Analysing existing data from observational or randomised controlled trial (RCT) data can be a valuable method for identifying drivers of effectiveness.
What data should be analysed?
Dataset
The information analysed should contain the following:
- exposure to medicines (which medicine each study participant received, duration, dosing and method of administration)
- outcome (the medicine’s effect in the study participants)
- key characteristics related to potential drivers of effectiveness. These may relate to the actual use of medicines, the underlying disease, the study participants or the healthcare system
Source
A variety of data sources, such as electronic health records, observational studies and RCTs can be used to explore drivers of effectiveness.
However, the following considerations need to be made when choosing different data sources:
- Some sources, such as disease registries, may not contain complete information on exposure or key outcomes.
- RCTs may collect and report data on covariates for patients (e.g. age and gender) or the disease (e.g. duration of illness and baseline risk), but usually lack information on the healthcare system (e.g. the type of healthcare provider).
- The use of a medicine in an RCT is usually standardised (for dose, duration and adherence of patients) and does not necessarily represent the use of the medicine in real life. Observational studies may offer more information on the variation of patient and disease characteristics as well as variations in the use of medicines in routine practice. However, when using more heterogeneous data from such studies, larger samples are needed to allow enough statistical power for the analyses.
For general descriptions of different data sources and study types see Sources of Real-World Data and Generating Real-World Evidence.
Level of detail
Aggregate data are summaries of patient-level data from different sources and so may lack precision. Individual participant data (IPD) will usually be required to permit further analyses to identify drivers of effectiveness with sufficient precision.
What type of analyses should be run?
To determine the appropriate analyses to be undertaken, it is useful to first develop a conceptual model that describes the relationship between the exposure to a medicine, the outcome and any possible contextual factors (which may be drivers of effectiveness).
Figure. Contextual factors or ‘drivers of effectiveness’ interact in the association between a medicine and the medicine’s effect or outcome
Clearly specifying the exposure to a medicine associated with the reported outcome is important as there are different ways to define each of these.
Outcomes may be continuous (such as evolution of symptoms and biological parameters) or categorical (such as death and hospitalisation). A range of outcomes should be considered, as different drivers of effectiveness may be identified by looking at different outcome measures.
Literature reviews (see Conducting a Literature Review) or expert interviews (see Liaising with Clinical Experts) should be used to form hypotheses of potential drivers of effectiveness before conducting data analysis. This will help to avoid obtaining misleading findings.
The possibility that the association between exposure and outcome may vary in strength (possibly reversing) for different levels of the driver of effectiveness should be considered when designing analyses.
The figure below uses the example of medicines for schizophrenia to help visualise the conceptual model.
Figure. A conceptual model for schizophrenia
- In this example, the exposure is to drug A (vs. drug B – relative effectiveness), and the outcome is the evolution of schizophrenia symptoms (over a time period to be defined). Two potential drivers of effectiveness are analysed: adherence to medication and cannabis use. The figure also shows that adherence and cannabis use may be correlated.
GetReal case studies using data analyses to identify drivers of effectiveness
Review the following GetReal case studies presented in the following RWE Navigator pages:
- Patient-level drivers of effectiveness: a case study in schizophrenia
- Healthcare setting-level drivers of effectiveness: a case study in schizophrenia
Key contributors
Clementine Nordon, LASER
Robert Olivares, Sanofi
Mikkel Z Ankarfeldt, Novo Nordisk