When randomized trials and observational data disagree: The case of preoperative versus postoperative chemoradiotherapy for esophageal cancer
Bibliographic record
Abstract
We read with interest the recent study by Wojcieszynski and colleagues, who used the Surveillance, Epidemiology, and End Results (SEER) database to assess “whether the sequencing of chemoradiotherapy (CRT) has an effect on survival and cardiopulmonary mortality” for esophageal cancer.1 Although chemotherapy data are not available in SEER, the authors use the delivery of radiotherapy (RT) as a surrogate for CRT, assuming that RT is “almost always administered concurrently with chemotherapy.”1 The findings are striking: the use of preoperative RT was associated with a 10% improvement in 5-year survival compared with postoperative RT (P < .0001). The sequencing of CRT relative to surgery is an area of clinical uncertainty, with arguments supporting both approaches. One advantage of neoadjuvant CRT is that RT volumes are usually smaller; conversely, with the adjuvant approach, some patients may avoid CRT if their surgical staging indicates very early stage disease. The authors indicate that no randomized controlled trials (RCTs) are available to address this issue; but indeed there is at least 1, and the differences in results are quite informative. That trial randomized 238 patients who were undergoing surgery for locally advanced thoracic squamous cell esophageal carcinoma to preoperative CRT, postoperative CRT, or surgery alone. There were no differences in survival between the preoperative and postoperative arms, and both were superior to surgery.2 Although there are some differences between the baseline populations in the 2 studies, we are faced with a situation in which RCT data conflict with the results from a population-based study. Conceivably, the 2 studies may differ because of a lack of generalizability of the RCT, but it is more likely that the differences arise because randomization can control for both measured and unmeasured confounders, whereas an observational study cannot control for the latter. One unmeasured confounder that threatens the validity of the SEER study is comorbidity. In some situations, controlling for comorbidity can reverse the results of an observational study: for example, SEER data indicate that postoperative RT for N2 nonsmall cell lung cancer improves overall survival,3 whereas the more robust SEER-Medicare data (controlling for comorbidity) indicate a trend toward harm.4 Furthermore, radiation dose is not available in SEER, and some patients in the postoperative cohort in the study by Wojcieszynski et al actually may have received RT for palliation of early recurrence. The optimal timing CRT for esophageal cancer remains uncertain, and an ongoing RCT is comparing quality-of-life outcomes with the 2 approaches.5 We encourage additional RCTs to further examine survival outcomes between the 2 groups. David A. Palma, MD, MSc, PhD1George B. Rodrigues, MD, MSc1Richard Malthaner, MD, MSc2 1Department of Radiation Oncology, London Health Sciences Center, London, Ontario, Canada 2Department of Thoracic Surgery, London Health Sciences Center, London, Ontario, Canada
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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 | 0.726 | 0.888 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.012 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.039 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".