Promoting tailored healthcare: improving methods to investigate subgroup effects in treatment response when having multiple individual participant datasets
Johannes Reitsma, Maroeska Rovers
Individuals differ in their response to treatment. Personalized or patient-centered healthcare involves tailoring therapeutic decisions to individuals based on patient and disease characteristics. The current widespread initiatives for sharing individual participant data (IPD) will create unique opportunities to investigate whether subgroups differ in their response to treatments. Benefits of using IPD rather than traditional meta-analysis arise from standardization of subgroups and outcomes across studies, the higher validity of subgroup findings through proper adjustment for differences in patient and study characteristics, and the increased possibilities to search for subgroups based on multiple characteristics.
In our recent papers in PlosMed, JAMA and BMJ we however identified several remaining challenges in IPD meta-analyses leading to diversity and uncertainty on how to perform and interpret subgroup analyses based on IPD. These gaps include: (i) lack of insight and evidence why and when various distinct statistical subgroup approaches lead to conflicting results; (ii) uncertainty how to combine IPD and aggregate data; (iii) unclear how to incorporate differences in design and population between studies; (iv) uncertainty how to best calculate the probability that a new trial will find the subgroup of interest. In view of the expected rise in open access data, guidance on appropriate methodology is urgently needed to improve the use and uptake of IPD meta-analyses and their findings.
Our overall aim is to evaluate and improve prevailing approaches, and to develop novel methods where needed, for investigating and interpreting subgroup effects in treatment response when multiple IPD sets are available. It should lead to more credible evidence about whether a relevant subgroup effect exists.