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29, 30 We showed that a clinically based algorithm for tumor stage, allowing for some degree of missing data, could reduce the proportion of tumors of unknown stage markedly.
We explored potential bias arising from missing stage information using multiple imputation of missing stage repeating the analysis in a complete dataset including patients with imputed stage [ 7, 14, 15].
Stage-specific survival was lower after imputation of missing stage, particularly for late-stage disease, and especially in the UK regions, where over 25% of patients had missing data on stage.
However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases.
Detailed analysis of filtered-out probes shows that 60% of excluded probes in Stage 1 were detected in Stage 4, and vice versa 65% of missing spots in Stage 4 were present in Stage 1.
Overall, the reports demonstrated a substantial variation in the completeness of TNM stage registration in the DCR according to cancer site, and the proportions of missing stage information were generally not trivial.
Unfortunately, this study is limited in determining the full impact of stage by the screening programme as it was not possible to ascertain the stage of every tumour in the cohort and, although the trend for the screening programme to identify tumours at an early stage was strong, it has not been conclusively proven due to relatively large amounts of missing staging data.
Some registries reported high levels of missing stage or size data [ 10].
The findings were robust to multiple imputation of missing stage (under the MAR assumption).
The largest percentage of missing stage data (97%) was observed for NHL.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com