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Therefore, in addition, we have used two different sensitivity analysis approaches for handling potential bias arising from missing stage information, bearing in mind different assumptions about the potential mechanisms generating missing data.
Two cases had missing stage information.
Patients with missing stage data were excluded from the analyses.
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.
Text fields were not analyzed to determine whether they contained missing stage information.
Third, for missing stage cases, we used the multiple imputation approach [ 19].
Cases missing stage at diagnosis (n = 3,275) were excluded from these analyses.
We ran stratified, stage-specific models, treating patients with missing stage as a distinct category.
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Similar patterns of variation were also observed for extreme case scenario analysis (under the MNAR assumption of missing stage=advanced stage), except that deprivation differences in advanced stage diagnosis for breast cancer were smaller.
Repeating the analysis using the extreme case scenario approach (missing stage=advanced stage) produced similar patterns of variation for lung cancer.
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