Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial-Based Cost-Effectiveness Data

An effectiveness-implementation trial protocol to evaluate PrEP initiation among U.S. cisgender women using eHealth tools vs. standard care

Background

The United States' (U.S.) initiative to End the HIV Epidemic aims to reduce new HIV infections in areas of high HIV prevalence. Despite national efforts to reduce HIV incidence, cisgender women continue to represent approximately one out of every five new HIV diagnoses in the U.S. Taking pre-exposure prophylaxis (PrEP) is an effective HIV prevention strategy; however, PrEP initiation among cisgender women is suboptimal, with only 10% of eligible women receiving PrEP prescriptions in 2019.

Methods

We designed a trial to test the effectiveness of interventions to increase PrEP initiation, while evaluating the implementation strategy (hybrid type II trial) in seven obstetrics and gynecology (OB/GYN) clinics (two federally qualified health centers, three community-based, and two academic) in Baltimore, Maryland. A total of 42 OB/GYN providers will be enrolled and randomized (1:1:1) into one of three clinical trial arms (standard of care, patient-level intervention, or multi-level intervention). Eligible patients of enrolled providers will receive a sexual health questionnaire before their appointment through the electronic health record's (EHR) patient portal. The questionnaire will be scored in three tiers (low, moderate, and high) to assess HIV risk. Patients at low risk will be offered an HIV test only, while those who score medium or high risk will be included in the clinical trial and assigned to the clinical trial arm associated with their provider. Differences in PrEP initiation, our primary outcome, across the three arms will be analyzed using generalized linear mixed-effect models with logistic regression. We will adjust results for demographic differences observed between arms and examine PrEP initiation stratified by patient's and provider's race and ethnicity.Additionally, a comprehensive economic analysis for each intervention will be conducted.

Discussion

We hypothesize that gathering information on sensitive sexual behaviors electronically, communicating HIV risk in an understandable and relatable format to patients and OB/GYN providers, and deploying EHR alerts will increase PrEP initiation and HIV testing.

Trial Registration

The trial is registered with ClinicalTrials.gov (NCT05412433) on 09 June 2022. https://clinicaltrials.gov/ct2/show/NCT05412433?term=NCT05412433&draw=2&rank=1.

A scoping review of statistical methods for trial-based economic evaluations: The current state of play

The statistical quality of trial-based economic evaluations is often suboptimal, while a comprehensive overview of available statistical methods is lacking. Therefore, this review summarized and critically appraised available statistical methods for trial-based economic evaluations. A literature search was performed to identify studies on statistical methods for dealing with baseline imbalances, skewed costs and/or effects, correlated costs and effects, clustered data, longitudinal data, missing data and censoring in trial-based economic evaluations. Data was extracted on the statistical methods described, their advantages, disadvantages, relative performance and recommendations of the study. Sixty-eight studies were included. Of them, 27 (40%) assessed methods for baseline imbalances, 39 (57%) assessed methods for skewed costs and/or effects, 27 (40%) assessed methods for correlated costs and effects, 18 (26%) assessed methods for clustered data, 7 (10%) assessed methods for longitudinal data, 26 (38%) assessed methods for missing data and 10 (15%) assessed methods for censoring. All identified methods were narratively described. This review provides a comprehensive overview of available statistical methods for dealing with the most common statistical complexities in trial-based economic evaluations. Herewith, it can provide valuable input for researchers when deciding which statistical methods to use in a trial-based economic evaluation.