Bibliographic record
Abstract
To the Editor: I read with interest the paper by Jones et al. (1Jones DG Taylor AM Enkiri SA et al.Extent and severity of coronary disease and mortality in patients with end‐stage renal failure evaluated for renal transplantation.Am J Transplant. 2009; 9: 1846-1852Crossref PubMed Scopus (17) Google Scholar) in the August 2009 issue of the American Journal of Transplantation. One of their main conclusions is that kidney transplantation affords a dramatic survival benefit for dialysis patients on the waiting list regardless of the extent of their coronary artery disease. I found the paper unclear about two important methodologic issues that may significantly impact on the validity of the authors’ inferences. First, there is no mention in the Methods section that kidney transplantation was handled as a time‐dependent covariate in the multivariable Cox proportional hazards model. Assuming this was not done, the marked reduction in the relative hazard of death for patients receiving kidney transplants shown in Table 3 (i.e. HR 0.191, p < 0.001) is likely a substantial overestimate of the protective effect of transplantation. By definition, patients who ultimately received a kidney transplant had to survive the waitlisting period (i.e. they were ‘immortal’ during this time). Therefore, attributing this waiting time to the transplanted group (instead of the group remaining on the waiting list) will exaggerate the survival benefit of transplantation. This ‘immortal time bias’ in observational studies has been previously recognized and described in the medical literature (2Suissa S Effectiveness of inhaled corticosteroids in chronic obstructive pulmonary disease: Immortal time bias in observational studies.Am J Respir Crit Care Med. 2003; 168: 49-53Crossref PubMed Scopus (217) Google Scholar, 3Sylvestre MP Huszti E Hanley JA Do OSCAR winners live longer than less successful peers? A reanalysis of the evidence.Ann Intern Med. 2006; 145: 361-363Crossref PubMed Scopus (114) Google Scholar, 4Shariff SZ Cuerden MS Jain AK Garg AX The secret of immortal time bias in epidemiologic studies.J Am Soc Nephrol. 2008; 19: 841-843Crossref PubMed Scopus (134) Google Scholar). A time‐dependent Cox regression model would ensure that the person‐time experience on the waiting list for those patients who eventually received a kidney transplant is properly attributed to the wait‐listed state. This methodology can also be extended to the construction of Kaplan–Meier survival curves (5Snapinn SM Jiang Q Iglewicz B Illustrating the impact of a time‐varying covariate with an extended Kaplan‐Meier estimator.Am Stat. 2005; 59: 301-307Crossref Scopus (165) Google Scholar). Second, the etiologic question of the reduction in mortality associated with kidney transplantation in dialysis patients possessing various degrees of coronary artery disease is one of effect modification or interaction. In this case, an interaction term for the extent of coronary artery disease and kidney transplantation should have been incorporated into the Cox regression model so that the relative hazard of death for those receiving (vs. not receiving) a kidney transplant can be compared across coronary artery disease burden groups. From what can be discerned in the Methods and Results sections, this was not done and thus the authors’ assertion is not supported by their data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".