1. Administration of therapy is inconsistent with usual practice
The administration of the study medicine in trials (for example, dose, dose titration/escalation, frequency, route of administration, monitoring) may differ from the schedule that is likely to be used in clinical practice. This is perhaps more likely if the new therapy is added to current usual care, or if there is a risk that the treatment itself may be intentionally misused by patients if strict controls are not used (for example, opioids in pain relief). In some situations the clinical background and skill level of the administering clinicians may be important.
2. Stopping rules for therapy are unclear
Health technology assessment (HTA) agencies and healthcare payers are interested in knowing when new therapies should cease to be used by patients, for example due to lack of response. or another measure of efficacy, and potentially when there is no likely patient benefit to patients even in the presence of continuing efficacy. ‘Stopping rules’ may not be well defined in clinical practice (especially clinical guidelines), and further evidence for such rules may not available from the clinical data. Where stopping rules are used in the clinical trials, these may not match those used/which can be applied in local clinical practice.
3. Adherence in study differs from usual practice
Outcomes reported in trials are usually for study participants who maintain a high level of adherence to the study medicine and comparator therapies. However, in usual practice lower levels of adherence are expected. There may be different levels of adherence for different therapy options, resulting from different side effect profiles or differences in methods of administration. In the absence of real-world evidence of effectiveness, an understanding of the relationship between effectiveness and adherence may be required in order to project estimates of effectiveness in usual practice (i.e. with sub-optimal adherence) from the efficacy results reported in trials. This relationship is likely to be non-linear, and may be difficult to predict: having real-world data on adherence alone is generally insufficient to estimate effectiveness.