Design of egocentric network-based studies to estimate causal effects under interference
Bibliographic record
Abstract
Many public health interventions are conducted in settings where individuals are connected and the intervention assigned to some individuals may spill over to other individuals. In these settings, we can assess: (a) the individual effect on the treated, (b) the spillover effect on untreated individuals through an indirect exposure to the intervention, and (c) the overall effect on the whole population. Here, we consider an egocentric network-based randomized design in which a set of index participants is recruited and randomly assigned to treatment, while data are also collected on their untreated network members. Such a design is common in peer education interventions conceived to leverage behavioral influence among peers. Using the potential outcomes framework, we first clarify the assumptions required to rely on an identification strategy that is commonly used in the well-studied two-stage randomized design. Under these assumptions, causal effects can be jointly estimated using a regression model with a block-diagonal structure. We then develop sample size formulas for detecting individual, spillover, and overall effects for single and joint hypothesis tests, and investigate the role of different parameters. Finally, we illustrate the use of our sample size formulas for an egocentric network-based randomized experiment to evaluate a peer education intervention for HIV prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".