wdempsey / sense2stop-lvm Goto Github PK

• Convert eod-puffmarker to event-eda
• Combine self-report and random EMA
• Build first-smoking day survival curve
• Build # of smoking events per day since first lapse

Project the scale parameter sigma into a proper window (low of 2.38^2/k * 10^(-6) and high 10)

• Fix sr-htmg.ipy to run with self-report and get final estimates
• Fix random-htmg.ipy to run with final random EMA file and get estimates
• Update readme with results

1. Finish sr_eod.ipynb for EOD given SR
2. Finish random_eod.ipynb for EOD given random EMA
3. Finish pM_eod.ipynb for EOD given puffMarker
4. Update README with results

1. Construct pM_confirmation.ipynb
2. For every SR or random EMA 'Yes', what percent are NOT confirmed by puffMarker
3. For every random EMA 'No', what percent had a puffMarker detection go off in that window
4. Report in README what the implied false positive, false negative rates could be

Add a survival comparison at day level under random, sr, and eod versus combined

See how it would impact the curve

• Update README so that symbolic link instructions are included
• These are used for data pre-processing and to tell the system where "final" is as well

1. Create a file 'sr_confirmation.ipynb'
2. We can use random EMA to get at the
   • If random EMA is "Yes", then the person didn't self-report
   • This gives us P( SR = 0 | event) = # of random Yes / (# of random Yes + # of SR).
   • Check this is a good estimate
3. Report in the README

I noticed that the current file duplicates rows
Happens in code block starting at line 162.

build sr_gen.py file that builds the self-report for analysis/inference phase