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Identifying Potential Biases in the Nephrology Literature
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Identifying Potential Biases in the Nephrology Literature Thomas W. Ferguson and Navdeep Tangri Observational studies are common in the nephrology literature, particularly given the lack of large randomized trials. While these studies have identified important associations, potential biases, if unrecognized, can result in misinterpreted conclusions. In this review, we present an example of four potentially important biases (lead time bias, survivor bias, immortal time bias, and index event bias) that can result in inaccurate estimates of association between risk factors or treatments and outcomes. Recognition of these potential biases can help improve study design and interpretation. Q 2016 by the National Kidney Foundation, Inc. All rights reserved. Key Words: Immortal time bias, Index event bias, Survivor bias, Lead time bias, Epidemiology
INTRODUCTION Landmark observational studies have been instrumental in the definition, staging and prognostication of CKD, definition of acute kidney injury, and in identifying potentially modifiable risk factors in the dialysis population.1,2 While the field of nephrology has had a relative lack of large randomized trials,3 well-designed observational studies have often filled the evidence gap. However, these observational studies can be prone to biases in design, and their findings may be inaccurate if these epidemiological biases are not recognized and appropriately managed. In this review, we aim to provide a synopsis of common epidemiological biases and their importance in the nephrology literature. We have focused on lead time bias, survivor bias, immortal time bias, and index event bias. LEAD TIME BIAS Lead time bias manifests in individuals who enter a treatment earlier than their comparative counterparts, and as such, are attributed an additional survival benefit that may not reflect the actual effectiveness of the alternative treatment being considered.4 Comparison between these two treatment groups would ideally begin at a common point rather than the beginning of their respective treatments. Considering lead time bias in the context of early screening and treatment studies is essential, in particular with diseases, such as CKD, that are often asymptomatic in the early stages and have a long duration. In this case, lead time would refer to the time between early diagnosis by screening and the usual time of diagnosis due to complications or symptoms. In many cases, observed survival may appear to be increased in the early screening group, with the actual time of death not being delayed (Fig 1).5 Therefore, the length of survival attributed to patients may be a result of earlier registration in a study and not wholly attributable to the treatment in question. Attenuation for lead time bias can be mitigated in these specific analyses by starting follow-up at a relatable point between both treatment groups, such as similar estimated glomerular filtration rate (eGFR) rather than at point of first screen.6 In the case of screening, even randomized trials are affected. For example, a randomized controlled trial of screening for CKD would need to be concerned with the Adv Chronic Kidney Dis. 2016;23(6):373-376
lead time bias associated with the potential for a propensity to detect nonprogressive/nonaggressive CKD that is not likely to reach a clinical end point likely to reach a clinical end point (dialysis or death). This phenomenon has been extensively described among prostate cancer screenees,7 where the potential for detecting nonaggressive (benign or overdiagnosed) latent disease may introduce substantial harms of screening that must be weighed against potential benefits.8 Control for lead time bias in circumstances such as these can be accomplished by estimating the additional follow-up time that is observed purely as a result of lead time, as a function of the estimated rate of transition to symptomatic disease, and subtracting it from the total survival time.9 SURVIVOR BIAS Survivor bias is another often overlooked bias in many cohort and case-control studies. In case-control studies, survivor bias can occur when cases are selected a long period following the event being studied, with exposed cases having an increased risk of severe illness or death compared with nonexposed cases during the selection period.10 In cohort studies, survivor bias can arise when using data from patients that are collected at a given time point among survivors rather than gathering data on a group of incident cases.11 In the case of progressive CKD, this form of bias can be particularly problematic due to the strong relationship between declining kidney function and resulting risk of allcause mortality (12% risk of death over a 4.9-year median follow-up at an eGFR threshold of 80 mL/min/1.73 m2; 17%
From the Section of Nephrology, Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada; and Seven Oaks Hospital Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada. Financial Disclosure: No specific funding was received for this study. Dr. Navdeep Tangri receives honoraria from Otsuka Inc., AstraZeneca Inc. and is on the advisory board for Viewics Inc. The authors have no other conflicts of interest to disclose. Address correspondence to Navdeep Tangri, MD, PhD, 2PD07—2300 McPhillips Street, Winnipeg, Manitoba R2V3M3, Canada. E-mail: ntangri@ sogh.mb.ca Ó 2016 by the National Kidney Foundation, Inc. All rights reserved. 1548-5595/$36.00 http://dx.doi.org/10.1053/j.ackd.2016.11.013
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This is because an individual has to survive to their high school graduation to have a chance of being included in the “high school graduate” group. This time between birth and graduation in the “high school graduate” cohort is known as immortal time. In contrast, the nongraduate group had the potential to die before the typical graduation date, and as such, an excess divergence in early mortality rates would likely be observed between the two groups. The bias resulting from this is known as immortal time bias or survivor treatment selection bias. In a similar fashion, immortal time bias has presented itself in clinical studies in CKD. One such study examined the impact of attending a multidisciplinary clinic and its Figure 1. Overview of lead time bias. (A) The usual time of impact on survival time in elderly CKD patients, with a diagnosis and the length of survival after diagnosis. (B) Earlier diagnosis increases observed survival time, but designated cohort entry date taken as the date of a patient’s death is not actually delayed. Including the survival times first serum creatinine (SCr) test. In these patients, the multibetween the periods of early diagnosis as a result of disciplinary clinic visit occurred at some time following the screening and the usual time of diagnosis, when comparing measurement of SCr, resulting in all individuals who were them with observed survival times in those who are not in the clinic exposure group being submitted to a period of screened, results in lead time bias. Jaar et al. J Am Soc immortal time between the measurement of their SCr and 5 Nephrol 3: 601-609, 2008. their clinic attendance date. In this study, a steeper slope in the survival curve of those with no multidisciplinary at 60 mL/min/1.73 m2; and 25% at 40 mL/min/1.73 m2).12,13 care was observed at the beginning of follow-up and Among patients on dialysis, the 5-year rate of mortality became less pronounced in later years, suggesting a strong ranges between 25% and over 60% dependent on immediate treatment effect of etiology of disease,14 further multidisciplinary clinics that making survivors inherently can be attributed, at least in CLINICAL SUMMARY different from all patients part, to immortal time bias with the disease. (Fig 2).17,18 As such, upon Epidemiological biases can affect the estimation and An example of survivor further in-vestigation, the interpretation of findings from observational studies. bias is found in observaauthors of the study in Several of these biases are relevant to the nephrology tional studies that examine question were able to literature and can lead to potentially inaccurate findings. the relationship of eGFR at reaffirm the conclusions of dialysis initiation and mor Recognition of these biases can aid in the design and their initial analysis with tality on dialysis. In these interpretation of observational studies. adjustments for immortal studies, patients who begin time bias, finding a dialysis at a lower eGFR persistent relationship could possibly already have selected themselves as survibetween multidisciplinary care and mortality, however, vors since they may have survived with the “condition” with a smaller magnitude of the treatment effect.19 for longer than those who begin dialysis at higher levels Immortal time bias can be mitigated in several ways. of eGFR.15 In other studies of “incident” dialysis patients, First, patients in both groups could be matched. In the survivor bias arises when a 3-month window or on occaabove high school graduation example, this would sion a 6-month window is used to select the study popularequire an individual who did not graduate high tion. These patients are inherently different from the up to school to be alive at the same time as their matched 10% of the dialysis population who die within the first 3 graduate attends their graduation ceremony. In the months on treatment, and treatment effects in these “incicase of the patient attending a multidisciplinary clinic, dent” patients should be interpreted with appropriate the matched patient who is not exposed to the clinic caution. Attenuation for this bias can be achieved by would be required to be living at the time their counincluding only truly incident dialysis patients.16 terpart visits the clinic. In both cases, counting of survival time would begin at the same time (eg, gradIMMORTAL TIME BIAS uation day or the day of clinic attendance) and any In a hypothetical situation, if we were to follow a cohort of time prior to this index date would not be counted individuals from birth until death in order to determine for either individual being compared. Second, analysis the relationship between high school graduation and the can be performed using time-dependent covariates. In risk of mortality, we may conclude that high school gradthis situation, variables can be used which have a dyuation is associated with a reduced risk of mortality for namic value dependent on the individuals status in many different reasons. There is, however, the potential the study, such as the clinic patient being considered for bias to impact the magnitude of the final result if surunexposed until such time as they visit the clinic, vival time is simply counted from birth in both the exposed whereas they would be considered exposed following and unexposed groups. their visit date.17 Adv Chronic Kidney Dis. 2016;23(6):373-376
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Figure 2. Overview of immortal time bias. Situation in which MDC clinic visit occurred after serum creatinine test. Exposed patient was guaranteed to be alive between the test date and the clinic visit, resulting in a period of “immortal time.” Control patient died within the immortal time window, resulting in an immortal time bias. MDC, multidisciplinary care; SCr, serum creatinine. Shariff et al. J Am Soc Nephrol 19: 841-843, 2008.17
INDEX EVENT BIAS The index event (or collider) bias arises when a stratification variable applied in an analysis is related to both the outcome and predictor being considered and is termed a collider variable.20 In many clinical studies, this has been termed “reverse epidemiology,” such as a protective effect being demonstrated by obesity, hypertension, high cholesterol, or high creatinine with respect to the risk of adverse outcomes in dialysis patients despite these factors often being found to be associated with increased risk in the otherwise healthy general population.21,22 A similar effect has been observed with respect to smoking and its perceived protective effect on progression risk to kidney failure when controlling for urine albumin:creatinine ratio (ACR) in participants from the SHARP trial.23 The hypothesis that this conditioning on urine ACR introduces an index event bias and artificial protective effect between smoking and ESRD is possible for several reasons: (1) urine ACR has been shown to be modified by smoking24; (2) urine ACR is a mediator on the pathway between smoking status and ESRD, as smoking status is associated with both urine ACR and ESRD, and ACR is associated with progressing to ESRD; (3) conditioning for urine ACR as a mediator on the causal pathway between smoking and ESRD would result in the total effect of smoking having this pathway removed from its estimation, biasing the true effect of smoking on ESRD status.20 As such, the SHARP trial found no protective effect of smoking on progression to ESRD when urine ACR was not considered a confounder in their Cox regression analysis.23 An important example of index event bias in the nephrology literature is the “obesity paradox” in determining survival of patients on dialysis. A multitude of observational studies have shown that obesity is associated with a more rapid decline in kidney function, kidney failure, and early mortality. However, in studies of patients with existing kidney failure, obesity is associated with a lower risk of mortality suggesting a possible reverse epidemiology. In these studies, it is nearly impossible to account Adv Chronic Kidney Dis. 2016;23(6):373-376
for all potential risk factors for death, and given that death can occur before dialysis, it is almost certain that these risk factors are imbalanced in patients selected after dialysis initiation. As such, patients who are obese and survive to reach dialysis are likely to have a lower burden of these unmeasured or unaccounted risk factors, and their magnitude is represented as the protective effect of obesity. In fact, similar relationships between colliders and variables of interest may explain other paradoxes such as the effects of African-American race, elevated cholesterol, and blood pressure in patients on dialysis.25 Analytical tools exist to help manage index event bias, such as directed acyclic graphs (Fig 326). These schematics are a useful tool to identify which potential confounders may result in unnoticed index event bias in statistical models. This can be accomplished with a six-step process that has been outlined in detail previously.27 In addition to this, more complex questions that involved variables that are simultaneously confounders, indication for exposure, or influenced by the exposure variable can be handled using marginal structural models that allow for inclusion of these factors by treating them as time-dependent variables using inverse probability weighting.28
Figure 3. Directed acyclic graph. Sample of a directed acyclic graph. In this example, not accounting for socioeconomic status can lead to biased estimates for the effect of obesity on mortality.
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CONCLUSIONS In summary, epidemiological biases are common in the nephrology literature and can result in misinterpretation of findings. Recognition of these potential biases should occur during study design and should be addressed by peer reviewers and journal editors when observational study reports are submitted for publication. Increased awareness of these biases can aid clinician readers in the recognition of potentially erroneous findings. REFERENCES 1. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. 1999;130(6):461-470. 2. Hoste EA, Clermont G, Kersten A, et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10(3):R73. 3. Strippoli GF, Craig JC, Schena FP. The number, quality, and coverage of randomized controlled trials in nephrology. J Am Soc Nephrol. 2004;15(2):411-419. 4. Crews DC, Scialla JJ, Boulware LE, et al. Comparative effectiveness of early versus conventional timing of dialysis initiation in advanced CKD. Am J Kidney Dis. 2014;63(5):806-815. 5. Jaar BG, Khatib R, Plantinga L, Boulware LE, Powe NR. Principles of screening for chronic kidney disease. Clin J Am Soc Nephrol. 2008;3(2):601-609. 6. Chen SC, Hwang SJ, Tsai JC, et al. Early nephrology referral is associated with prolonged survival in hemodialysis patients even after exclusion of lead-time bias. Am J Med Sci. 2010;339(2):123-126. 7. Finne P, Fallah M, Hakama M, et al. Lead-time in the European randomised study of screening for prostate cancer. Eur J Cancer. 2010;46(17):3102-3108. 8. Qaseem A, Barry MJ, Denberg TD, Owens DK, Shekelle P. Clinical Guidelines Committee of the American College of Physicians: screening for prostate cancer: a guidance statement from the clinical guidelines committee of the American College of Physicians. Ann Intern Med. 2013;158(10):761-769. 9. Duffy SW, Nagtegaal ID, Wallis M, et al. Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98-104. 10. van Rein N, Cannegieter SC, Rosendaal FR, Reitsma PH, Lijfering WM. Suspected survivor bias in case-control studies: stratify on survival time and use a negative control. J Clin Epidemiol. 2014;67(2):232-235. 11. Hoogeveen EK, Halbesma N, Rothman KJ, et al. Obesity and mortality risk among younger dialysis patients. Clin J Am Soc Nephrol. 2012;7(2):280-288. 12. Jager KJ, Stel VS, Zoccali C, Wanner C, Dekker FW. The issue of studying the effect of interventions in renal replacement therapy—
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26. 27. 28.
to what extent may we be deceived by selection and competing risk? Nephrol Dial Transplant. 2010;25(12):3836-3839. Tonelli M, Wiebe N, Culleton B, et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol. 2006;17(7):2034-2047. Canadian Institute for Health Information. Report Page: CORR 2016. Available at: https://www.cihi.ca/en/organ-replacements/corr-annualreport. Accessed October 31, 2016. Stel VS, Dekker FW, Ansell D, et al. Residual renal function at the start of dialysis and clinical outcomes. Nephrol Dial Transplant. 2009;24(10):3175-3182. McDonald SP, Collins JF, Johnson DW. Obesity is associated with worse peritoneal dialysis outcomes in the Australia and New Zealand patient populations. J Am Soc Nephrol. 2003;14(11):2894-2901. Shariff SZ, Cuerden MS, Jain AK, Garg AX. The secret of immortal time bias in epidemiologic studies. J Am Soc Nephrol. 2008;19(5):841-843. Hemmelgarn BR, Manns BJ, Zhang J, et al. Association between multidisciplinary care and survival for elderly patients with chronic kidney disease. J Am Soc Nephrol. 2007;18(3):993-999. Hemmelgarn BR, Zhang J, Manns BJ, Tonelli M. Multidisciplinary care and immortal time bias. Kidney Int. 2013;84(5):1052. Staplin N, Herrington WG, Judge PK, et al. Use of causal diagrams to inform the design and interpretation of observational studies: an example from the study of heart and renal protection (SHARP) [epub ahead of print]. Clin J Am Soc Nephrol http://dx.doi.org/10. 2215/CJN.02430316, accessed August 2016. Levin NW, Handelman GJ, Coresh J, Port FK, Kaysen GA. Reverse epidemiology: a confusing, confounding, and inaccurate term. Semin Dial. 2007;20(6):586-592. Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int. 2003;63(3):793-808. Staplin N, Haynes R, Herrington WG, et al. Smoking and adverse outcomes in patients with CKD: the Study of Heart and Renal Protection (SHARP). Am J Kidney Dis. 2016;68(3): 371-380. Pinto-Sietsma SJ, Mulder J, Janssen WM, Hillege HL, de Zeeuw D, de Jong PE. Smoking is related to albuminuria and abnormal renal function in nondiabetic persons. Ann Intern Med. 2000;133(8):585-591. Flanders WD, Eldridge RC, McClellan W. A nearly unavoidable mechanism for collider bias with index-event studies. Epidemiology. 2014;25(5):762-764. Textor J, Hardt J, Knuppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology. 2011;22(5):745. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 2008;8:70. Mehrotra R, Chiu YW, Kalantar-Zadeh K, Bargman J, Vonesh E. Similar outcomes with hemodialysis and peritoneal dialysis in patients with end-stage renal disease. Arch Intern Med. 2011;171(2): 110-118.
Adv Chronic Kidney Dis. 2016;23(6):373-376
Identifying Potential Biases in the Nephrology Literature Thomas W. Ferguson and Navdeep Tangri Observational studies are common in the nephrology literature, particularly given the lack of large randomized trials. While these studies have identified important associations, potential biases, if unrecognized, can result in misinterpreted conclusions. In this review, we present an example of four potentially important biases (lead time bias, survivor bias, immortal time bias, and index event bias) that can result in inaccurate estimates of association between risk factors or treatments and outcomes. Recognition of these potential biases can help improve study design and interpretation. Q 2016 by the National Kidney Foundation, Inc. All rights reserved. Key Words: Immortal time bias, Index event bias, Survivor bias, Lead time bias, Epidemiology
INTRODUCTION Landmark observational studies have been instrumental in the definition, staging and prognostication of CKD, definition of acute kidney injury, and in identifying potentially modifiable risk factors in the dialysis population.1,2 While the field of nephrology has had a relative lack of large randomized trials,3 well-designed observational studies have often filled the evidence gap. However, these observational studies can be prone to biases in design, and their findings may be inaccurate if these epidemiological biases are not recognized and appropriately managed. In this review, we aim to provide a synopsis of common epidemiological biases and their importance in the nephrology literature. We have focused on lead time bias, survivor bias, immortal time bias, and index event bias. LEAD TIME BIAS Lead time bias manifests in individuals who enter a treatment earlier than their comparative counterparts, and as such, are attributed an additional survival benefit that may not reflect the actual effectiveness of the alternative treatment being considered.4 Comparison between these two treatment groups would ideally begin at a common point rather than the beginning of their respective treatments. Considering lead time bias in the context of early screening and treatment studies is essential, in particular with diseases, such as CKD, that are often asymptomatic in the early stages and have a long duration. In this case, lead time would refer to the time between early diagnosis by screening and the usual time of diagnosis due to complications or symptoms. In many cases, observed survival may appear to be increased in the early screening group, with the actual time of death not being delayed (Fig 1).5 Therefore, the length of survival attributed to patients may be a result of earlier registration in a study and not wholly attributable to the treatment in question. Attenuation for lead time bias can be mitigated in these specific analyses by starting follow-up at a relatable point between both treatment groups, such as similar estimated glomerular filtration rate (eGFR) rather than at point of first screen.6 In the case of screening, even randomized trials are affected. For example, a randomized controlled trial of screening for CKD would need to be concerned with the Adv Chronic Kidney Dis. 2016;23(6):373-376
lead time bias associated with the potential for a propensity to detect nonprogressive/nonaggressive CKD that is not likely to reach a clinical end point likely to reach a clinical end point (dialysis or death). This phenomenon has been extensively described among prostate cancer screenees,7 where the potential for detecting nonaggressive (benign or overdiagnosed) latent disease may introduce substantial harms of screening that must be weighed against potential benefits.8 Control for lead time bias in circumstances such as these can be accomplished by estimating the additional follow-up time that is observed purely as a result of lead time, as a function of the estimated rate of transition to symptomatic disease, and subtracting it from the total survival time.9 SURVIVOR BIAS Survivor bias is another often overlooked bias in many cohort and case-control studies. In case-control studies, survivor bias can occur when cases are selected a long period following the event being studied, with exposed cases having an increased risk of severe illness or death compared with nonexposed cases during the selection period.10 In cohort studies, survivor bias can arise when using data from patients that are collected at a given time point among survivors rather than gathering data on a group of incident cases.11 In the case of progressive CKD, this form of bias can be particularly problematic due to the strong relationship between declining kidney function and resulting risk of allcause mortality (12% risk of death over a 4.9-year median follow-up at an eGFR threshold of 80 mL/min/1.73 m2; 17%
From the Section of Nephrology, Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada; and Seven Oaks Hospital Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada. Financial Disclosure: No specific funding was received for this study. Dr. Navdeep Tangri receives honoraria from Otsuka Inc., AstraZeneca Inc. and is on the advisory board for Viewics Inc. The authors have no other conflicts of interest to disclose. Address correspondence to Navdeep Tangri, MD, PhD, 2PD07—2300 McPhillips Street, Winnipeg, Manitoba R2V3M3, Canada. E-mail: ntangri@ sogh.mb.ca Ó 2016 by the National Kidney Foundation, Inc. All rights reserved. 1548-5595/$36.00 http://dx.doi.org/10.1053/j.ackd.2016.11.013
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This is because an individual has to survive to their high school graduation to have a chance of being included in the “high school graduate” group. This time between birth and graduation in the “high school graduate” cohort is known as immortal time. In contrast, the nongraduate group had the potential to die before the typical graduation date, and as such, an excess divergence in early mortality rates would likely be observed between the two groups. The bias resulting from this is known as immortal time bias or survivor treatment selection bias. In a similar fashion, immortal time bias has presented itself in clinical studies in CKD. One such study examined the impact of attending a multidisciplinary clinic and its Figure 1. Overview of lead time bias. (A) The usual time of impact on survival time in elderly CKD patients, with a diagnosis and the length of survival after diagnosis. (B) Earlier diagnosis increases observed survival time, but designated cohort entry date taken as the date of a patient’s death is not actually delayed. Including the survival times first serum creatinine (SCr) test. In these patients, the multibetween the periods of early diagnosis as a result of disciplinary clinic visit occurred at some time following the screening and the usual time of diagnosis, when comparing measurement of SCr, resulting in all individuals who were them with observed survival times in those who are not in the clinic exposure group being submitted to a period of screened, results in lead time bias. Jaar et al. J Am Soc immortal time between the measurement of their SCr and 5 Nephrol 3: 601-609, 2008. their clinic attendance date. In this study, a steeper slope in the survival curve of those with no multidisciplinary at 60 mL/min/1.73 m2; and 25% at 40 mL/min/1.73 m2).12,13 care was observed at the beginning of follow-up and Among patients on dialysis, the 5-year rate of mortality became less pronounced in later years, suggesting a strong ranges between 25% and over 60% dependent on immediate treatment effect of etiology of disease,14 further multidisciplinary clinics that making survivors inherently can be attributed, at least in CLINICAL SUMMARY different from all patients part, to immortal time bias with the disease. (Fig 2).17,18 As such, upon Epidemiological biases can affect the estimation and An example of survivor further in-vestigation, the interpretation of findings from observational studies. bias is found in observaauthors of the study in Several of these biases are relevant to the nephrology tional studies that examine question were able to literature and can lead to potentially inaccurate findings. the relationship of eGFR at reaffirm the conclusions of dialysis initiation and mor Recognition of these biases can aid in the design and their initial analysis with tality on dialysis. In these interpretation of observational studies. adjustments for immortal studies, patients who begin time bias, finding a dialysis at a lower eGFR persistent relationship could possibly already have selected themselves as survibetween multidisciplinary care and mortality, however, vors since they may have survived with the “condition” with a smaller magnitude of the treatment effect.19 for longer than those who begin dialysis at higher levels Immortal time bias can be mitigated in several ways. of eGFR.15 In other studies of “incident” dialysis patients, First, patients in both groups could be matched. In the survivor bias arises when a 3-month window or on occaabove high school graduation example, this would sion a 6-month window is used to select the study popularequire an individual who did not graduate high tion. These patients are inherently different from the up to school to be alive at the same time as their matched 10% of the dialysis population who die within the first 3 graduate attends their graduation ceremony. In the months on treatment, and treatment effects in these “incicase of the patient attending a multidisciplinary clinic, dent” patients should be interpreted with appropriate the matched patient who is not exposed to the clinic caution. Attenuation for this bias can be achieved by would be required to be living at the time their counincluding only truly incident dialysis patients.16 terpart visits the clinic. In both cases, counting of survival time would begin at the same time (eg, gradIMMORTAL TIME BIAS uation day or the day of clinic attendance) and any In a hypothetical situation, if we were to follow a cohort of time prior to this index date would not be counted individuals from birth until death in order to determine for either individual being compared. Second, analysis the relationship between high school graduation and the can be performed using time-dependent covariates. In risk of mortality, we may conclude that high school gradthis situation, variables can be used which have a dyuation is associated with a reduced risk of mortality for namic value dependent on the individuals status in many different reasons. There is, however, the potential the study, such as the clinic patient being considered for bias to impact the magnitude of the final result if surunexposed until such time as they visit the clinic, vival time is simply counted from birth in both the exposed whereas they would be considered exposed following and unexposed groups. their visit date.17 Adv Chronic Kidney Dis. 2016;23(6):373-376
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Figure 2. Overview of immortal time bias. Situation in which MDC clinic visit occurred after serum creatinine test. Exposed patient was guaranteed to be alive between the test date and the clinic visit, resulting in a period of “immortal time.” Control patient died within the immortal time window, resulting in an immortal time bias. MDC, multidisciplinary care; SCr, serum creatinine. Shariff et al. J Am Soc Nephrol 19: 841-843, 2008.17
INDEX EVENT BIAS The index event (or collider) bias arises when a stratification variable applied in an analysis is related to both the outcome and predictor being considered and is termed a collider variable.20 In many clinical studies, this has been termed “reverse epidemiology,” such as a protective effect being demonstrated by obesity, hypertension, high cholesterol, or high creatinine with respect to the risk of adverse outcomes in dialysis patients despite these factors often being found to be associated with increased risk in the otherwise healthy general population.21,22 A similar effect has been observed with respect to smoking and its perceived protective effect on progression risk to kidney failure when controlling for urine albumin:creatinine ratio (ACR) in participants from the SHARP trial.23 The hypothesis that this conditioning on urine ACR introduces an index event bias and artificial protective effect between smoking and ESRD is possible for several reasons: (1) urine ACR has been shown to be modified by smoking24; (2) urine ACR is a mediator on the pathway between smoking status and ESRD, as smoking status is associated with both urine ACR and ESRD, and ACR is associated with progressing to ESRD; (3) conditioning for urine ACR as a mediator on the causal pathway between smoking and ESRD would result in the total effect of smoking having this pathway removed from its estimation, biasing the true effect of smoking on ESRD status.20 As such, the SHARP trial found no protective effect of smoking on progression to ESRD when urine ACR was not considered a confounder in their Cox regression analysis.23 An important example of index event bias in the nephrology literature is the “obesity paradox” in determining survival of patients on dialysis. A multitude of observational studies have shown that obesity is associated with a more rapid decline in kidney function, kidney failure, and early mortality. However, in studies of patients with existing kidney failure, obesity is associated with a lower risk of mortality suggesting a possible reverse epidemiology. In these studies, it is nearly impossible to account Adv Chronic Kidney Dis. 2016;23(6):373-376
for all potential risk factors for death, and given that death can occur before dialysis, it is almost certain that these risk factors are imbalanced in patients selected after dialysis initiation. As such, patients who are obese and survive to reach dialysis are likely to have a lower burden of these unmeasured or unaccounted risk factors, and their magnitude is represented as the protective effect of obesity. In fact, similar relationships between colliders and variables of interest may explain other paradoxes such as the effects of African-American race, elevated cholesterol, and blood pressure in patients on dialysis.25 Analytical tools exist to help manage index event bias, such as directed acyclic graphs (Fig 326). These schematics are a useful tool to identify which potential confounders may result in unnoticed index event bias in statistical models. This can be accomplished with a six-step process that has been outlined in detail previously.27 In addition to this, more complex questions that involved variables that are simultaneously confounders, indication for exposure, or influenced by the exposure variable can be handled using marginal structural models that allow for inclusion of these factors by treating them as time-dependent variables using inverse probability weighting.28
Figure 3. Directed acyclic graph. Sample of a directed acyclic graph. In this example, not accounting for socioeconomic status can lead to biased estimates for the effect of obesity on mortality.
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CONCLUSIONS In summary, epidemiological biases are common in the nephrology literature and can result in misinterpretation of findings. Recognition of these potential biases should occur during study design and should be addressed by peer reviewers and journal editors when observational study reports are submitted for publication. Increased awareness of these biases can aid clinician readers in the recognition of potentially erroneous findings. REFERENCES 1. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. 1999;130(6):461-470. 2. Hoste EA, Clermont G, Kersten A, et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10(3):R73. 3. Strippoli GF, Craig JC, Schena FP. The number, quality, and coverage of randomized controlled trials in nephrology. J Am Soc Nephrol. 2004;15(2):411-419. 4. Crews DC, Scialla JJ, Boulware LE, et al. Comparative effectiveness of early versus conventional timing of dialysis initiation in advanced CKD. Am J Kidney Dis. 2014;63(5):806-815. 5. Jaar BG, Khatib R, Plantinga L, Boulware LE, Powe NR. Principles of screening for chronic kidney disease. Clin J Am Soc Nephrol. 2008;3(2):601-609. 6. Chen SC, Hwang SJ, Tsai JC, et al. Early nephrology referral is associated with prolonged survival in hemodialysis patients even after exclusion of lead-time bias. Am J Med Sci. 2010;339(2):123-126. 7. Finne P, Fallah M, Hakama M, et al. Lead-time in the European randomised study of screening for prostate cancer. Eur J Cancer. 2010;46(17):3102-3108. 8. Qaseem A, Barry MJ, Denberg TD, Owens DK, Shekelle P. Clinical Guidelines Committee of the American College of Physicians: screening for prostate cancer: a guidance statement from the clinical guidelines committee of the American College of Physicians. Ann Intern Med. 2013;158(10):761-769. 9. Duffy SW, Nagtegaal ID, Wallis M, et al. Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98-104. 10. van Rein N, Cannegieter SC, Rosendaal FR, Reitsma PH, Lijfering WM. Suspected survivor bias in case-control studies: stratify on survival time and use a negative control. J Clin Epidemiol. 2014;67(2):232-235. 11. Hoogeveen EK, Halbesma N, Rothman KJ, et al. Obesity and mortality risk among younger dialysis patients. Clin J Am Soc Nephrol. 2012;7(2):280-288. 12. Jager KJ, Stel VS, Zoccali C, Wanner C, Dekker FW. The issue of studying the effect of interventions in renal replacement therapy—
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