Synthetic data ยท Methods demonstration
Remote monitoring after heart failure discharge
Target trial emulation example using a fictional linked hospital and claims dataset. All counts and estimates are illustrative.
Study question
Among eligible adults discharged home after a heart failure admission, what is the difference in 30-day risk of unplanned readmission between patients offered remote monitoring at discharge and those discharged with usual care?
- Eligibility
- Adults discharged alive to home after their first qualifying heart failure admission, with 180 days of observable baseline history.
- Time zero
- Discharge date for both groups. The offer must be documented by discharge. Later enrollment does not define exposure.
- Strategies
- Offer remote monitoring at discharge versus usual discharge care. Analyze by the initial offer regardless of later uptake.
- Outcome
- First unplanned all-cause inpatient readmission by day 30. Death before readmission is a competing event.
- Estimand
- Difference in 30-day cumulative incidence, in percentage points, in the population where either strategy was plausible. The crude contrast is shown for context.
Compare estimates
The interval includes zero. These illustrative data are compatible with a modest reduction or little difference in readmissions.
Cohort and balance
Illustrative eligible cohort: 1,200 offered monitoring and 3,600 with usual care. The table shows selected baseline measures for the primary analysis.
| Baseline measure | Before weighting | After weighting |
|---|---|---|
| Age | 0.24 | 0.02 |
| Prior admissions | 0.31 | 0.03 |
| Kidney disease | 0.18 | 0.02 |
| Length of stay | 0.21 | 0.04 |
Methods and limitations
The primary design uses propensity-score overlap weights with age, prior utilization, comorbidity, length of stay, hospital and discharge month. A production analysis would estimate weighted cumulative incidence and bootstrap intervals by hospital, refitting the propensity model in each replicate. The sensitivity view illustrates restricting to hospitals that used both strategies.
Residual confounding from functional status, social support and clinician selection could explain the association. A live analysis would report missingness, overlap, effective sample sizes, event counts and protocol deviations alongside the estimates.
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