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Rutger A Middelburg,1,2 M Sesmu Arbous,2,3 Judith G Middelburg,4 Johanna G van der Bom1,2 1Center for Clinical Transfusion Research, Sanquin Research, Leiden, the Netherlands; 2Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; 3Department of Intensive Care Medicine, Leiden University Medical Center, Leiden, the Netherlands; 4Department of Radiation Oncology, Erasmus University Medical Center, Rotterdam, the Netherlands Abstract: Evidence-based medicine has become associated with a preference for randomized trials. Randomization is a powerful tool against both known and unknown confounding. However, due to cost-induced constraints in size, randomized trials are seldom able to provide the subgroup analyses needed to gain much insight into effect modification. To apply results to an individual patient, effect modification needs to be considered. Results from randomized trials are therefore often difficult to apply in daily clinical practice. Confounding by indication, which randomization aims to prevent, is caused by more severely ill patients being less or more likely to be treated. Therefore, the prognostic indicators that physicians use to make treatment decisions become confounders. However, these same prognostic indicators are also effect modifiers. This is in fact exactly why they are relevant to decision-making. We use simple, fictive numerical examples to illustrate these concepts. Then we argue that if we would record all relevant variables, it would simultaneously solve the problem of confounding by indication and allow quantification of effect modification. It has previously been argued that it is practically more feasible to “simply” randomize treatment allocation, than to adequately correct for confounding by indication. We will argue that, in the current age of evidence-based medicine and highly regulated randomized trials, this balance has shifted. We therefore call for better observational clinical research. However, careless acceptance of results from poorly performed observational research can lead clinicians seriously astray. Therefore, a more interactive approach toward the medical literature might be needed, where more room is made for scientific discussion and interpretation of results, instead of one-way reporting. Keywords: treatment, personalized, effectiveness, effect modification, risk factors, confounding by indication

Rutger A Middelburg,1,2 Jean-Louis H Kerkhoffs,1,3 Johanna G van der Bom1,2 1Center for Clinical Transfusion Research, Sanquin Research, Leiden, the Netherlands; 2Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; 3Department of Hematology, Hagaziekenhuis, Den Haag, the Netherlands Background: Hematology–oncology patients often become severely thrombocytopenic and receive prophylactic platelet transfusions when their platelet count drops below 10×109 platelets/L. This so-called “platelet count trigger” of 10×109 platelets/L is recommended because currently available evidence suggests this is the critical concentration at which bleeding risk starts to increase. Yet, exposure time and lag time may have biased the results of studies on the association between platelet counts and bleeding risks.Methods: We performed simulation studies to examine possible effects of exposure time and lag time on the findings of both randomized trials and observational data.Results: Exposure time and lag time reduced or even reversed the association between the risk of clinically relevant bleeding and platelet counts. The frequency of platelet count measurements influenced the observed bleeding risk at a given platelet count trigger. A transfusion trigger of 10×109 platelets/L resulted in a severely distorted association, which closely resembled the association reported in the literature. At triggers of 0, 5, 10, and 20×109 platelets/L the observed percentages of patients experiencing bleeding were 18, 19, 19, and 18%. A trigger of 30×109 platelets/L showed an observed bleeding risk of 16% and triggers of 40 and 50×109 platelets/L both resulted in observed bleeding risks of 13%.Conclusion: The results from our simulation study show how minimal exposure times and lag times may have influenced the results from previous studies on platelet counts, transfusion strategies, and bleeding risk and caution against the generally recommended universal trigger of 10×109 platelets/L. Keywords: platelet transfusions, platelet counts, bleeding, simulation study, lag time, exposure time