Generating evidence from computerized healthcare utilization databases.

Randomized clinical trials (RCTs) are regarded as the highest level of therapeutic evidence because they are based on random allocation of participants to ≥2 treatment groups, which provides patients with superimposable initial demographic and clinical characteristics and allows the results to reflect the effect of the treatment strategies under study safely. Also, new treatments are compared with placebo or current interventions , which offers information on their absolute or added value. Finally, end points of documented clinical relevance are selected, the results having an immediate bearing for patients' health. However, RCTs also have limitations that can make their results of uncertain and limited application to daily life medicine. 1–4 For example, in RCTs, treatments are delivered according to preselected plans that make management more rigid than the one adopted in real-life. Two, treatments are delivered in a highly controlled environment by operators with specific competence, which results into a much lower chance of mistreatment or errors. Three, for several reasons (cost, progressive patients' dropout, changes of patient residence , job instability of investigators, etc), RCTs can have a few year duration only, extrapolation been required to apply their results to daily life patients with a much longer life expectancy. Four, to make data scientifically interpretable, in RCTs, vulnerable patients are usually avoided and patients are recruited based on restricted eligibility criteria, which do not reflect the demographic and clinical heterogeneity of the individuals to whom the trial results are eventually applied. Finally, in RCTs, high motivation and close follow-up make patients well compliant to treatment, at variance from clinical practice in which a low and variable treatment adherence is common, with unmeasured but probably substantial modifications of the original trial results. Recognition of the above limitations has favored the design and conduction of trials, which could may more appropriately reflect clinical practice. One example is the expansion of the end points by which to determine the efficacy of the intervention to events (eg, revascularization procedures) diagnostically more open to errors or bias, but nevertheless of a large prevalence and decision relevance in real life. 13 Furthermore, to ensure a better generalizability of the results, the so-called pragmatic trials are more and more frequently performed, with the aim of selecting patients more similar to those in whom the trial results are applied. 14 However, it is widely thought that this does not substantially reduce the gap between the artificial environment where …