The proposal for the pragmatic–explanatory continuum indicator summary (PRECIS) was developed by an international group of interested trialists at 2 meetings in Toronto (2005 and 2008) and in the time between. The initiative grew from the Pragmatic Randomized Controlled Trials in HealthCare (Practihc) project (www.practihc.org
), an initiative funded by Canada and the European Union to promote pragmatic trials in low- and middle-income countries.
The development of the PRECIS indicator began with the identification of key domains that distinguish pragmatic from explanatory trials. As illustrated in , they comprise:
- The eligibility criteria for trial participants.
- The flexibility with which the experimental intervention is applied.
- The degree of practitioner expertise in applying and monitoring the experimental intervention.
- The flexibility with which the comparison intervention is applied.
- The degree of practitioner expertise in applying and monitoring the comparison intervention.
- The intensity of follow-up of trial participants.
- The nature of the trial’s primary outcome.
- The intensity of measuring participants’ compliance with the prescribed intervention, and whether compliance-improving strategies are used.
- The intensity of measuring practitioners’ adherence to the study protocol, and whether adherence-improving strategies are used.
- The specification and scope of the analysis of the primary outcome.
During the 2005 meeting, 8 domains emerged during a brainstorming session. Furthermore, 5 mutually exclusive definitions were used to assign the level of pragmatism in each domain. Attempts to use the initial tool on a number of published trials revealed some difficulties. The mutually exclusive categories were technically difficult to understand and use, and in some cases contradictory among domains. The current approach, for the most part, is to consider a number of design tactics or restrictions consistent with an explanatory trial in each domain. The more tactics that are present, the more explanatory is the trial. However, these design tactics and restrictions (see “The domains in detail” section for some examples) are not equally important, so it is not a simple matter of adding up tactics. Where exactly to place a trial on the pragmatic–explanatory continuum is, therefore, a judgment best made by trialists discussing these issues at the design stage of their trial and reaching consensus. Initially, the domains for intervention flexibility and practitioner expertise addressed both the experimental and comparison interventions. Discussions at the 2008 meeting led to the separation of experimental and comparison interventions into their own domains and the replacement of a domain regarding trial duration with the domain related to the nature of the primary outcome.
At this point, a brief explanation of our use of some terminology may be helpful. In this paper, we view a trial participant as the recipient of the intervention. In many trials, the participants are patients. However, in a trial of a continuing education intervention, for example, the participants may be physicians. By practitioner we mean the person delivering the intervention. Again, for many trials the practitioners are physicians. For a continuing education intervention, the practitioners may be trained instructors.
We defined the purpose of a pragmatic trial as answering the question “Does an intervention work under usual conditions?,” where we take “usual conditions” to mean the same as, or very similar to, the usual-care setting. Characterizing the pragmatic extreme of each domain is less straight forward, since what is considered “usual care” may depend on context. For some interventions, what is usual for each domain may vary across different settings. For example, the responsiveness and compliance of patients, adherence of practitioners to guidelines, and the training and experience of practitioners may be different in different settings. Thus, characterizing the pragmatic extreme requires specifying the settings for which a trial is intended to provide an answer. Occasionally a pragmatic trial addresses a question in a single specific setting. For example, a randomized trial of interventions to improve the use of active sick leave was designed to answer a pragmatic question under usual conditions specific to the Norwegian context, where active sick leave was being promoted as a public sickness benefit scheme offered to promote early return to modified work for temporarily disabled workers.7
More often pragmatic trials will address questions across specific types of settings or across a wide range of settings. Examples of specific types of settings include settings where chloroquine-resistant falciparum malaria is endemic, where hospital facilities are in close proximity, or where trained specialists are available.
Conversely, we defined the purpose of an explanatory trial as answering the question “Can an intervention work under ideal conditions?” Given this definition, characterizing the explanatory extreme of each domain is relatively straightforward and intuitive. It simply requires considering the design decisions one would make in order to maximize the chances of success. Thus, for example, one would select patients that are most likely to comply and respond to the intervention, ensure that the intervention is delivered in a way that optimizes its potential for beneficial effects, and ensure that it is delivered by well-trained and experienced practitioners.
Thus, we recommend that trialists or others assessing whether design decisions are fit for purpose do this in 4 steps:
- Declare whether the purpose of the trial is pragmatic or explanatory.
- Specify the settings or conditions for which the trial is intended to be applicable.
- Specify the design options at the pragmatic and explanatory extremes of each domain.
- Decide how pragmatic or explanatory a trial is in relation to those extremes for each domain.
For some trials, there may not be any important difference between the pragmatic and explanatory extremes for some dimensions. For example, delivering an intervention, such as acetylsalicylic acid (ASA) therapy to someone with an acute myocardial infarction, does not require practitioner expertise. As mentioned earlier, for domains where the extremes are clear, it should not be difficult to decide whether a design decision is at one extreme or the other. For design decisions that are somewhere in between the extremes, it can be more challenging to determine how pragmatic or explanatory a trial will be. For this reason we recommend that all the members of the trial design team rate each domain and compare.
To facilitate steps 3 and 4, we have identified a number of design tactics that either add restrictions typical of explanatory trials or remove restrictions in the fashion of pragmatism. The tactics that we describe here are not intended to be prescriptive, exhaustive or even ordered in a particular way, but rather illustrative. They are to aid trialists or others in assessing where, within the pragmatic–explanatory continuum, a domain is, allowing them to put a “tick” on a line representing the continuum. To display the “results” of this assessment, the lines for each domain are arranged like spokes of a wheel, with the explanatory pole near the hub and the pragmatic pole on the rim (). The display is completed by joining the locations of all 10 indicators as we progress around the wheel.
The blank “wheel” of the pragmatic–explanatory continuum indicator summary (PRECIS) tool. “E” represents the “explanatory” end of the pragmatic–explanatory continuum.
The proposed scales seem to make sense intuitively and can be used without special training. Although we recognize that alternative graphical displays are possible, we feel that the proposed wheel plot is an appealing summary and is informative in at least 3 ways.
First, it depicts whether a trial is tending to take a broad view (as in a pragmatic trial asking whether an intervention does work, under usual conditions) or tending to be narrowly “focused” near the hub (as for an explanatory trial asking whether an intervention can work, under ideal conditions).
Second, the wheel plot highlights inconsistencies in how the 10 domains will be managed in a trial. For example, if a trial is to admit all patients and practitioners (extremely pragmatic) yet will intensely monitor compliance and intervene when it falters (extremely explanatory), a single glance at the wheel will immediately identify this inconsistency. This allows the researcher to make adjustments in the design, if possible and appropriate, to obtain greater consistency with their objective in conducting the trial.
Third, the wheel plot can help trialists better report any limitations in interpretation or generalization resulting from design inconsistencies. This could help users of the trial results make better decisions.