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When survival curve estimation fails and consequently a test fails within survmean, this is what the user sees:
The total person-time was zero in some survival intervals, when summed to the variable(s) 'sexaGwHW68X5m', 'agegrsBcfXLlUQx' (i.e. over all other variables, if any) _non-consecutively_, i.e. some intervals after an empty interval had person-time in them. Keeping all survival intervals with some estimates as NA for inspection.
Some cumulative surv.obs were zero or NA:
sexaGwHW68X5m agegrsBcfXLlUQx first.bad.surv.int last.bad.surv.int surv.obs
1: 0 [0,50) 34 108 NaN
2: 0 [50,60) 15 108 NaN
3: 0 [60,70) 68 108 NaN
4: 0 [70,Inf) 44 108 NaN
5: 1 [50,60) 23 108 NaN
6: 1 [60,70) 22 108 NaN
7: 1 [70,Inf) 75 108 NaN
Error in if (any(mi$surv > 1)) { : missing value where TRUE/FALSE needed
At the very least if (any(mi$surv > 1)) should be robust for NA values. Much more preferably survmean should either fail when there are NA values (with human-readable error msg) or automatically try to adapt to the situation (remove all unusable estimates, shift extrapolation to start earlier).
The text was updated successfully, but these errors were encountered:
When survival curve estimation fails and consequently a test fails within
survmean
, this is what the user sees:At the very least
if (any(mi$surv > 1))
should be robust for NA values. Much more preferablysurvmean
should either fail when there are NA values (with human-readable error msg) or automatically try to adapt to the situation (remove all unusable estimates, shift extrapolation to start earlier).The text was updated successfully, but these errors were encountered: