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includes RMST, difference in RMST and confidence intervals #1526

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includes RMST, difference in RMST and confidence intervals #1526

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e291356
includes RMST, difference in RMST and confidence intervals
bayesfactor May 22, 2023
f447b26
Update statistics.py
bayesfactor May 22, 2023
107da27
update rmst calculation
bayesfactor Nov 12, 2023
514e898
moved the event_table property into the base fitter class so all fitt…
bayesfactor Nov 23, 2023
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20 changes: 8 additions & 12 deletions lifelines/statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -375,29 +375,25 @@ def restricted_mean_survival_time(point_in_time, fitterA) -> pd.Series:
results
"""
ft = pd.DataFrame({"time": fitterA.timeline})
data = pd.DataFrame({"time": np.array(fitterA.durations), "event": np.array(fitterA.event_observed)})
# print(data)
ft.index = ft.time
ft["n_risk"] = [sum(data.time >= i) for i in ft.index]
ft["n_risk"] = fitterA.event_table.at_risk
ft["surv"] = fitterA.survival_function_

n_event = data[data.event == 1].groupby("time").count()
n_event = n_event.join(ft["time"], how="right").drop("time", axis=1)
n_event.event.fillna(0, inplace=True)
# print(n_event)
n_event = pd.merge(fitterA.event_table.observed, ft["time"], how='right', left_index=True, right_index=True).drop('time', axis=1)

idx = ft.time <= point_in_time

wk_time = sorted(ft.time[idx].index.tolist() + [point_in_time])
wk_surv = ft.surv[idx]
wk_n_risk = ft.n_risk[idx]
wk_n_event = n_event[ft.time <= point_in_time]
time_diff = np.diff([0] + wk_time)
areas = time_diff * ([1] + wk_surv.tolist())
time_diff = np.diff(wk_time)
areas = time_diff * wk_surv
rmst = sum(areas)

wk_var = wk_n_event.event / (wk_n_risk * (wk_n_risk - wk_n_event.event))
wk_var = wk_var.tolist() + [0]
rmst_var = sum((np.flip(areas[1:])).cumsum() ** 2 * np.flip(wk_var)[1:])
wk_var = wk_n_event.observed / (wk_n_risk * (wk_n_risk - wk_n_event.observed))
wk_var = wk_var.replace(np.inf, 0).tolist()[1:] + [0]
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this is the part that fixes the NaN issue. By adding .replace(np.inf, 0), the confidence intervals are not NaN now.

rmst_var = sum((np.flip(areas.values[1:])).cumsum() ** 2 * np.flip(wk_var)[1:])
rmst_se = np.sqrt(rmst_var)
z = stats.norm.ppf(1 - fitterA.alpha / 2)
out = pd.Series(
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