statsmodels.duration.survfunc.SurvfuncRight

class statsmodels.duration.survfunc.SurvfuncRight(time, status, entry=None, title=None, freq_weights=None, exog=None, bw_factor=1.0) [source]

Estimation and inference for a survival function.

The survival function S(t) = P(T > t) is the probability that an event time T is greater than t.

This class currently only supports right censoring.

Parameters:

time (array-like) – An array of times (censoring times or event times) status (array-like) – Status at the event time, status==1 is the ‘event’ (e.g. death, failure), meaning that the event occurs at the given value in time; status==0 indicates that censoring has occured, meaning that the event occurs after the given value in time. entry (array-like, optional An array of entry times for handling) – left truncation (the subject is not in the risk set on or before the entry time) title (string) – Optional title used for plots and summary output. freq_weights (array-like) – Optional frequency weights exog (array-like) – Optional, if present used to account for violation of independent censoring. bw_factor (float) – Band-width multiplier for kernel-based estimation. Only used if exog is provided.

surv_prob

array-like – The estimated value of the survivor function at each time point in surv_times.

surv_prob_se

array-like – The standard errors for the values in surv_prob. Not available if exog is provided.

surv_times

array-like – The points where the survival function changes.

n_risk

array-like – The number of subjects at risk just before each time value in surv_times. Not available if exog is provided.

n_events

array-like – The number of events (e.g. deaths) that occur at each point in surv_times. Not available if exog is provided.

Notes

If exog is None, the standard Kaplan-Meier estimator is used. If exog is not None, a local estimate of the marginal survival function around each point is constructed, and these are then averaged. This procedure gives an estimate of the marginal survival function that accounts for dependent censoring as long as the censoring becomes independent when conditioning on the covariates in exog. See Zeng et al. (2004) for details.

References

D. Zeng (2004). Estimating marginal survival function by adjusting for dependent censoring using many covariates. Annals of Statistics 32:4. http://arxiv.org/pdf/math/0409180.pdf

Methods

plot([ax])	Plot the survival function.
quantile(p)	Estimated quantile of a survival distribution.
quantile_ci(p[, alpha, method])	Returns a confidence interval for a survival quantile.
simultaneous_cb([alpha, method, transform])	Returns a simultaneous confidence band for the survival function.
summary()	Return a summary of the estimated survival function.