Are there roles for observational database studies and structured quantification of expert opinion to answer therapy controversies in transplants?

Approaches to determine whether one transplant-related therapy is better than another include: (1) using experimental data, such as those from randomized controlled trials (RCTs); (2) using observational data, such as those from observational databases (ODBs) and (3) using conclusions from the structured quantification of expert opinion based on a consideration of evidence from RCTs, ODBs and other sources. Large RCTs are widely and appropriately regarded as the gold standard of clinical investigation. However, data from large RCTs are rarely available for transplant-related therapy questions. We discuss some of the limitations of RCTs in the transplant setting often including small size and short follow-up. These limitations are only partly solved by meta-analyses of RCTs. Data from high-quality ODBs are not only often useful in this setting but also have limitations. Biases may be difficult or impossible to identify and/or adjust for. However, ODBs have large numbers of diverse subjects receiving diverse therapies and analyses that often give answers more useful to clinicians than RCTs. Side-by-side comparisons suggest analyses from high-quality ODBs often give similar conclusions to meta-analyses of high-quality RCTs. Meta-analyses combining data from RCTs and ODBs are sometimes appropriate. Quantitation of expert opinion, when of high quality, is also useful: experts rarely disagree under precisely defined circumstances and their consensus conclusions are often concordant with results of high-quality RCTs and ODBs. We suggest increased use of ODBs and expert opinion as reliable and effective ways to determine relative efficacies of new therapies in transplant settings.