guanrank

Python implementation of the GuanRank hazard ranking algorithm. Assigns a continuous hazard rank to each subject in a survival dataset — higher rank means higher risk of earlier event. Ranks can be used as regression targets for machine learning models in place of raw, right-censored survival times.

Algorithm

GuanRank (Huang et al., 2017) collapses right-censored survival data into a single scalar hazard per subject:

1. Kaplan-Meier curve. Estimate the survival function SR(t) from all observed event/censoring times.
2. Pairwise scoring. For every ordered pair of subjects, assign a score in [0, 1] under six rules. When the event ordering is unambiguous (both observed, or one event clearly precedes the other), the earlier-event subject scores 1 and the other 0. When censoring makes the ordering uncertain, the score is the Kaplan-Meier conditional probability ρ(t₁, t₂) = 1 − SR(t₂)/SR(t₁) that the event fell in the ambiguous interval.
3. Raw rank. Sum each subject's pairwise scores.
4. Normalization. Divide every raw rank by the largest raw rank in the training set, so the highest-hazard subject maps to 1.0.

Input / output

• Inputs — T: time-to-event or censoring time per subject; E: event indicator (1 = event observed, 0 = right-censored).
• Output — one normalized hazard rank per subject, in input order. In-sample ranks lie in (0, 1], with the highest-hazard subject at 1.0. Out-of-sample ranks from GuanRank.transform share the training scale and may exceed 1.0 when a held-out subject is riskier than every training subject.

The pairwise rules are documented in guankrank_alg.md.

Citation

Huang et al. (2017). Complete hazard ranking to analyze right-censored data: An ALS survival study. PLOS Comp Bio, 15(1), 41–51. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005887

Installation

pip install guanrank
# or with uv
uv add guanrank

Usage

Functional API

from guanrank import guanrank

T = [5.0, 3.0, 8.0, 3.0]  # time-to-event or censoring times
E = [1,   1,   0,   0  ]  # 1 = event, 0 = censored

ranks = guanrank(T, E)

Sklearn-style API (train/test split)

from guanrank import GuanRank

gr = GuanRank()
train_ranks = gr.fit_transform(T_train, E_train)
test_ranks = gr.transform(T_test, E_test)

fit stores the training cohort's Kaplan-Meier curve and normalization constant (its maximum raw rank); transform scores new subjects against the training cohort and divides by that same constant, keeping held-out ranks on the training scale.