Exp consistent lifespans

crcsim/analysis.py

@@ -970,11 +970,11 @@ def compute_pop_rates(self, status_arrays: list):
         """
         # Sum all of the person status arrays to get an array of counts of the number of
         # people in each status for each year.
-        statuses: np.ndarray = sum(status_arrays)
+        status_array: np.ndarray = sum(status_arrays)
 
         # Convert to DataFrame so we can index by column name
         statuses = pd.DataFrame(
-            statuses,
+            status_array,
             columns=[
                 "alive",
                 "crc_death",

crcsim/experiment/README.md

@@ -1,17 +1,8 @@
-# Replication of the CRCCP intervention scenarios
+# Testing changes to expected lifespan calculation
 
-This experiment is a replication of the CRCCP compliance intervention experiment, which was conducted prior to open-sourcing the model and making some changes to the AWS infrastructure. We are replicating this experiment to ensure continuity after those changes.
+This experiment tests new `crcsim` functionality to make expected lifespans consistent when the model is run with a given random seed. 
 
-The CRCCP compliance intervention experiment examines the cost-effectiveness of interventions designed to improve compliance with routine screening.
-
-The experiment is designed around 8 health centers (labeled FHQC1-FHQC8), each having its own baseline compliance rate, intervention cost, and post-intervention compliance rate.
-
-We don't model the intervention explicitly. In other words, we didn't add any code to the model to implement the intervention. Instead, we model the intervention by assuming it leads to a change in the compliance rate, and so we run a pair of simulations: one using the baseline compliance rate and another using the post-intervention compliance rate. Any differences in outcomes can therefore be attributed to the intervention.
-## Scenarios
-
-Includes 2 scenarios per health center: one baseline scenario and one intervention scenario. The baseline scenarios are based on real data and the intervention scenarios include a hypothetical increase in screening compliance rates.
-
-The scenarios are created by `prepare.py`. This script reads a set of base parameters defined in `crcsim/experiment/parameters.json`, modifies them to create the scenarios, and saves them in a directory structure that will eventually be copied to AWS.
+See https://github.com/RTIInternational/crcsim/pull/18 for rationale. 
 
 ## Defining new experiments
 
@@ -53,7 +44,7 @@ The subdirectories and files in `scenarios/` must be uploaded to AWS S3 for the
 
 To upload the files to S3, run
 ```
-aws s3 cp ./scenarios s3://crcsim-exp-crccp-replication/scenarios --recursive
+aws s3 cp ./scenarios s3://crcsim-exp-consistent-lifespans/scenarios --recursive
 ```
 *(Another note: this manual step is necessary because `boto3` does not include functionality to upload a directory to S3 recursively. Future experiments could improve this workflow by writing a function to upload the directory recursively in `prepare.py`. Or submit a patch to resolve https://github.com/boto/boto3/issues/358)*
 
@@ -98,7 +89,7 @@ Most of the AWS architecture was built via the AWS Console. As such, there is no
 
 ### S3
 
-We used the S3 console to create the `crcsim-exp-template` bucket to store output files generated by simulation jobs.
+We used the S3 console to create the `crcsim-exp-consistent-lifespans` bucket to store output files generated by simulation jobs.
 
 ### IAM
 

crcsim/experiment/parameters.json

@@ -23,7 +23,7 @@
   "proportion_survive_clin3": 0,
   "proportion_survive_clin4": 0,
 
-  "cost_polypectomy": 170,
+  "cost_polypectomy": 650,
   "cost_polyp_pathology": 65,
 
   "cost_treatment_stage1_initial": 34258,
@@ -71,31 +71,31 @@
   "tests": {
     "FIT": {
       "proportion": 1.0,
-      "routine_start": 50,
+      "routine_start": 45,
       "routine_end": 75,
       "routine_freq": 1,
       "specificity": 0.97,
       "sensitivity_polyp1": 0.07,
       "sensitivity_polyp2": 0.07,
       "sensitivity_polyp3": 0.22,
       "sensitivity_cancer": 0.74,
-      "cost": 22,
+      "cost": 27,
       "proportion_perforation": 0,
       "cost_perforation": 0,
       "compliance_rate_given_prev_compliant":     [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],
       "compliance_rate_given_not_prev_compliant": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]
     },
     "Colonoscopy": {
       "proportion": 0.0,
-      "routine_start": 50,
+      "routine_start": 45,
       "routine_end": 75,
       "routine_freq": 10,
       "specificity": 0.86,
       "sensitivity_polyp1": 0.75,
       "sensitivity_polyp2": 0.85,
       "sensitivity_polyp3": 0.95,
       "sensitivity_cancer": 0.95,
-      "cost": 700,
+      "cost": 1608,
       "proportion_perforation": 0.001,
       "cost_perforation": 6487,
       "compliance_rate_given_prev_compliant":     [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],
@@ -107,9 +107,9 @@
   "surveillance_test": "Colonoscopy",
   "polypectomy_proportion_lethal": 0.00002,
 
-  "cost_discount_age": 50,
+  "cost_discount_age": 45,
   "cost_discount_rate": 0.03,
-  "lifespan_discount_age": 50,
+  "lifespan_discount_age": 45,
   "lifespan_discount_rate": 0.03,
 
   "death_rate_black_female_ages":  [        0,        1,        2,        3,        4,        5,        6,        7,        8,        9,       10,       11,       12,       13,       14,       15,       16,       17,       18,       19,       20,       21,       22,       23,       24,       25,       26,       27,       28,       29,       30,       31,       32,       33,       34,       35,       36,       37,       38,       39,       40,       41,       42,       43,       44,       45,       46,       47,       48,       49,       50,       51,       52,       53,       54,       55,       56,       57,       58,       59,       60,       61,       62,       63,       64,       65,       66,       67,       68,       69,       70,       71,       72,       73,       74,       75,       76,       77,       78,       79,       80,       81,       82,       83,       84,       85,       86,       87,       88,       89,       90,       91,       92,       93,       94,       95,       96,       97,       98,       99,      100 ],

crcsim/experiment/requirements.in

@@ -1,5 +1,5 @@
 # Clone the specific commit of the model that you want to use for the experiment
-git+https://github.com/RTIInternational/crcsim.git@55312a08eb176556555ce5ecb99ba9628d0afc3e
+git+https://github.com/RTIInternational/crcsim.git@6c264ca5e35c1b9ae20a6e4b95818f6e00970d71
 
 fire
 pandas==2.1.1

crcsim/experiment/requirements.txt

@@ -36,7 +36,7 @@ colorama==0.4.4
     # via awscli
 contourpy==1.1.1
     # via matplotlib
-crcsim @ git+https://github.com/RTIInternational/crcsim.git@55312a08eb176556555ce5ecb99ba9628d0afc3e
+crcsim @ git+https://github.com/RTIInternational/crcsim.git@6c264ca5e35c1b9ae20a6e4b95818f6e00970d71
     # via -r requirements.in
 cycler==0.12.0
     # via matplotlib

crcsim/experiment/run_iteration.sh

@@ -12,7 +12,7 @@ if [ ! -d "$output_dir" ]; then
   mkdir $output_dir
 fi
 
-aws s3 cp "s3://crcsim-exp-crccp-replication/scenarios/$scenario/params.json" "./params.json" 
+aws s3 cp "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/params.json" "./params.json" 
 
 crc-simulate \
     --npeople=$npeople \
@@ -23,5 +23,5 @@ crc-simulate \
 crc-analyze \
     --params-file=./params.json &&
 
-aws s3 cp ./results.csv "s3://crcsim-exp-crccp-replication/scenarios/$scenario/results_$iteration.csv"
-aws s3 cp ./output.csv "s3://crcsim-exp-crccp-replication/scenarios/$scenario/output_$iteration.csv"
+aws s3 cp ./results.csv "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/results_$iteration.csv"
+aws s3 cp ./output.csv "s3://crcsim-exp-consistent-lifespans/scenarios/$scenario/output_$iteration.csv"