Assessing spillover effects: Handling missing outcomes in network-based studies
Bibliographic record
Abstract
Estimating causal effects in the presence of spillover among individuals within a social network poses challenges due to missing information. Spillover effects refer to the impact of an intervention on individuals not directly exposed themselves but connected to intervention recipients within the network. In network-based studies, outcomes may be missing due to study termination or participant dropout, termed censoring. We introduce an inverse probability censoring weighted estimator which extends the inverse probability weighted estimator for network-based observational studies to handle possible outcome censoring. We prove the consistency and asymptotic normality of the proposed estimator and derive a closed-form estimator for its asymptotic variance. Applying the inverse probability censoring weighted estimator, we assess spillover effects in a network-based study of a nonrandomized intervention with outcome censoring. A simulation study evaluates the finite-sample performance of the inverse probability censoring weighted estimator, demonstrating its effectiveness with sufficiently large sample sizes and number of connected subnetworks. We then employ the method to assess spillover effects of community alerts on self-reported human immunodeficiency virus risk behavior among people who inject drugs and their contacts in the Transmission Reduction Intervention Project (TRIP), from 2013 to 2015, Athens, Greece. Results suggest that community alerts may help reduce human immunodeficiency virus risk behavior for both the individuals who receive them and others in their network, possibly through shared information. In this study, we found that the risk of human immunodeficiency virus behavior was reduced by increasing the proportion of a participant's immediate contacts exposed to community alerts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.417 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".