. The statistical analysis plan

6.1. General considerations

6.2. Statistical analysis plan structure

6.3. Handling of missing data

7. Quality management

8. Dissemination and reporting

8.1. Principles of communication

8.2. Communication of study results

9. Data protection and ethical aspects

9.1. Patient and data protection

9.2. Scientific integrity and ethical conduct

10. Specific topics

10.1. Comparative effectiveness research

10.1.1. Introduction

10.1.2. General aspects

10.1.3. Prominent issues in CER

10.2. Vaccine safety and effectiveness

10.2.1. Vaccine safety

10.2.2. Vaccine effectiveness

10.3. Design and analysis of pharmacogenetic studies

10.3.1. Introduction

10.3.2. Identification of generic variants

10.3.3. Study designs

10.3.4. Data collection

10.3.5. Data analysis

10.3.6. Reporting

10.3.7. Clinical practice guidelines

10.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes