The effect of institutional research activity on patient outcomes has not yet been investigated extensively, despite its great relevance to healthcare providers, policy makers, and patients. So far, only a few studies have examined the association between patient outcomes and institutional participation in clinical trials, as opposed to trial participation of individual patients
. Thus, the authors of the 2011 special issue of Annals of Oncology entitled “Clinical Research and Healthcare Outcomes: A Workshop at the International Agency for Research on Cancer, Lyon” agreed that further research on this topic is urgently required. If research activity has beneficial effects on patient outcomes, these benefits are not solely restricted to research participants.
Investigating the relationship between research activity at the level of institutions or providers and outcomes at the level of patients is not as easy as it seems at first glance. Researchers interested in this relationship are confronted with several practical challenges, such as getting data from less research-active institutions, as well as with several methodological challenges, for instance, the choice of the appropriate study design
. In addition, sole focus on establishing effects and measuring the potential benefits of research activity on healthcare outcomes is often considered insufficient
. The assessment of variables mediating the effects may be helpful in explaining the effects of exposure or in investigating the reasons why an exposure failed to yield an expected outcome. More importantly, knowledge of such mechanisms enables specific measures to improve healthcare at the individual level and at the institutional level—and such measures may be implemented even in the absence of exposure. Krzyzanowska et al.
 described a conceptual framework for understanding how institutional research activity might lead to better outcomes, even for patients who are not participants in a research project. The authors pointed out that the processes of care received by patients may have a strong impact on outcomes and that such processes may systematically differ between research-active and research-inactive settings. For example, institutions that actively participate in research may be more likely to follow clinical guidelines for cancer treatment. In addition, participation in research may facilitate early access to new treatment approaches, allowing the faster implementation of new evidence into practice.
The main purpose of the present work was to explore the mechanisms underlying the association between institutional research activity and patient outcomes. We first re-examined the association between institutional research activity and survival described by du Bois et al.
. Using the data of 352 patients with advanced ovarian cancer, we showed that hospital participation in clinical trials was associated with improved survival. The effect observed in our study was large and clearly relevant because of a relative reduction in the risk of death by 42% in favor of research activity. Whether this effect is in line with the literature is difficult to answer because, with respect to ovarian cancer, trial participation has hardly been investigated outside Germany. Furthermore, only a few studies have examined the association between patient outcomes and research activity at the level of health care institutions
As expected, patients with ‘optimal surgery’ and ‘optimal chemotherapy’ lived longer than patients who were not treated according to the standard of care. In addition, patients treated in trial hospitals were more likely to receive optimal treatment than patients treated in non-trial hospitals. We thus confronted the question of how much of the effect of hospital participation in clinical trials on patient survival was mediated through optimal debulking and optimal chemotherapy selection. To answer this question, we used a recently developed methodology for assessing mediation in the context of a survival analysis
. Taking into account several known baseline confounders, the overall hazard ratio (total effect) of 0.58 was decomposed into a direct effect of research activity of 0.67 and two indirect effects of 0.93 each mediated through surgery and chemotherapy. The aggregate indirect effect through both mediators was 0.87, that is, about 26% of the beneficial effect of research activity was mediated through both surgery and chemotherapy. In conclusion, trial participation of a hospital contributed at least partially to a superior outcome through the better quality of treatment provided.
The probability of surviving ovarian cancer depends on (1) patient characteristics (e.g. age), (2) tumor biology (e.g. stage), and (3) the quality of treatment (e.g. surgical outcome, selection of chemotherapy regimen). The first two factors are hard to change but the quality of treatment is susceptible to direct influence and thus seems to be of utmost relevance when considering efforts to improve the outcome of this disease. The implementation of standards into clinical routine in our study was not satisfactory and still needs improvement; however, taken both mediators together, treatment standards were more strictly implemented in trial hospitals than in non-trial hospitals. The good news is that clinicians can influence the implementation of standards, regardless of whether they are employed in research-active hospitals or not.
The main limitation of our study is the lack of randomization of patients into research-active and research-inactive hospitals. Such a randomized controlled trial would facilitate a clear causal interpretation but would also be hard to implement. When randomization is not possible, well-conducted observational studies can provide the necessary data to guide the future development of clinical research and healthcare. In particular, mediation analysis can help explore the mechanisms by which research activity leads to the outcome of interest. The key assumption for mediation analysis is that all relevant confounders are included into the analysis. To be more precise, the approach proposed by Lange et al.
 requires that the considered variables are sufficient for controlling the confounding of 1) the exposure-outcome relation, 2) the exposure-mediator relation, and 3) the mediator-outcome relation. We addressed this issue by incorporating all established prognostic factors into the models for the mediator and the outcome. In particular, age at diagnosis, FIGO stage, ECOG performance status, volume of ascites, histology, grade, and comorbidity are well-known prognostic factors for survival in ovarian cancer
[22–24]. These factors also influence treatment recommendations.
There must be no other variables (measured or unmeasured) that confound the mediator-outcome relation which are themselves affected by the exposure; this last assumption essentially ensures that each of the considered pathways between exposure and outcome does indeed represent a unique causal mechanism. In observational studies, these assumptions are inherently untestable and must instead be justified by means of knowledge about the biological processes under consideration. In our study, for example, we did not collect information on socioeconomic factors (e.g. income or insurance status). Patients with a higher socioeconomic status (SES) may find it easier and hence choose to travel to specialized or better rated hospitals or institutions that are research-active and participate in clinical trials. In contrast, cancer patients with a low SES may be less likely to choose research-active hospitals because of the lack of corresponding health care information. The total effect of research activity would then at least be partially due to better general survival rates of patients with a high SES. Lower socioeconomic status has occasionally been associated with lower likelihood of receiving surgery and chemotherapy but does not seem to be an independent prognostic factor for survival in ovarian cancer
Finally, the mediation analysis is only valid if the logistic regressions are adequate descriptions of the mediators. Sensitivity analyses showed that misclassification of the mediator will bias the estimates of the indirect effect towards one and the direct effect away from one. In our study, measurement error was minimized by objectively assessing the quality of chemotherapy and by evaluating surgical outcome (i.e. tumor residual) in a standardized manner.