Worshipping at the Altar of St. Cox: WHO Adjusts the Adjustments?

s), there is no opportunity to muse on the nature of adjustments. The authors, to their credit, do not mention the proposed survival benefit of hypocalcemia in the abstract. This did not dissuade the speaker from offering multivariable-adjusted serum calcium as serum calcium, however, in trying to make his point about the purported dangers of calcium-containing phosphate binders. This leads to the recent analysis by Jaar and colleagues of outcome of peritoneal dialysis (PD) and HD patients (4). The difficulty in trying to determine if PD is as life prolonging as HD lies in the confounding effect of patient comorbidity. As evidenced by the NECOSAD group, a randomized controlled trial of HD versus PD in patients controlled for comorbid factors is almost impossible to carry out (5). Dialysis is such an imposition into daily life that patients cannot tolerate being assigned to a modality. In light of this, investigators have sought to compare outcome of the two modalities by controlling for comorbid conditions post hoc. The study by Jaar and colleagues was a prospective cohort analysis of outcome of 1041 patients starting dialysis in the United States (4). After a mean of almost 21⁄2 years, the mortality rate for HD patients was 24.4% and for PD patients 21.2%. While this outcome would suggest that outcome is similar between the two modalities, it is clear that the patients who undertook PD were a healthier group. For example, the mean age of the PD cohort was 53.7 years, more than 5 years younger than the HD cohort. Cardiovascular disease was listed as a comorbidity in 49.3% of the PD group compared to almost 60% of the patients receiving HD. So, while the percentage of those dying was smaller (although not statistically significant) in PD patients, some kind of adjustment must be made for their intrinsically healthier status. In addition to corrections made by Cox multivariable analysis, further adjustments were made by a propensity score model. This appears to adjust for characteristics that are associated with the likelihood of use of one versus the other dialysis modality. The patients were divided into tertiles of probability of being treated initially with PD. This is a difficult concept for a non-statistician, especially in trying to understand what this analysis adds to the multivariate one. In this study, patients with the highest propensity for starting PD had a better case-mix profile with respect to age, residual renal function, and laboratory data. Somewhat puzzling is the next result: “However, within each tertile, patients undergoing peritoneal dialysis differed little from those undergoing hemodialysis.except that within the highest tertile of propensity score, patients undergoing peritoneal dialysis were more likely to be high school graduates, married, and employed. ” Those are certainly interesting demographic observations but it is unclear how those factors would figure into mortality outcome data. Nonetheless, results were adjusted two ways: by the multivariable model and by the propensity score model. The authors report the following: “After adjustment, among patients in the highest tertile of propensity for receiving peritoneal dialysis (which includes those with an overall better case mix profile), the risk for death did not differ between those undergoing peritoneal dialysis and those undergoing hemodialysis.However, the risk for death was greater in patients undergoing peritoneal dialysis than those undergoing hemodialysis in the lowest and intermediate tertiles of propensity.but the association was statistically significant only for the intermediate tertile. ” If that were not confusing enough, the authors point out that the risk of death was no different for the two modalities in comparisons of the lowest and highest propensity tertiles, or between the middle and lowest tertiles. Now, this may be clear to many people tracking the literature, but not to me. In the multivariate model addressing the second year on dialysis, there was no difference in death hazard in the unadjusted data. Interestingly, adjustment for demographic characteristics or clinical/treatment factors led to a lower (but not statistically significant) death hazard for PD patients (0.77 and 0.84). However, when adjusted for “laboratory values” the death hazard flips to 2.34! It is not clear what it is about laboratory values that should dramatically reverse the death hazard in the second year of dialysis. For example, 5 months after starting dialysis, mean C-reactive protein level was 3.92 mg/L in those on HD, compared with 3.18 mg/L in those on PD. If the difference in C-reactive protein is actually the result of HD and is, say, reflective of the inflammatory nature of this process, is it reasonable to “control” for it in statistical analysis? If the hazard ratio changes so dramatically just by adjusting for laboratory values, could it not be a problem with the analysis itself? However, like the $55 bar of soap, or the good outcome associated with hypocalcemia, do we assume that since it emanated from a computer it must be so? Statisticians can perform an impressive array of analyses, but