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• W2084286036 abstract "A common frustration for practicing Nephrologists is the adage that the lack of randomized controlled trials (RCTs) does not allow us to establish causality, but merely associations. The field of nephrology, like many other disciplines, has been suffering from a lack of RCTs. The view that without RCTs, there is no reliable evidence has hampered our ability to ascertain the best course of action for our patients. However, many clinically important questions in medicine and public health, such as the association of smoking and lung cancer, are not amenable to RCTs owing to ethical or other considerations. Whereas RCTs unquestionably hold many advantages over observational studies, it should be recognized that they also have many flaws that render them fallible under certain circumstances. We provide a description of the various pros and cons of RCTs and of observational studies using examples from the nephrology literature, and argue that it is simplistic to rank them solely based on preconceived notions about the superiority of one over the other. We also discuss methods whereby observational studies can become acceptable tools for causal inferences. Such approaches are especially important in a field like nephrology where there are myriads of potential interventions based on complex pathophysiologic states, but where properly designed and conducted RCTs for all of these will probably never materialize. A common frustration for practicing Nephrologists is the adage that the lack of randomized controlled trials (RCTs) does not allow us to establish causality, but merely associations. The field of nephrology, like many other disciplines, has been suffering from a lack of RCTs. The view that without RCTs, there is no reliable evidence has hampered our ability to ascertain the best course of action for our patients. However, many clinically important questions in medicine and public health, such as the association of smoking and lung cancer, are not amenable to RCTs owing to ethical or other considerations. Whereas RCTs unquestionably hold many advantages over observational studies, it should be recognized that they also have many flaws that render them fallible under certain circumstances. We provide a description of the various pros and cons of RCTs and of observational studies using examples from the nephrology literature, and argue that it is simplistic to rank them solely based on preconceived notions about the superiority of one over the other. We also discuss methods whereby observational studies can become acceptable tools for causal inferences. Such approaches are especially important in a field like nephrology where there are myriads of potential interventions based on complex pathophysiologic states, but where properly designed and conducted RCTs for all of these will probably never materialize. Clinical Summary•Properly performed randomized controlled trials can offer the most certainty regarding causal inference.•Sometimes, even randomized controlled trials can have flaws that make them ill-suited to provide definitive answers for clinical dilemmas.•Under certain circumstances, the level of evidence from observational studies can approach that of randomized controlled trials.A frequent lament in nephrology is the lack of randomized controlled trials (RCTs), and thus the inability to conclusively establish a cause-and-effect relationship between an exposure and an outcome. This has led to some professional guidelines concluding that no firm recommendations can be made about the utility of many therapeutic interventions,1KDIGO clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD).Kidney Int Suppl. 2009; 76: S1-S130Crossref Google Scholar causing significant confusion and disappointment among the rank and file. The prevailing view is that observational studies are in many ways flawed, and thus inferior, and their main utility is to generate hypotheses that can then be tested in RCTs, the crown jewels of investigative science. This mentality has resulted in a frustrating stalemate in many disciplines, including nephrology, where the low number of RCTs has limited the scope of many therapeutic interventions to short-term goals of correcting biochemical abnormalities without targeting clinical outcomes such as morbidity or mortality. The negative results of several recent large RCTs2Besarab A. Bolton W.K. Browne J.K. et al.The effects of normal as compared with low hematocrit values in patients with cardiac disease who are receiving hemodialysis and epoetin.N Engl J Med. 1998; 339: 584-590Crossref PubMed Scopus (1884) Google Scholar, 3Drueke T.B. Locatelli F. Clyne N. et al.Normalization of hemoglobin level in patients with chronic kidney disease and anemia.N Engl J Med. 2006; 355: 2071-2084Crossref PubMed Scopus (1788) Google Scholar, 4Pfeffer M.A. Burdmann E.A. Chen C.Y. et al.A trial of darbepoetin alfa in type 2 diabetes and chronic kidney disease.N Engl J Med. 2009; 361: 2019-2032Crossref PubMed Scopus (1653) Google Scholar, 5Singh A.K. Szczech L. Tang K.L. et al.Correction of anemia with epoetin alfa in chronic kidney disease.N Engl J Med. 2006; 355: 2085-2098Crossref PubMed Scopus (2284) Google Scholar, 6Fellstrom B.C. Jardine A.G. Schmieder R.E. et al.Rosuvastatin and cardiovascular events in patients undergoing hemodialysis.N Engl J Med. 2009; 360: 1395-1407Crossref PubMed Scopus (1604) Google Scholar, 7Wanner C. Krane V. Marz W. et al.Atorvastatin in patients with type 2 diabetes mellitus undergoing hemodialysis.N Engl J Med. 2005; 353: 238-248Crossref PubMed Scopus (2212) Google Scholar, 8Suki W.N. Zabaneh R. Cangiano J.L. et al.Effects of sevelamer and calcium-based phosphate binders on mortality in hemodialysis patients.Kidney Int. 2007; 72: 1130-1137Crossref PubMed Scopus (442) Google Scholar, 9Jamison R.L. Hartigan P. Kaufman J.S. et al.Effect of homocysteine lowering on mortality and vascular disease in advanced chronic kidney disease and end-stage renal disease: a randomized controlled trial.JAMA. 2007; 298: 1163-1170Crossref PubMed Scopus (404) Google Scholar could result in an even more cautious attitude toward designing and conducting such investigations; hence, it is likely that nephrologists’ frustrations with the level of evidence regarding the efficacy of many therapeutic interventions will probably persist for some time. Under such circumstances, it is worthwhile reexamining our present paradigm that declares that observational studies and, to some extent, laboratory experiments are merely laying the groundwork for RCTs (Fig. 1). We shall thus examine the pros and cons of observational studies vis-a-vis to RCTs along with various ways that could allow the former to be regarded as high-level evidence. •Properly performed randomized controlled trials can offer the most certainty regarding causal inference.•Sometimes, even randomized controlled trials can have flaws that make them ill-suited to provide definitive answers for clinical dilemmas.•Under certain circumstances, the level of evidence from observational studies can approach that of randomized controlled trials. All exploration starts with careful observation of the world around us, and scientists have used observations for hundreds of years to explain why events such as diseases occur. Rickets, for example, which is now believed to be caused by vitamin D deficiency, became common during the industrial revolution as a result of the “modern” lifestyle of new city dwellers that resulted in significantly decreased exposure to sunlight. In 1822, Jedrzej Sniadecki, a Polish physician working at the University of Wilno proposed that the new lifestyle was linked to rickets and recommended exposure to open air and sunshine for the cure of the “English disease.”10Sniadecki J. In: Dziela. Warsaw, Poland; 1840:273–274.Google Scholar He thus established an association between the observed lifestyle and a disease and proposed an experiment (which could have taken the form of an RCT) to test the hypothesis that the lifestyle caused the disease. Almost 200 years later, we now know how ultraviolet radiation generates vitamin D, which in turn affects bone health; however, purely based on the association between lifestyle and rickets, one cannot necessarily claim that the lack of sunshine was the proximal cause of the disease. An equally plausible hypothesis could have been that the disease itself caused a change in lifestyle, as patients with rickets were likely to be less mobile and hence more likely to be homebound, a phenomenon called reverse causation. One could also (maybe less plausibly) hypothesize that both rickets and reclusive lifestyle have a common source, which would then confound the association between the 2 and create a false sense that they are somehow linked. It is thus clear that one has to be careful when interpreting associations, as they are not always due to causal relationships. It is also true that in the above example, an RCT to test the hypothesis about sunshine’s role in the disease process can provide more conclusive evidence, especially given knowledge of the condition’s pathophysiology. But what if we are faced with a condition for which designing an RCT is not feasible, or is unethical, such as examining the association of tobacco smoking and lung cancer? Can we then turn to other types of investigations to establish causality? Are there certain rules that determine to what extent an observation can be considered valid and unbiased? There have been numerous attempts throughout history to codify the rules that establish the validity of a scientific investigation. A formal set of rules to describe valid experimentation and drug testing was established as early as 1025 AD by the Persian physician and philosopher Ibn Sina (Avicenna) in his work The Canon of Medicine.11Brater D.C. Daly W.J. Clinical pharmacology in the middle ages: principles that presage the 21st century.Clin Pharmacol Ther. 2000; 67: 447-450Crossref PubMed Scopus (41) Google Scholar, 12Kalantar-Zadeh K. Navab M. The importance of origins?.Science. 2005; 309: 1673-1675Crossref PubMed Google Scholar More relevant to the topic discussed herein was a later effort by Sir Austin Bradford Hill, who established a number of criteria, which, if fulfilled, would strengthen the possibility of a causal relationship.13Hill A.B. The environment and disease: association or causation?.Proc R Soc Med. 1965; 58: 295-300PubMed Google Scholar As shown in Table 1, these criteria can by no means be applied as a simple checklist to achieve logical proof of causality. Short of maybe the criterion of temporal sequence (ie, the exposure has to precede the disease), none of the others (including experimentation) can be construed as both necessary and sufficient to prove causality beyond any doubt.Table 1Hill’s Criteria of Causal Inference in EpidemiologyCriterionDefinitionCounterargument1.Temporal relationshipExposure precedes the disease.None2.Strength of associationThe stronger the association, the more likely it is that the relation is causal.Weak associations do not rule out causality. Furthermore, the presence of a strong unmeasured confounder could result in strong associations without true causation. Strength of association should not be mistaken for statistical significance.3.Dose responseIncreasing amount of exposure increases the risk proportionally.Causality can also be present with mechanisms that have threshold effects, in which case, increasing exposure would not lead to further increase in effect. It is also possible that the presence of an unmeasured confounder could change the shape of the dose–effect curve in spite of a true cause–effect relationship.14Shinaberger C.S. Kopple J.D. Kovesdy C.P. et al.Ratio of paricalcitol dosage to serum parathyroid hormone level and survival in maintenance hemodialysis patients.Clin J Am Soc Nephrol. 2008; 3: 1769-1776Crossref PubMed Scopus (64) Google Scholar4.ConsistencyThe association is consistent when results are replicated in studies in different settings using different methods.Certain biologic processes may be different under different circumstances; hence, the lack of an association in one population does not rule out a cause–effect relationship in another.5.Biologic plausibilityThe association agrees with currently accepted understanding of biologic processes.It is always possible that new mechanisms of disease are discovered; hence, studies that disagree with established understanding of biological processes may force a reevaluation of accepted beliefs. An association that is seemingly biologically implausible may be merely highlighting a yet unknown mechanism.6.ExperimentationThe condition can be altered (prevented or ameliorated) by an appropriate experimental intervention.A negative experiment cannot always rule out a cause–effect relationship. A positive experiment in one group of patients cannot be construed as proof of causality in all groups. Flawed experiments can lead to erroneous assumptions of causality.7.SpecificityA single putative cause produces a specific effect.A complex mechanism of action does not rule out a cause–effect relationship; it merely makes it more difficult to explain.8.Biologic coherenceThe association is consistent with the natural history of the disease.There may be unknown aspects of any diseases’ natural history. Seemingly anomalous associations could be causally explained by such unknown aspects.9.AnalogyThere are similar associations in other populations or under different settings.Biological differences between populations make it possible that the same exposure has different results depending on the circumstances. This does not rule out the possibility that associations in certain populations are due to cause–effect relationships. Open table in a new tab Experiments such as RCTs are in fact themselves merely observations; what distinguishes them is the apparent control that we believe we have over the circumstances of the observations. However, RCTs too are subject to a number of fallacies (vide infra) that could render their results invalid or questionable. It is thus naive to look at RCTs as a universal panacea that will always tell us what is right or what is wrong, as it is also simplistic to question the validity of observational studies merely because of their nonexperimental and nonrandomized nature. Observational studies could be biased in many ways, be it selection bias including survivor bias, information bias related to the use of missing data or inappropriate categories, confounding bias, or any residual sources of bias, not to mention new sources or errors and bias upon analyses such as those due to overadjustment, inappropriate modeling, and assumption violations. Due to the constant suspicion that the results of observational studies are affected by inadequate control of some or all of these biases, preference is given to RCTs to have an unbiased assessment of causality. RCTs can, however, also suffer from considerable bias; as discussed below, some of these can be controlled with careful planning, but others are inherent shortcomings of all RCTs that have to be considered when interpreting their results. An important limitation of some RCTs is nonadherence to an assigned intervention that can render the planned comparison difficult. One can choose to maintain the validity of the randomization sequence by comparing the groups according to their original assignment, the so-called “intention-to-treat” analysis; the problem in this case will be that the observed effect may not be the result of the originally assigned treatment(s), rendering any causal inference about the intervention(s) invalid or biased toward the null. Choosing to compare patients treated with one versus the other intervention irrespective of their original assignment (the so-called “as-treated” analysis) is not any better, as such a comparison will render the randomization moot and the RCT becomes an observational study riddled with its potential biases. A possible solution to this dilemma is the application of G-estimation to RCTs with significant noncompliance, which considers both assigned and received treatment simultaneously in a structural nested model.15Mark S.D. Robins J.M. A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial.Control Clin Trials. 1993; 14: 79-97Abstract Full Text PDF PubMed Scopus (116) Google Scholar Inability to follow-up on enrolled participants, also known as censoring, can likewise hamper the interpretation of RCTs. The number of participants to be enrolled in an RCT is established based on assumptions that a certain number of patients will complete the study and a certain number of events will be observed, which will then assure that a given minimum difference between the outcomes in the intervention arms can be detected. Deviations from these assumptions, whether due to larger-than-expected dropouts or fewer-than-planned events, can result in a study that does not have the power to detect meaningful differences. A recent example for several of the above pitfalls was the Dialysis Clinical Outcomes Revisited study,8Suki W.N. Zabaneh R. Cangiano J.L. et al.Effects of sevelamer and calcium-based phosphate binders on mortality in hemodialysis patients.Kidney Int. 2007; 72: 1130-1137Crossref PubMed Scopus (442) Google Scholar which compared clinical outcomes between patients randomized to receive sevelamer hydrochloride or calcium-based phosphorus binders. Only 52% of patients assigned to sevelamer and 49% of patients assigned to calcium completed the study, and the original duration of the study had to be extended due to lower-than-expected event rates. Due to these shortcomings, the Dialysis Clinical Outcomes Revisited study should be regarded as inconclusive and not one that refutes the original hypothesis of the study. Another potential problem with RCTs is unsuccessful randomization, whereby the 2 comparison groups differ from one another in one or more important characteristics, thus creating confounding. This problem can be addressed by adjustment for the unbalanced variables, provided that we know what those are; however, it is always possible that there may be some unmeasured variable that could end up confounding the results of the RCT. This can be a significant problem with small RCTs, especially if the mechanisms of action of the studied intervention are not clearly defined, making the identification of key covariates difficult. A frequently applied “remedy” for small RCTs is the technique of meta-analysis, whereby the results of many smaller studies are pooled together, thus increasing their statistical power. The problem that is often overlooked is that pooling together many poor-quality studies suffering from significant bias may alleviate the shortcoming stemming from their sample size, but does not render the aggregate results qualitatively any more valid than those of the individual studies themselves. Furthermore, significant heterogeneity among the individual studies included in the meta-analysis can make it difficult to unify their findings under a single umbrella. A recent example to these effects was a meta-analysis that examined the impact of vitamin D therapy on various biochemical end points.16Palmer S.C. McGregor D.O. Macaskill P. Craig J.C. Elder G.J. Strippoli G.F. Meta-analysis: vitamin D compounds in chronic kidney disease.Ann Intern Med. 2007; 147: 840-853Crossref PubMed Scopus (204) Google Scholar One of the main results of this study (that treatment with active vitamin D does not lower parathyroid hormone levels) has been widely cited as proof toward the ineffectiveness of active vitamin D, ignoring the fact that many of the individual studies included in this meta-analysis had significant flaws individually and that there was a significant heterogeneity among them.17Coyne D.W. Vitamin D compounds in chronic kidney disease.Ann Intern Med. 2008; 148: 969-970Crossref PubMed Google Scholar Besides the above pitfalls that can at times be difficult to predict when planning clinical trials, there are universal flaws of RCTs that need to be kept in mind before canonizing them. Perhaps, the most important problem is their limited external validity. A well-designed and well-executed RCT (ie, one with excellent internal validity) can indeed provide conclusions about the studied group of patients, which was selected based on strict inclusion and exclusion criteria, and such conclusions can be regarded valid with great degree of certainty. They, however, do not necessarily inform us about how the same or similar interventions would fare under different circumstances; patients of different age, race, and gender, or those with different kidney or liver function, may respond differently to a treatment owing to myriads of potential reasons. Nephrologists are often faced with this problem, as it is tempting to fill the void of RCTs in patients with CKD with results from studies performed in patients with normal kidney function. One example to this problem is the treatment of hypertension: the Kidney Disease Outcome Quality Improvement guidelines made recommendations about the treatment of hypertension in CKD; the recommendations were based on extrapolations from studies in patient populations without CKD owing to lack of studies in patients with CKD and end-stage renal disease (ESRD).18K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease.Am J Kidney Dis. 2004; 43: S1-S290PubMed Google Scholar This conjecture can be challenged based on biological differences in how patients with CKD respond to changes in blood pressure19Palmer B.F. Renal dysfunction complicating the treatment of hypertension.N Engl J Med. 2002; 347: 1256-1261Crossref PubMed Scopus (263) Google Scholar and also based on observational studies suggesting that the association between blood pressure and clinical outcomes is different in patients with ESRD20Kalantar-Zadeh K. Kilpatrick R.D. McAllister C.J. Greenland S. Kopple J.D. Reverse epidemiology of hypertension and cardiovascular death in the hemodialysis population: the 58th Annual Fall Conference and Scientific Sessions.Hypertension. 2005; 45: 811-817Crossref PubMed Scopus (195) Google Scholar and CKD21Kovesdy C.P. Trivedi B.K. Kalantar-Zadeh K. Anderson J.E. Association of low blood pressure with increased mortality in patients with moderate to severe chronic kidney disease.Nephrol Dial Transplant. 2006; 21: 1257-1262Crossref PubMed Scopus (106) Google Scholar than that seen in the general population.22Lewington S. Clarke R. Qizilbash N. Peto R. Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies.Lancet. 2002; 360: 1903-1913Abstract Full Text Full Text PDF PubMed Scopus (7709) Google Scholar Nevertheless, the conviction that the lack of RCTs should prevent even speculations about causal inferences that contradict mainstream paradigms seems to trump these arguments at the moment. Another practical shortcoming of RCTs is the ethical concern raised by some interventions. To completely eliminate bias, one would have to assign potentially harmful interventions to study participants and thus intentionally jeopardize their wellbeing. It is easy to see how one cannot ethically conduct an RCT examining the harmful effects of smoking, for example. The causal role of smoking in engendering lung cancer is now universally accepted, even though it is based exclusively on observational data, and one could potentially construct alternative hypotheses how an association between smoking and lung cancer could be biased by confounders like, for example, inhalation of phosphorus from the fires of the matches used to light cigarettes. In multivariate analyses, adjusting for the status of “carrying matches” may indeed nullify the statistical association between smoking and lung cancer. Nevertheless, the causal association between tobacco smoking and lung cancer has hardly been questioned despite a lack of RCTs. An example from the field of nephrology where concerns for the wellbeing of participants made the completion of an RCT difficult was a recent clinical trial that compared early versus late initiation of dialysis.23Cooper B.A. Branley P. Bulfone L. et al.A randomized, controlled trial of early versus late initiation of dialysis.N Engl J Med. 2010; 363: 609-619Crossref PubMed Scopus (662) Google Scholar This study failed to achieve the prespecified difference in glomerular filtration rate at dialysis initiation between the 2 intervention arms, as 76% of patients in the late-start arm initiated dialysis earlier than planned, largely because of the investigators’ concerns about the wellbeing of those patients who were supposed to delay dialysis initiation. In cases like these, observational studies employing various techniques to minimize bias may be the only feasible way to gain better understanding of a disease process or an intervention (vide infra). Finally, the most mundane deficiencies of RCTs are their high cost and the long time required for their completion. Compared with observational studies RCTs carry a price that is several magnitudes higher, and typically, they take many more years to complete. A significant proportion of RCTs are sponsored by pharmaceutical companies to whom the investment is justified by future returns due to a new indication for their marketed drug or to higher sales boosted by the benefits shown by their agent in the clinical trial. The recent spate of large pharmaceutical-sponsored RCTs showing neutral or unexpectedly deleterious effects of the applied interventions in ESRD/CKD patients2Besarab A. Bolton W.K. Browne J.K. et al.The effects of normal as compared with low hematocrit values in patients with cardiac disease who are receiving hemodialysis and epoetin.N Engl J Med. 1998; 339: 584-590Crossref PubMed Scopus (1884) Google Scholar, 3Drueke T.B. Locatelli F. Clyne N. et al.Normalization of hemoglobin level in patients with chronic kidney disease and anemia.N Engl J Med. 2006; 355: 2071-2084Crossref PubMed Scopus (1788) Google Scholar, 4Pfeffer M.A. Burdmann E.A. Chen C.Y. et al.A trial of darbepoetin alfa in type 2 diabetes and chronic kidney disease.N Engl J Med. 2009; 361: 2019-2032Crossref PubMed Scopus (1653) Google Scholar, 5Singh A.K. Szczech L. Tang K.L. et al.Correction of anemia with epoetin alfa in chronic kidney disease.N Engl J Med. 2006; 355: 2085-2098Crossref PubMed Scopus (2284) Google Scholar, 6Fellstrom B.C. Jardine A.G. Schmieder R.E. et al.Rosuvastatin and cardiovascular events in patients undergoing hemodialysis.N Engl J Med. 2009; 360: 1395-1407Crossref PubMed Scopus (1604) Google Scholar, 7Wanner C. Krane V. Marz W. et al.Atorvastatin in patients with type 2 diabetes mellitus undergoing hemodialysis.N Engl J Med. 2005; 353: 238-248Crossref PubMed Scopus (2212) Google Scholar, 8Suki W.N. Zabaneh R. Cangiano J.L. et al.Effects of sevelamer and calcium-based phosphate binders on mortality in hemodialysis patients.Kidney Int. 2007; 72: 1130-1137Crossref PubMed Scopus (442) Google Scholar along with the economic recession and a change in the payment structure for medications in dialysis patients will foreseeably dampen the enthusiasm for corporate sponsorship of RCTs in this patient population, making it even more important to explore alternative ways to make causal inferences. What are clinicians to do if there are no RCTs for most major problems faced in clinical practice of nephrology? If we adhere to the “no RCT equals no conclusive proof” principle (Fig. 1), we will never have all the answers we need, especially if we consider the above limitations of RCTs. It is thus worthwhile to explore alternative ways to try and make causal inferences using methods other than RCTs. The principle advantage of RCTs is their controlled nature, which eliminates many of the biases that hamper the interpretation of observational studies. There are, however, techniques that can be applied to observational data to eliminate bias, which could theoretically allow causal inference. The simplest and most widely applied one is adjustment, whereby analyses can account for the bias introduced by a known confounder. A variant of adjustment is the use of propensity scores, where the likelihood of a certain intervention or treatment is quantified using known determinants of the intervention, and analyses are then either conditioned (stratified) on or adjusted for the propensity scores. A special case of confounding is the so-called time-dependent confounding and when a confounder also acts simultaneously as an intermediate confounder; an example is the effect of active vitamin D on mortality: in clinical practice, active vitamin D is initiated based on a patient’s serum parathyroid hormone (PTH) level, which in itself can be associated with mortality (thus it is a confounder). Once, however, active vitamin D is started, it will usually lower the PTH level (thus, PTH is also an intermediate), but the lower PTH level will then often result in the discontinuation or in a dose adjustment for active vitamin D (thus, it is a time-dependent confounder and may also be in the causal pathway to mortality). Adjustment for baseline or even time-dependent values of PTH and/or active vitamin D doses in Cox models (as was done in numerous studies that examined the association of active vitamin D with mortality24Kalantar-Zadeh K. Kuwae N. Regidor D.L. et al.Survival predictability of time-varying indicators of bone disease in maintenance hemo" @default.
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• W2084286036 title "Observational Studies Versus Randomized Controlled Trials: Avenues to Causal Inference in Nephrology" @default.
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