Back

Financial incentives to motivate treatment for hepatitis C with direct acting antivirals among Australian adults (The Methodical evaluation and Optimisation of Targeted IncentiVes for Accessing Treatment of Early-stage hepatitis C: MOTIVATE-C): Statistical Analysis Plan

Jones, M.; Totterdell, J.; Fathima, P.; Snelling, T.

2024-12-01 infectious diseases
10.1101/2024.11.27.24318114 medRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWThe MOTIVATE-C study explores a critical question in hepatitis C treatment: How do financial incentives influence patients decisions to initiate direct-acting antiviral (DAA) therapy? Using an innovative Bayesian adaptive design, the research aims to determine the precise relationship between monetary support and treatment initiation among individuals with untreated hepatitis C virus. The studys unique approach involves response-adaptive randomization, which dynamically allocates participants to different financial incentive levels. As the trial progresses, doses more likely to encourage treatment will receive increased emphasis, while less effective incentive levels may be systematically eliminated through pre-defined futility stopping rules. Participants will be tracked for DAA therapy initiation within 12 weeks of enrollment, with dedicated study navigators assisting them through the treatment access process. The primary analysis will adhere to the intention-to-treat principle, ensuring a comprehensive and unbiased evaluation of the interventions effectiveness. This manuscript details the statistical analysis plan, presenting the precise methodological framework, decision-making criteria, and analytical thresholds that will guide the studys interpretation of how financial incentives might overcome barriers to hepatitis C treatment.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.