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    "# Getting started"
   ]
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   "source": [
    "## Installation\n",
    "\n",
    "You can install `survivalstan` with `pip`:\n",
    "\n",
    "    pip install survivalstan"
   ]
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    "## Typical analysis workflow\n",
    "\n",
    "At a high level, the Survival modeling workflow proceeds in several steps:\n",
    "\n",
    "1. Obtain or simulate data: `survivalstan.sim`\n",
    "2. Select model to fit: `survivalstan.models`\n",
    "3. Transform data, if required by your model: `survivalstan.prep_data_long_surv`\n",
    "4. Fit model to data: `survivalstan.fit_stan_survival_model`\n",
    "5. Check model fit\n",
    "    - `survivalstan.utils.print_stan_summary`\n",
    "    - `survivalstan.utils.plot_pp_survival`\n",
    "6. Review inferences from model\n",
    "    - `survivalstan.utils.plot_coefs`, etc\n",
    "7. Update model: rince and repeat!\n",
    "\n",
    "Some high-level comments:\n",
    "\n",
    "* **Most if not all survivalstan functions accept a list of models.**\n",
    "    - It's rare to analyze your data with one and only one model. \n",
    "    - Often you run the same model with more interations, or you try alternate specifications of the same model.\n",
    "    - Use the optional parameter `model_cohort` to \"label\" each model fit object.\n",
    "* Not all functions will work with all models\n",
    "    - Example, `survivalstan.utils.plot_grp_coefs` will only work for models with grouped-coefficients (ie a varying-coefficient model).\n",
    "    - Similarly, `survivalstan.utils.plot_baseline_hazard` will only work for \"PEM\" models, which have semi- or non-parametrics baseline hazards.\n",
    "\n",
    "\n",
    "### Obtain or simulate data\n",
    "\n",
    "- The `survivalstan.sim` module includes several data-simulation routines.\n",
    "- typically one record per subject\n",
    "- \"survival status\" typically consists of several columns. At a minimum: time and status (1:event, 0:censor)\n",
    "\n",
    "### Select the survival model you want to fit\n",
    "\n",
    "- Survivalstan includes Stan code for a variety of models, including: \n",
    "    - Parametric [baseline hazards]() (weibull, gamma, exponential)\n",
    "    - Semi- and non-parametric baseline hazards (pem_randomwalk, pem_gamma, pem_unstructured)\n",
    "    - Estimating time-dependent effects\n",
    "    - Varying-coefficient models\n",
    "    - Stratified models\n",
    "    - Competing events models\n",
    "    - .. etc\n",
    "- Or, you may have your own Stan code - a variation of one of the above, or your own\n",
    "    - Many of the models provided by Survivalstan include helper functions for regularizing priors, etc. \n",
    "\n",
    "### Transform data into a format suitable for your model\n",
    "\n",
    "- Many of the models provided by Survivalstan (any that start with \"*pem*\") require that data be provided in a \"long\" format. See [Survival modeling with long vs wide format]()\n",
    "- You can use `prep_data_long_surv` function to transform your data into the long format prior to analysis.\n",
    "\n",
    "### Fit your model to your data: `fit_stan_survival_model`\n",
    "- (within `fit_stan_survival_model`):\n",
    "    - calls StanSurvivalData() to return a data dictionary for your stan model\n",
    "    - compiles & optionally caches the compiled model\n",
    "    - calls `pystan.stan`, `stancache.cached_model_fit` to fit the model\n",
    "    - (tries to) call `stanity.psisloo` to summarize fit \n",
    "    - (tries to) call `survivalstan.utils.extract_baseline_hazard`, `extract_grp_coefs`, and `extract_coefs`\n",
    "    - returns a data dictionary containing meta-data about your inputs & outputs\n",
    "\n",
    "### Check model fit & posterior predictive checking\n",
    "- Try `survivalstan.utils.plot_baseline_hazard`\n",
    "- Try `survivalstan.utils.plot_pp_survival`\n",
    "\n",
    "### Update the model (go back to # 2)\n",
    "\n"
   ]
  }
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