Reply to: “Built-in bias in HCV clearance in acute HCV infection”
Authors: Massimiliano Copetti, Fabio Pellegrini, Valeria Piazzolla, Alessandra Mangia
Journal: Journal of Hepatology (2013)

Treatment optimization and prediction of HCV clearance in patients with acute HCV infectionJournal of HepatologyVol. 59Issue 2PreviewThe lack of consensus on the optimal timing, regimen, and duration of treatment, in patients with acute HCV infection, stimulates the research on both favourable outcome predictors and individualized treatment regimens. This study aimed at investigating the impact of IL28B SNP rs12979860 alone or in combination with HLA class II alleles in both predicting spontaneous viral clearance and individualizing treatment strategies for patients with HCV persistence, after acute HCV exposure. Full-Text PDF Built-in bias in HCV clearance in acute HCV infectionJournal of HepatologyVol. 60Issue 2PreviewTo the Editor: Full-Text PDF Open Access To the Editor: We would like thank our colleagues for their insightful notes on basic epidemiology. We agree with them that researchers and clinicians sometimes forget the different nature of the risk metrics. Undoubtedly, odds ratios (OR) and relative risks (RR) have different definitions and, therefore, different interpretations. The RR, computed as the ratio of two risks, is a natural way to compare risk proportions: a RR of 1.60 indicates a higher risk of 60% in exposed subjects vs. non-exposed ones to develop an event. The OR, computed as the ratio of two odds, is a less intuitive risk measure: an OR of 1.60 does not suggest a higher risk of 60% but a higher odds in exposed subjects. In our study, the crude OR of IL28B (exposure) for clearance (outcome) is 4.22 (Table 1), while the correspondent crude RR is equal to 2.95. The so called built-in bias could only occur if one reads this OR interpreting it as an RR. Therefore, why is RR not always used in cohort studies as in our case? The reason stems from the need to perform a multivariable analysis in an observational (i.e., non-randomized) study, in which it is necessary to adjust for potential confounders. To estimate an adjusted RR, binomial or Poisson regressions are usually used. However, as is commonly known, none of them are satisfactory [1McNutt L.A. Wu C. Xue X. Hafner J.P. Estimating the relative risk in cohort studies and clinical trials of common outcomes.Am J Epidemiol. 2003; 157: 940-943Crossref PubMed Scopus (1618) Google Scholar, 2Barros A.J. Hirakata V.N. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio.BMC Med Res Methodol. 2003; 3: 21Crossref PubMed Scopus (2811) Google Scholar]. Convergence problems very often arise with binomial regression models failing to provide an estimate of RR. While Poisson regression models provide conservative results (Poisson distribution is typically used for rare events). Also the conversion method proposed by the authors has been proven to be invalid [[3]Zou G. A modified Poisson regression approach to prospective studies with binary data.Am J Epidemiol. 2004; 159: 702-706Crossref PubMed Scopus (6521) Google Scholar]. For these reasons, multivariable logistic regression models, and therefore the OR, have been widely used and still are even when events are not rare.Table 1Standardized mean difference of ribavirin use, adherence and treatment timing between IL28B and HCV genotype groups. Open table in a new tab We presented in our original article the well-established crude and adjusted ORs as the measure of the associations, aware that Journal of Hepatology readership bears in mind that an OR must be not interpreted as a RR. In our paper we never discussed ORs as RRs, and, even when comparisons were reported, they referred to crude frequencies and percentages. Multivariable models were simply used to adjust for potential confounders. Furthermore, it seems that the results of the multivariable analysis on sustained virological response (SVR) left the authors at least perplexed, as the use of the exclamation mark suggests. However, here we provide some useful epidemiological explanations about confounding. The univariate ORs for IL28B CC carriers and for HCV genotype were 1.658 and 2.987, respectively. On the other hand, in the multivariable model, the possible clinical confounders, i.e. ribavirin use, adherence and treatment timing, resulted highly predictive of SVR. Furthermore we observed (see Table 1) that these very covariates were strongly unbalanced in the two IL28B groups and the two HCV genotype groups, as shown by their large standardized mean differences (an absolute value greater than 5% indicates between-group imbalance). Indeed this proves their key role as confounders, which was sufficient to move the ORs from 1.658 to 8.716, and from 2.987 to 15.610, for IL28B and HCV genotype, respectively. A key paper on sensitivity analysis for residual confounding serves as reference [[4]Lin D.Y. Psaty B.M. Krommal R.A. Assessing the sensitivity of regression results to unmeasured confounders in observational studies.Biometrics. 1998; 54: 948-963Crossref PubMed Scopus (599) Google Scholar]. For sake of completeness we also reported in Table 2 the model building, which shows how the ORs moved toward higher values and more significant p values. As a further proof, we estimated the same multivariable model using a Bayesian exact approach, implemented in WinBugs, to verify whether in the previous approach estimation problems occurred. The Bayesian exact logistic regression model, which appropriately handles zero cells strata in the adjustment process, provided overlapping results (Table 3).Table 2Model building for sustained virological response (SVR). Open table in a new tab Table 3Multivariable model for sustained virological response (SVR) estimated using a Bayesian approach. Open table in a new tab Also the ORs 95% confidence intervals troubled our colleagues. Please note that the two independent predictors, treatment timing and adherence, are continuous variables with their own metrics and results must be interpreted consequently. For example, in our study treatment timing was expressed in weeks and resulted, in the multivariable analyses, an independent predictor of treatment response with a OR = 0.937 (95% CI = 0.898–0.978, p = 0.003). If we express treatment timing in days, the association results in OR = 0.991 (95% CI = 0.985–0.997, p = 0.003). On the other hand, if we express treatment timing in months, the association results in OR = 0.753 (95% CI = 0.625–0.907, p = 0.003). Therefore the narrowness of confidence intervals for continuous covariates measures of risk is only apparently misleading. The authors correctly stated that an imperfect reporting is present in the manuscript, but not in Table 2 of the original manuscript, rather in Table 1, where the absolute counts for "No jaundice" have been inverted between Clearance and Viral Persistence groups. The corrected numbers are now reported in the following Table 4.Table 4Correction of Table 1 in "Mangia A, et al. Treatment optimization and prediction of HCV clearance in patients with acute HCV infection. J Hepatol 2013;59:221–228". Open table in a new tab The authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript.