Implement yli.IntervalCensoredCox
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@ -45,7 +45,8 @@ The mandatory dependencies of this library are:
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Optional dependencies are:
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* matplotlib and [seaborn](https://seaborn.pydata.org/), for plotting functions
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* [hpstat](https://yingtongli.me/git/hpstat), for *IntervalCensoredCox*
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* [matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/), for plotting functions
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* [mpmath](https://mpmath.org/), for *beta_ratio* and *beta_oddsratio*
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* [PyCryptodome](https://www.pycryptodome.org/), for *pickle_write_encrypted* and *pickle_read_encrypted*
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* [rpy2](https://rpy2.github.io/), with R packages:
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@ -64,6 +65,7 @@ Relevant statistical functions are all directly available from the top-level *yl
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* *pearsonr*: Pearson correlation coefficient *r*
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* *ttest_ind*: Independent 2-sample *t* test
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* Regression:
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* *IntervalCensoredCox*: Model for interval-censored Cox regression
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* *PenalisedLogit*: Model for Firth penalised logistic regression
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* *regress*: Fit arbitrary regression models
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* *vif*: Compute the variance inflation factor for independent variables in regression
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@ -17,3 +17,6 @@ Global options
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.. autoattribute:: yli.config.Config.repr_is_summary
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:annotation: = True
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.. autoattribute:: yli.config.Config.hpstat_path
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:annotation: = True
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@ -20,7 +20,7 @@ from .descriptives import auto_correlations, auto_descriptives
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .graphs import init_fonts
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from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import Logit, OLS, OrdinalLogit, PenalisedLogit, regress, vif
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from .regress import IntervalCensoredCox, Logit, OLS, OrdinalLogit, PenalisedLogit, regress, vif
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, spearman, ttest_ind
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from .survival import kaplanmeier, logrank, turnbull
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from .utils import as_ordinal
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@ -33,5 +33,8 @@ class Config:
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#: If enabled, `__repr__` on test results, etc. directly calls the ``summary`` function (*bool*)
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self.repr_is_summary = True
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#: Path to hpstat binary
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self.hpstat_path = './hpstat'
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"""Global configuration singleton"""
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config = Config()
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@ -105,7 +105,7 @@ def regress(model_class, df, dep, formula, *, nan_policy='warn', bool_baselevels
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:type model_class: :class:`yli.regress.RegressionModel` subclass
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:param df: Data to perform regression on
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:type df: DataFrame
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:param dep: Column in *df* for the dependent variable (numeric)
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:param dep: Column(s) in *df* for the dependent variable (numeric)
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:type dep: str
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:param formula: Patsy formula for the regression model
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:type formula: str
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@ -184,13 +184,16 @@ def regress(model_class, df, dep, formula, *, nan_policy='warn', bool_baselevels
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def df_to_dmatrices(df, dep, formula, nan_policy):
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# Check for/clean NaNs in input columns
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columns = [dep] + cols_for_formula(formula, df)
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columns = cols_for_formula(dep, df) + cols_for_formula(formula, df)
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df = df[columns]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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df[dep], dep_categories = as_numeric(df[dep])
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if '+' in dep:
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dep_categories = None
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else:
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df[dep], dep_categories = as_numeric(df[dep])
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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@ -575,6 +578,92 @@ def raw_terms_from_statsmodels_result(raw_result):
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# ------------------------
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# Concrete implementations
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class IntervalCensoredCox(RegressionModel):
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"""
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Interval-censored Cox regression
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Uses hpstat *intcox* command.
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**Example:**
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.. code-block::
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df = pd.DataFrame(...)
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yli.regress(yli.IntervalCensoredCox, df, 'Left_Time + Right_Time', 'A + B + C + D + E + F + G + H')
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.. code-block:: text
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Interval-Censored Cox Regression Results
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===================================================================
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Dep. Variable: Left_Time + Right_Time | No. Observations: 1124
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Model: Interval-Censored Cox | No. Events: 133
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Date: 2023-04-17 | Df. Model: 8
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Time: 22:30:40 | Pseudo R²: 0.00
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Std. Errors: OPG | LL-Model: -613.21
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| LL-Null: -615.81
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| p (LR): 0.82
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===================================================================
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exp(β) (95% CI) p
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----------------------------------
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A 0.83 (0.37 - 1.88) 0.66
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B 1.08 (0.81 - 1.46) 0.60
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C 0.49 (0.24 - 1.00) 0.052
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D 0.79 (0.42 - 1.50) 0.48
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E 0.87 (0.40 - 1.85) 0.71
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F 0.64 (0.28 - 1.45) 0.29
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G 1.07 (0.44 - 2.62) 0.88
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H 1.23 (0.48 - 3.20) 0.67
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----------------------------------
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The output summarises the result of the regression.
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**Reference:** Zeng D, Mao L, Lin DY. Maximum likelihood estimation for semiparametric transformation models with interval-censored data. *Biometrika*. 2016;103(2):253–71. `doi:10.1093/biomet/asw013 <https://doi.org/10.1093/biomet/asw013>`_
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"""
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@property
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def model_long_name(self):
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return 'Interval-Censored Cox Regression'
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@property
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def model_short_name(self):
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return 'Interval-Censored Cox'
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@classmethod
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def fit(cls, data_dep, data_ind):
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if len(data_dep.columns) != 2:
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raise ValueError('IntervalCensoredCox requires left and right times')
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# Drop explicit intercept term
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if 'Intercept' in data_ind:
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del data_ind['Intercept']
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result = cls()
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result.exp = True
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result.cov_type = 'OPG'
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result.nevents = np.isfinite(data_dep.iloc[:, 1]).sum()
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result.dof_model = len(data_ind.columns)
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# Export data to CSV
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csv_buf = io.StringIO()
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data_dep.join(data_ind).to_csv(csv_buf, index=False)
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csv_str = csv_buf.getvalue()
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# Run intcens binary
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proc = subprocess.run([config.hpstat_path, 'intcox', '-', '--output', 'json'], input=csv_str, capture_output=True, encoding='utf-8', check=True)
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raw_result = json.loads(proc.stdout)
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z_critical = -stats.norm.ppf(config.alpha / 2)
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result.terms = {raw_name: SingleTerm(
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raw_name=raw_name,
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beta=Estimate(raw_param, raw_param - z_critical * raw_se, raw_param + z_critical * raw_se),
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pvalue=stats.norm.cdf(-np.abs(raw_param) / raw_se) * 2
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) for raw_name, raw_param, raw_se in zip(data_ind.columns, raw_result['params'], raw_result['params_se'])}
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result.ll_model = raw_result['ll_model']
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result.ll_null = raw_result['ll_null']
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return result
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class Logit(RegressionModel):
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"""
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Logistic regression
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