Real-world evidence (RWE) services are becoming increasingly important in the healthcare industry, as they offer insights into the effectiveness and safety of medical treatments in real-world settings. Our experienced team can provide valuable support and guidance in utilizing these services to improve patient outcomes and inform healthcare decision-making.
Developing evidence from real-world data (RWD) follows the same process as developing evidence from clinical trial data. It requires careful planning and proper execution. Like clinical trials, RWE starts with a statement of objectives and research questions. Planning differs because the source of the RWD must be identified, whether it be medical records, insurance claim data, or prospective observational studies.
Alimentiv Statistics business unit specializes in clinical statistical analysis, clinical trial design and data management of clinical research for pharmaceutical, biotechnology and medical device companies. Our extensive expertise, timely and accurate data and analytics, and service flexibility have significantly expedited time-to-market and lowered development costs for our clients’ clinical trials.
Depending on the study objectives, RWD might require special analytical methods. For example, if the objective involves determining cause and effect related to safety or effectiveness, then the lack of randomization to alternative treatments (including no treatment) might introduce confounding of causes. The RWE analysis must account for this possibility by using methods like propensity score matching or causal modeling. Our team will explain what is required for your study and discuss recommended options.
Because RWD is often collected for reasons other than research (e.g., medical records), preparing it for research requires skill and experience dealing with data sources that may not align properly, contain missing or discrepant values, and be poorly documented. Our statistical team have extensive experience analyzing and reporting real world and administrative data.
