Top ten errors of statistical analysis in observational studies for cancer research

Observational studies using registry data make it possible to compile quality information and can surpass clinical trials in some contexts. However, data heterogeneity, analytical complexity, and the diversity of aspects to be taken into account when interpreting results makes it easy for mistakes to be made and calls for mastery of statistical methodology. Some questionable research practices that include poor analytical data management are responsible for the low reproducibility of some results; yet, there is a paucity of information in the literature regarding specific statistical pitfalls of cancer studies. In addition to proposing how to avoid or solve them, this article seeks to expose ten common problematic situations in the analysis of cancer registries: convenience, dichotomization, stratification, regression to the mean, impact of sample size, competing risks, immortal time and survivor bias, management of missing values, and data dredging

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Anthracycline-based triplets do not improve the efficacy of platinum-fluoropyrimidine doublets in first-line treatment of advanced gastric cancer: real-world data from the AGAMEMON National Cancer Registry.

BACKGROUND: Although anthracycline-based triplets are one of the most widely used schedules to treat advanced gastric cancer (AGC), the benefit of including epirubicin in these therapeutic combinations remains unknown. This study aims to evaluate both the efficacy and tolerance of triplets with epirubicin vs. doublets with platinum-fluoropyrimidine in a national AGC registry. METHODS: Patients with AGC treated with polychemotherapy without trastuzumab at 28 hospitals in Spain between 2008 and 2016 were included. The effect of anthracycline-based triplets against doublets was evaluated by propensity score matching (PSM) and Cox proportional hazards (PH) regression. RESULT: A total of 1002 patients were included (doublets, n = 653; anthracycline-based triplets, n = 349). The multivariable Cox PH regression failed to detect significantly increased OS in favor of triplets with anthracyclines: HR 0.90 (95% CI, 0.78-1.05), p = 0.20035. After PSM, the sample contained 325 pairs with similar baseline characteristics. This method was also unable to reveal an increase in OS: 10.5 (95% CI, 9.7-12.3) vs. 9.9 (95% CI, 9.2-11.4) months, HR 0.91 (CI 95%, 0.76-1.083), and (log-rank test, p = 0.226). Response rates (42.1 vs. 33.1%, p = 0.12) and PFS (HR 0.95, CI 95%, 0.80-1.13, log-rank test, p = 0.873) were not significantly higher with epirubicin-based regimens. The triplets were associated with greater grade 3-4 hematological toxicity, and increased hospitalization due to toxicity by 68%. The addition of epirubicin is viable, but 23.7% discontinued treatment because of adverse effects or patient decision. CONCLUSION: Anthracyclines added to platinum-fluoropyrimidine doublets did not improve the response rate or survival outcomes in patients with AGC but entailed greater toxicity.

Top ten errors of statistical analysis in observational studies for cancer research.

Observational studies using registry data make it possible to compile quality information and can surpass clinical trials in some contexts. However, data heterogeneity, analytical complexity, and the diversity of aspects to be taken into account when interpreting results makes it easy for mistakes to be made and calls for mastery of statistical methodology. Some questionable research practices that include poor analytical data management are responsible for the low reproducibility of some results; yet, there is a paucity of information in the literature regarding specific statistical pitfalls of cancer studies. In addition to proposing how to avoid or solve them, this article seeks to expose ten common problematic situations in the analysis of cancer registries: convenience, dichotomization, stratification, regression to the mean, impact of sample size, competing risks, immortal time and survivor bias, management of missing values, and data dredging.

Nomogram-based prediction of survival in patients with advanced oesophagogastric adenocarcinoma receiving first-line chemotherapy: a multicenter prospective study in the era of trastuzumab.

BACKGROUND: To develop and validate a nomogram and web-based calculator to predict overall survival (OS) in Caucasian-advanced oesophagogastric adenocarcinoma (AOA) patients undergoing first-line combination chemotherapy. METHODS: Nine hundred twenty-four AOA patients treated at 28 Spanish teaching hospitals from January 2008 to September 2014 were used as derivation cohort. The result of an adjusted-Cox proportional hazards regression was represented as a nomogram and web-based calculator. The model was validated in 502 prospectively recruited patients treated between October 2014 and December 2016. Harrell's c-index was used to evaluate discrimination. RESULTS: The nomogram includes seven predictors associated with OS: HER2-positive tumours treated with trastuzumab, Eastern Cooperative Oncology Group performance status, number of metastatic sites, bone metastases, ascites, histological grade, and neutrophil-to-lymphocyte ratio. Median OS was 5.8 (95% confidence interval (CI), 4.5-6.6), 9.4 (95% CI, 8.5-10.6), and 14 months (95% CI, 11.8-16) for high-, intermediate-, and low-risk groups, respectively (P<0.001), in the derivation set and 4.6 (95% CI, 3.3-8.1), 12.7 (95% CI, 11.3-14.3), and 18.3 months (95% CI, 14.6-24.2) for high-, intermediate-, and low-risk groups, respectively (P<0.001), in the validation set. The nomogram is well-calibrated and reveals acceptable discriminatory capacity, with optimism-corrected c-indices of 0.618 (95% CI, 0.591-0.631) and 0.673 (95% CI, 0.636-0.709) in derivation and validation groups, respectively. The AGAMENON nomogram outperformed the Royal Marsden Hospital (c-index=0.583; P=0.00046) and Japan Clinical Oncology Group prognostic indices (c-index=0.611; P=0.03351). CONCLUSIONS: We developed and validated a straightforward model to predict survival in Caucasian AOA patients initiating first-line polychemotherapy. This model can contribute to inform clinical decision-making and optimise clinical trial design.