[This is the final blog in the PRO series that takes the conversation forward from the challenges of capturing reliable and validated PRO data to the complexities involved in its analysis and reporting.]
Along with challenges in capturing reliable and validated PRO data, there are also complexities in its analysis, reporting and interpretation. Adherence to blinding and randomisation requirements is critical in order for analysis of PRO endpoints to be valid, given the subjective nature of measurement.
When PROs are included as endpoints in a study, they can be primary or secondary endpoints. Multiple comparisons are invariably required when PRO endpoints are included. Appropriate statistical methods have to be applied to handle multiplicity and to make adjustments to control the overall Type I error rate.
A PRO instrument includes multiple domains and hence composite endpoints often need to be defined. Analysis of composite endpoints and interpretation of results is challenging, especially when inference has to be made on individual components of the composite endpoint.
PRO data is also usually longitudinal in nature and mixed effects models have to be used for statistical analysis of the data. Incompleteness of data is often a challenge in case of longitudinal data and composite endpoints and it increases the likelihood of bias in the results. Hence imputation and sensitivity analysis is required.
Clinical interpretation of results and assessment of clinical significance can also be challenging. Considering that PROs measure the well-being of patients, cross-cultural comparability of data can be contentious even when validated translated versions of the PRO instrument are used. The CONSORT (Consolidated Standards of Reporting Trials) lacks guidance on the reporting of PROs, which is addressed through the development of the CONSORT PRO extension based on the methodological framework for guidelines proposed by the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network. Improved reporting of PRO data will facilitate robust interpretation of the results from clinical studies and informed patient care.
It therefore becomes imperative to use the right skills to design, analyse and report PRO endpoints. Outcomes research is often a specialized and separate group within the R&D or commercial organizations of pharmaceutical companies. With the surge in the volume of Outcomes Research (OR) data, especially in the post-approval phase, sponsor organizations often outsource some of the analysis. Many CROs (contract research organizations) and other niche providers have been building expertise in handling PRO data to cater to this need. Outsourcing to the right provider gives an upper hand to the sponsor organization and enables the scientists and clinicians to overcome the challenges of the time, resource and skill required to interpret and present complex data in an effective and efficient manner.