Trial results may be difficult to interpret because of different (possibly inconsistent) efficacy results across the pivotal studies. This may be due to differences in the trial populations associated with differences in efficacy across important sub-populations.
Results for an individual trial may vary in magnitude and/or direction for different outcomes, making interpretation less straightforward, for example when reviewing results for individual components of composite endpoints.
Trial results may be difficult to interpret because of large uncertainty (wide confidence intervals) in the outcome measures. Trials may not have been powered specifically to detect differences in patient-relevant endpoints such as health-related quality of life. In some healthcare systems, secondary and tertiary outcomes may be considered to be lower quality evidence. High levels of uncertainty may be reported if there are lower rates of the outcome event than expected. Results for subgroups of importance to the health system of interest will have greater uncertainty, and in some cases may not have been reported.
Outcomes reported in pivotal trials may not be considered to be measures of relative effectiveness from a health technology assessment (HTA) or reimbursement perspective. These outcomes (efficacy or safety) are selected to meet the needs of regulatory approval, but may not be optimal for some HTA agencies. A number of factors may be relevant. Trial outcomes may represent physiological parameters, such as tumour response, blood haemoglobin level or lung function, which are not considered to be patient-relevant. However, these may serve as surrogate endpoints (proxies) for effectiveness outcomes of relevance to HTA, but the relationship between the surrogate and ‘final’ endpoint needs to be demonstrated quantitatively. Outcomes that are clinically assessed disease activity indices may be considered measures of effectiveness if they are validated and are widely used. Outcomes that are composite endpoints (for example, MACE: major adverse cardiac events) may need to be disaggregated into their components for consideration in HTA.
Current usual care (or standard of care) for the healthcare system of interest is not included as a comparator in the clinical trial. This may be because there is wide variation in usual care across healthcare systems, so that not all options can be included in a single study. A ’usual care’ medicine in the country of interest may not be licensed or reimbursed, or it may not be recommended for use (for example, in clinical guidelines) in some study countries, preventing its inclusion in the trial. In some cases a placebo-controlled trial may have been required to support regulatory approval, for example to resolve safety concerns, which might preclude the inclusion of usual care as a comparator in the trial. It is possible that more than one usual care comparator is relevant for different segments of the target population, for example if a new diagnostic paradigm is involved.
Any comparison with usual care based on clinical trial data will therefore need to rely on an indirect comparison, for example a network meta-analysis based on an evidence network of results from all trials in populations with the disease of interest. Such analyses depend on statistical modelling assumptions (mostly concerning heterogeneity of the results across the source trials) as well as similarity in the design of the source trials (for example, study durations, study populations and definitions of health outcomes). Results of such meta-analyses may be associated with high levels of uncertainty. They are viewed with caution by some decision-makers because they are quite new (not yet fully ‘tried and tested’), are quire complex (loss of transparency) and are not yet widely understood.
The disease/indication may have an imprecise definition, and so there may be no well-established criteria for defining the population of interest (likely to receive the new intervention). A clinical code (ICD or other) may not yet have been established for the disease entity. Correspondingly there may be a lack of clinical guidelines, and usual care (or standard of care) may not be well established for the target population within an agreed treatment pathway. This may lead to uncertainty in applying trial results to the local context, as well as subsequent difficulties in setting up studies to monitor the usage and effectiveness of the medicine post-launch.
Effectiveness results from previous or concurrent studies of comparator therapies (including usual care or standard of care) are potentially useful for planning new studies, and for inclusion in indirect comparisons or meta-analyses that include results for the new medicine of interest. However, the populations included in these studies may have different characteristics to the target population for the new medicine. Differences may be related to demographics, risk profile, place in the treatment pathway, concomitant care received or referral practices. These may be a result of secular changes in patient management since these data were reported. As a result there may be variations in effectiveness reported for usual care in such trials or non-randomised studies, which may present problems for trial design (for example, using expected event rates to calculate adequate study size) as well as for meta-analyses synthesising results from relevant trials.
‘Treatment pathway’ refers to the sequence of previous treatments received, based on patient selection criteria and response to previous therapies, often reflected in clinical guidelines. The position of patients in the treatment pathway drives the choice of appropriate comparator.
The position in the treatment pathway occupied by trial participants may differ from that which they would occupy in usual practice in the healthcare system of interest. For example, in usual practice the new medicine may be considered for use (only) after the disease progressed on two types of therapy, whereas trial participants may have experienced disease progression on just one. This assumes that the pathway has not changed during the course of the trial.
Application of trial inclusion/exclusion criteria, together with other factors affecting recruitment, may mean that the trial population does not coincide with the population likely to be treated locally in usual practice. There may be differences in the distribution by age, gender, ethnicity, socio-economic factors, co-morbidities or other factors. Firstly, this is of concern if the intervention of interest has different efficacy across these factors. Secondly, even in the absence of such efficacy differences, the risk profile of the study population for the outcome of interest may differ from that of the population likely to be treated in usual practice. In this case there may be different levels of absolute effect (events averted etc.) associated with levels of trial-reported efficacy for the different populations.
