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• W2169558520 abstract "Fundamental to evidence-based health care is the concept of ‘hierarchy of evidence’, deriving from different study designs addressing a given research question (Figure 1). Evidence grading is based on the idea that different designs vary in their susceptibility to bias and, therefore, in their ability to predict the true effectiveness of health care practices. For assessment of interventions, randomized controlled trials (RCTs) or systematic review of good quality, RCTs are at the top of the evidence pyramid, followed by longitudinal cohort, case-control and cross-sectional studies [2,3]. However, the choice of the study design depends on the question at hand, the nature of the exposure and the frequency of the disease. Intervention questions are ideally addressed with experiments (RCTs), since observational data are prone to unpredictable bias and confounding that only the randomization process will control [1]. Appropriately designed RCTs allow also stronger causal inference for disease mechanisms. However, ideal RCTs cannot be implemented in the same way to answer all intervention questions. Some therapies can even not be masked or randomly assigned (e.g. dialysis modalities). In circumstances where the ‘intervention’ is clearly identified and easily applied, such as the use of a new oral medication to reduce proteinuria, both internal and external validity can be reasonably maximized using standard approaches (limited exclusion criteria, multiple blinding, minimization of missing data and dropouts). In contrast, when the intervention is aimed at achieving a clinical target, such as haemoglobin or blood pressure levels, many treatment adjustment decisions are often left to the discretion of the treatment team during the trial, blinding may be difficult to maintain and patients are often exposed to multiple strategies (e.g. iron supplementation, erythropoietic agents, antihypertensive medications in studies of haemoglobin targets). In such cases, practitioners may be left with uncertainty as to what aspect of the intervention led to the observed trial results. For example, if higher cardiovascular event rates were associated with aiming for higher haemoglobin targets, it might be unclear whether this was due to the dose of erythropoietic agents employed, the amount of iron given or indeed the interaction between these factors and characteristics of the trial subjects. Those at higher baseline cardiovascular risk might be more difficult to get to target and particularly susceptible to the adverse effects associated with higher doses of iron and erythropoietic agents given in an effort to achieve those targets. However, understanding these relationships as a result of a trial, particularly if confirmed in further research helps inform practitioners on how to best individualize the application of therapy. Prognostic and aetiologic questions are best addressed with longitudinal cohort studies, in which exposure is measured first and participants are followed forward in time. At least two (and possibly more) waves of measurements over time are undertaken. Initial assessment of an input–output relationship may derive from case-control studies, where the direction of the study is reversed. Participants are identified by the presence or absence of disease and exposure is assessed retrospectively. Cross-sectional studies may be appropriate for an initial evaluation of the accuracy of new diagnostic tests as compared to a gold standard. Correspondence and offprint requests to: Pietro Ravani, MD, Divisione di Nefrologia, Azienda Istituti Ospitalieri di Cremona, Italy, Largo priori 1, Cremona, 26100, Italy. Email: p.ravani@ospedale.cremona.it Nephrol Dial Transplant (2007) 22: 2785–2794" @default.
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• W2169558520 date "2007-07-07" @default.
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• W2169558520 title "Clinical research of kidney diseases II: problems of study design" @default.
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• W2169558520 doi "https://doi.org/10.1093/ndt/gfm433" @default.
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