# GSoC 2026 - Survival Models - Intent and Intro!

**URL:** <https://discourse.pymc.io/t/gsoc-2026-survival-models-intent-and-intro/17666>\
**Category:** Development\
**Created:** [March 25, 2026, 2:37pm UTC](https://discourse.pymc.io/t/gsoc-2026-survival-models-intent-and-intro/17666 "2026-03-25T14:37:19Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![vybhav72954](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/vybhav72954/32/9591_2.png) [@vybhav72954](https://discourse.pymc.io/u/vybhav72954)\
**Post date:** [March 25, 2026, 2:37pm UTC](https://discourse.pymc.io/t/gsoc-2026-survival-models-intent-and-intro/17666/1 "2026-03-25T14:37:19Z")

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Hi everyone,

I’m Vybhav Chaturvedi, interested in the Survival Models project for GSoC 2026. I’ve been actively contributing to PyMC’s infrastructure and wanted to share what I’ve done and where I think the project could go.

## Contributions

My contributions have been focused specifically on the `logccdf` i.e. censoring logic that the Survival Models would build on top of:

**Merged:**

- **[pymc#8166](https://github.com/pymc-devs/pymc/pull/8166)** — Added numerically stable `logccdf` to the LogNormal distribution. `pm.Censored` calls `logccdf` internally to compute the likelihood contribution of right-censored observations (\log S(t)). The naive approach `log(1 - CDF(t))` fails when CDF rounds to 1.0 in `float64`; this PR implements it via `log_erfc` to maintain full precision even deep in the tail. Without this, `pm.Censored(LogNormal.dist(...))` would silently produce garbage for outliers on tails.

- **[pytensor#1945](https://github.com/pymc-devs/pytensor/pull/1945)** — Fixed `Pow.impl` returning complex values for fractional powers of negative floats at the scalar level. Observed this while fixing the `logccdf` routine. This surfaced in distribution tests (Kumaraswamy), which `(-1.0) **0.01` produced a complex number. The fix replaces `x** y` with `np.power(x, y)` in scalar `Pow.impl`, aligning the scalar path with the tensor path which already uses `np.power` via `nfunc_spec`.

**Open (awaiting review):**

- **[pymc#8181](https://github.com/pymc-devs/pymc/pull/8181)** — Added `logccdf` test coverage for Censored distributions. The censoring module (`pymc/logprob/censoring.py`) already uses `_logccdf_helper` internally for right-censored `logp`, but the `logccdf` dispatch for censored distributions had no direct tests. This PR mirrors the existing `test_censored_logcdf_continuous` and `test_censored_logcdf_discrete` structure, covering all four censoring configurations (uncensored, left, right, interval) for both continuous and discrete base distributions.

## What I explored

I built a [notebook](https://github.com/vybhav72954/bayesian-sample/blob/master/bayesian_survival_analysis_pymc.ipynb) around a problem I’ve actually worked on. Before returning to academia, I was working as a healthcare consultant for 3 years. I am well-versed and very passionate about care gaps and unmet needs. Recently, I developed a clinical trial data quality platform ([Javelin.ai](https://github.com/vybhav72954/Javelin.ai)) for a Novartis-sponsored challenge that processes subject-level data across multi-site global studies and computes a Data Quality Index per subject using ICH E6(R2) weighted features. So modeling time-to-adverse-event with site-level heterogeneity and censoring is something I’ve dealt with directly.

The notebook fits a hierarchical Weibull AFT model using `pm.Censored` with site-level random effects, includes a three-way model comparison (Weibull vs Exponential vs LogNormal via LOO-CV), and demonstrates the numerical stability problem that my `logccdf` PR solved. I specifically used LogNormal as one of the comparison models to show that `pm.Censored(LogNormal.dist(...))` now works correctly for censored observations in the tail: at `t=500`, the naive `log(1 - CDF(t))` returns `-inf`, while the stable implementation via `log_erfc` stays accurate.

The notebook also revealed a practical challenge: the hierarchical censored model with `numpyro` produces ~1000 divergences in the centered parameterization. This is something a high-level survival module should handle transparently.

## API Prototype

After going through the codebase for inspiration (`censored_data`, `survival_analysis`, `weibull_aft`) and building the models of my own, i observed one bottleneck! Curernlty we have a lot of boilerplate. Every distributional assumption requires rebuilding the same model block: `pm.Data` containers, priors, linear predictor, `pm.Censored` wrapping. While it was manageblt for me when i was attemtpintg to compare three distributions, but at scale, this problem will result in a lot of redundant effort!

Building upon what wiki aready mentions, so I [prototyped a formula-based API](https://github.com/vybhav72954/bayesian-sample/blob/master/pymc_survival.py) based on CausalPy:

```python
from pymc_survival import WeibullAFT, LogNormalAFT, SurvivalModel

# one-line model specification -- no pm.Model() block needed
m = WeibullAFT(
    formula="time ~ treatment + dqi_score + (1 | site_id)",
    data=df,
    event_col="event",
).fit()

m.summary()
m.plot_survival(groups={"Control": {"treatment": 0}, "Treatment": {"treatment": 1}})

# swap distribution without rewriting the model
m_ln = LogNormalAFT(formula=..., data=df, event_col="event").fit()

# model comparison
SurvivalModel.compare({"Weibull": m, "LogNormal": m_ln})

```

The base `SurvivalModel` class handles formula parsing (`(1 | group)` for random intercepts), `pm.Censored` construction, and `ArviZ` integration. Subclasses only implement `_make_dist()` and `_survival_function()`: adding a new distribution is roughly 15 lines. The prototype runs end-to-end with Weibull, Exponential, and LogNormal.

## Questions for the mentors

Three design questions I’d appreciate feedback on before finalizing my proposal:

1. **Truncation** : The wiki mentions truncation alongside censoring. Left truncation (delayed entry) is logically and statistically different from censoring: it requires conditioning on T \> t\_{\text{entry}},. Should this be handled within the survival module’s model construction, or is it a better idea to create a more general wrapper, something like `pm.Truncated`?

2. **Semi-parametric models** : For Cox PH / piecewise exponential, the baseline hazard is discretized into intervals, which is architecturally different from parametric AFT models that just need a distribution + `pm.Censored`. Should these share the same `SurvivalModel` base class (with some flags for overrides), or would a separate class hierarchy be cleaner

3. **Formula library** : While i used a hack sort of an approach using regex, I believe for prod level code, we would want something more rigorous, can we use Bambi, or other similar libraires?

Happy to discuss any of this further. My proposal will focus on the parametric models + formula API (if applicable) as the core deliverable, with piecewise exponential / Cox PH as a stretch goal.

## About me

I’m a Masters student at IIT Kharagpur (Data Science/ML). I have two published papers in Springer journals on Brain-Computer Interfacing and physiological signal processing ([Link1](https://doi.org/10.1007/s11042-021-11650-0), [Link2](https://doi.org/10.1007/s00530-021-00786-6)) with 150+ combined citations, plus an Innovation Patent (No. 2021101097). My coursework covers Probability, Stochastic Processes, Time Series Analysis, and Machine Learning.

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**Author:** ![vybhav72954](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/vybhav72954/32/9591_2.png) [@vybhav72954](https://discourse.pymc.io/u/vybhav72954)\
**Post date:** [March 29, 2026, 5:35am UTC](https://discourse.pymc.io/t/gsoc-2026-survival-models-intent-and-intro/17666/2 "2026-03-29T05:35:53Z")

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@ricardoV94 @fonnesbeck @bwengals

Happy weekend team - Hope you are doing well! Would it be possible for you guys to share some feedback before the submission window?

I want to ensure that my proposal actually aligns with your vision for the project going forward. I have a good idea about the reposotory right now and hence i have made the proposal basis my understaidning but it would be really helpdul to get some inputs from the team!

Thank you for your guidance!