Access to full, appropriately de-identified datasets from clinical trials can benefit the biopharmaceutical industry by improving the efficiency of drug development, enhancing comparative-effectiveness analyses and reducing duplication of effort among trial sponsors.
1. Not sustainable
Industry is right to be concerned about the sustainability of the existing drug-development and business model.
In Pharma Industry, the timelines and costs of clinical drug development are increasing relentlessly, and the attrition rate of assets in development remains high.
At the same time, growing cost pressures in all healthcare environments are forcing restrictions on drug use, aiming to limit coverage only to patients who can be expected to benefit from a given intervention and for whom that intervention is clearly cost-effective.
2. Design and analysis
Access to the full datasets of completed clinical studies would, in the first place, lead to improvements in the design and analysis of subsequent trials.
To quote an example, available information about numerous variables can be used to identify and validate prognostic factors. Relevant validated prognostic factors can then be selected for use in the stratification of subsequent trials to reduce unwanted variability, minimise type I and type II error rates, and inform pre-specification of statistical modelling and subgroup analyses.
The identification and validation of factors that predict treatment response also enable active sampling or population enrichment in subsequent clinical trials, to avoid having a treatment appear ineffective because the trial has been conducted in a diluted population.
Enrichment can reduce sample sizes as it makes larger treatment effects easier to detect.
The inclusion of patient-level data can also generate comprehensive, quality-controlled databases with potential to inform future research projects and questions.
3. Heterogeneity of effects
Moreover, lessons from past clinical trials about the heterogeneity of treatment effects will not only streamline drug development but may also enhance a drug’s value in the marketplace.
Identifying a population with high unmet need in which a new treatment may be more cost-effective than other available options can help sponsors during reimbursement negotiations.
4. Comparative effectiveness
They also emphasise the growing importance of comparative-effectiveness insights to patients, prescribers and positioning of new medicines.
Data from individual patients on both outcomes and co-variates can alleviate some of the weaknesses of this approach, such as the need to make assumptions about heterogeneity and consistency of effect on the basis of the summary data that are currently in the public domain.
Wider access to patient-level data from clinical trials will allow sponsors to present more robust comparative-effectiveness information about their product soon after licensing and at a very limited cost compared with head-to-head trials.
5. Doomed from the outset
Finally, one of the “inherent inefficiencies” of data secrecy is the repetition of trials and projects that are doomed from the outset.
Drug developers may continue to pursue a given target even though clinical trials conducted by others have demonstrated the effort’s futility. With patient health at risk and limited resources for research, the high opportunity cost of clinical-data firewalls is difficult to justify.
6. Addressing concerns
Given the array of potential uses for patient-level data in facilitating research and development, it is surprising that few drug developers have been sharing data voluntarily.
Commonly voiced concerns, they note, include the risk of jeopardising patient privacy, of clinical trials being misinterpreted due to “inappropriate” analyses, and of commercially confidential information being disclosed to competitors.
Secondary analyses
While a “truly open” approach to clinical-trial data does carry a risk of inappropriate secondary data analysis and conclusions, this risk “exists for any type of secondary analysis, regardless of the nature of the data.
Clearly, though, legitimate interests in intellectual property and the protection of private investments “must be weighed against other legitimate interests, such as transparency regarding the outcomes of clinical trials and the protection of public health”.
Striking the right balance of these interests, they insist, “is a duty for all responsible stakeholders involved, not just for regulators.
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