Port SEQTaRget#173 fixes, plus further weighting and output fixes

.github/workflows/python-app.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [macos-26]
-        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14", "3.15"]
 
     steps:
     - uses: actions/checkout@v7
@@ -28,6 +28,19 @@ jobs:
     - name: Set up Python ${{ matrix.python-version }}
       run: uv python install ${{ matrix.python-version }}
 
+    # tabmat and glum have no Python 3.15 wheels yet, so they build from source.
+    # lifelines caps pandas below 3, and pandas 2.x is broken on 3.15, so override
+    # the cap and stop uv run from re-syncing it back.
+    - name: Prepare Python 3.15 build
+      if: matrix.python-version == '3.15'
+      run: |
+        brew install libomp xsimd jemalloc
+        echo "CPPFLAGS=-I$(brew --prefix libomp)/include -I$(brew --prefix xsimd)/include -I$(brew --prefix jemalloc)/include" >> "$GITHUB_ENV"
+        echo "LDFLAGS=-L$(brew --prefix libomp)/lib -L$(brew --prefix jemalloc)/lib" >> "$GITHUB_ENV"
+        echo "pandas>=3" > "$RUNNER_TEMP/uv-overrides.txt"
+        echo "UV_OVERRIDE=$RUNNER_TEMP/uv-overrides.txt" >> "$GITHUB_ENV"
+        echo "UV_NO_SYNC=1" >> "$GITHUB_ENV"
+
     - name: Install dependencies
       run: |
         uv venv --python ${{ matrix.python-version }}

CITATION.cff

@@ -18,7 +18,7 @@ authors:
     affiliation: "CAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health; Department of Biostatistics, Harvard T.H. Chan School of Public Health"
     orcid: https://orcid.org/0000-0003-1619-8456
 title: "pySEQTarget: Sequential Target Trial Emulation"
-version: 0.14.2
+version: 0.14.3
 url: https://pyseqtarget.readthedocs.io/
 repository-code: https://github.com/CausalInference/pySEQTarget
 repository-artifact: https://pypi.org/project/pySEQTarget/

docs/vignettes/end_of_followup.md

@@ -51,8 +51,8 @@ model.eof_data.select(["A", "Proportion", "SE", "95% LCI", "95% UCI",
 ┌─────┬────────────┬──────────┬──────────┬──────────┬────────────┬──────────┐
 │ A   ┆ Proportion ┆ SE       ┆ 95% LCI  ┆ 95% UCI  ┆ % Censored ┆ Subjects │
 ╞═════╪════════════╪══════════╪══════════╪══════════╪════════════╪══════════╡
-│ 0   ┆ 0.019865   ┆ 0.003014 ┆ 0.013956 ┆ 0.025773 ┆ 19.687712  ┆ 266      │
-│ 1   ┆ 0.027256   ┆ 0.002342 ┆ 0.022666 ┆ 0.031847 ┆ 28.46412   ┆ 236      │
+│ 0   ┆ 0.019865   ┆ 0.003014 ┆ 0.013956 ┆ 0.025773 ┆ 17.098809  ┆ 266      │
+│ 1   ┆ 0.027256   ┆ 0.002342 ┆ 0.022666 ┆ 0.031847 ┆ 24.81102   ┆ 236      │
 └─────┴────────────┴──────────┴──────────┴──────────┴────────────┴──────────┘
 ```
 
@@ -108,8 +108,8 @@ windowed.eof_data.select(["A", "Proportion", "95% LCI", "95% UCI",
 │ A   ┆ Proportion ┆ 95% LCI  ┆ 95% UCI  ┆ Trial-period ┆ Trial-period ┆ Trial-period ┆ % Censored │
 │     ┆            ┆          ┆          ┆ s (Eligible) ┆ s (Analysed) ┆ s (Censored) ┆            │
 ╞═════╪════════════╪══════════╪══════════╪══════════════╪══════════════╪══════════════╪════════════╡
-│ 0   ┆ 0.075704   ┆ 0.058602 ┆ 0.092805 ┆ 2946         ┆ 2523         ┆ 423          ┆ 14.358452  │
-│ 1   ┆ 0.102475   ┆ 0.0864   ┆ 0.11855  ┆ 6257         ┆ 4889         ┆ 1368         ┆ 21.863513  │
+│ 0   ┆ 0.075704   ┆ 0.058602 ┆ 0.092805 ┆ 2894         ┆ 2523         ┆ 371          ┆ 12.819627  │
+│ 1   ┆ 0.102475   ┆ 0.0864   ┆ 0.11855  ┆ 6073         ┆ 4889         ┆ 1184         ┆ 19.49613   │
 └─────┴────────────┴──────────┴──────────┴──────────────┴──────────────┴──────────────┴────────────┘
 ```
 
@@ -150,8 +150,8 @@ windowed.diagnostics["nonunique_eof"]
 │ tx_init_bas ┆ Eligible ┆ At k ┆ In window ┆ Excluded (outside ┆ Excluded (no measurement) │
 │             ┆          ┆      ┆           ┆ window)           ┆                           │
 ╞═════════════╪══════════╪══════╪═══════════╪═══════════════════╪═══════════════════════════╡
-│ 0           ┆ 2946     ┆ 2366 ┆ 157       ┆ 423               ┆ 0                         │
-│ 1           ┆ 6257     ┆ 4476 ┆ 413       ┆ 1368              ┆ 0                         │
+│ 0           ┆ 2894     ┆ 2366 ┆ 157       ┆ 371               ┆ 0                         │
+│ 1           ┆ 6073     ┆ 4476 ┆ 413       ┆ 1184              ┆ 0                         │
 └─────────────┴──────────┴──────┴───────────┴───────────────────┴───────────────────────────┘
 ```
 
@@ -272,8 +272,8 @@ perprotocol.eof_data.select(["A", "Proportion", "95% LCI", "95% UCI",
 ┌─────┬────────────┬──────────┬──────────┬────────────┬──────────┐
 │ A   ┆ Proportion ┆ 95% LCI  ┆ 95% UCI  ┆ % Censored ┆ Subjects │
 ╞═════╪════════════╪══════════╪══════════╪════════════╪══════════╡
-│ 0   ┆ 0.045069   ┆ 0.011689 ┆ 0.07845  ┆ 82.959946  ┆ 86       │
-│ 1   ┆ 0.088733   ┆ 0.065572 ┆ 0.111894 ┆ 49.592456  ┆ 211      │
+│ 0   ┆ 0.043282   ┆ 0.012381 ┆ 0.074182 ┆ 82.653766  ┆ 86       │
+│ 1   ┆ 0.088984   ┆ 0.065753 ┆ 0.112215 ┆ 48.065207  ┆ 211      │
 └─────┴────────────┴──────────┴──────────┴────────────┴──────────┘
 ```