Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints.

Impact of Baseline SARS-CoV-2 Load in Plasma and Upper Airways on the Incidence of Acute Extrapulmonary Complications of COVID-19: A Multicentric, Prospective, Cohort Study.

Background: Extrapulmonary complications (EPCs) are common in patients hospitalized for coronavirus disease 2019 (COVID-19), but data on their clinical consequences and association with viral replication and systemic viral dissemination are lacking.

Methods: Patients hospitalized for COVID-19 and enrolled in the Therapeutics for Inpatients with COVID-19 (TICO) platform trial at 114 international sites between August 2020 and November 2021 were included in a prospective cohort study. We categorized EPCs into 39 event types within 9 categories and estimated their frequency through day 28 and their association with clinical outcomes through day 90.

Commentary on Chen et al. (2022): The need for continued methodological research on leveraging information in secondary endpoints for more efficient RCTs.

Chen et al. (2022) recently proposed a set of estimating equations that incorporate data from secondary endpoints to improve precision in parameter estimates related to a primary endpoint. We were motivated to translate their methodology to the context of randomized controlled trials to gain precision in treatment effect estimation using data from secondary endpoints.

Consequences of low estimated glomerular filtration rate either before or early after kidney donation.

In the general population, decreases in glomerular filtration rate (GFR) are associated with subsequent development of chronic kidney disease (CKD), cardiovascular disease (CVD), and death. It is unknown if low estimated GFR (eGFR) before or early after kidney donation was also associated with these risks. One thousand six hundred ninety-nine living donors who had both predonation and early (4-10 weeks) postdonation eGFR were included.

The landscape of liver transplantation for patients with alcohol-associated liver disease in the United States.

Indications for liver transplants have expanded to include patients with alcohol-associated liver disease (ALD) over the last decade. Concurrently, the liver allocation policy was updated in February 2020 replacing the Donor Service Area with Acuity Circles (ACs). The aim is to compare the transplantation rate, waitlist outcomes, and posttransplant survival of candidates with ALD to non-ALD and assess differences in that effect after the implementation of the AC policy.

Appetitive and psychological phenotypes of pediatric patients with obesity.

Background: Obesity is a heterogeneous disease with variable treatment response. Identification of the unique constellation of contributors to obesity may allow for targeted interventions and improved outcomes.

Objective: Identify empirically derived phenotypes of pediatric patients with obesity based on appetitive and psychological correlates of obesity.

School Connectedness and Adolescent E-cigarette Susceptibility in an Urban Sample of Middle and High School Students.

Adolescent school connectedness generally protects from risk behaviors such as tobacco use; however, its relationship to e-cigarette use is unclear. This study examines the relationship between adolescent school connectedness and e-cigarette susceptibility in a diverse longitudinal sample. This secondary analysis of a school-based intervention surveyed 608 middle (66%) and high school (34%) students from 10 schools at 3 time points over 1 year.

SMART use of medications for the treatment of adolescent severe obesity: A sequential multiple assignment randomized trial protocol.

Background: Severe obesity is a complex, chronic disease affecting nearly 9% of adolescents in the U.S. Although the current mainstay of treatment is lifestyle therapy, pediatric clinical practice guidelines recommend the addition of adjunct anti-obesity medication (AOM), such as phentermine and topiramate.

Should children receive a kidney transplant before 2 years of age?

Background: The optimal age of kidney transplantation for infants and toddlers with kidney failure is unclear. We aimed to evaluate the patient survival associated with kidney transplantation before 2 years of age versus remaining on the waitlist until ≥2 years.

Method: We used the Scientific Registry of Transplant Recipients to identify all children added to the deceased-donor waitlist before 2 years of age between 1/1/2000 and 4/30/2020.

Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity.

A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear regression is widely used for this purpose due to its ease of implementation.

A Microsimulation of Well-Being and Literacy Interventions to Reduce Scam Susceptibility in Older Adults.

Poor financial and health literacy and poor psychological well-being are significant correlates of scam susceptibility in older adults; yet, no research has examined whether interventions that target these factors may effectively reduce susceptibility. Using longitudinal data from older adults in the Rush Memory and Aging Project (MAP) ( = 1,231), we used microsimulations to estimate the causal effect of hypothetical well-being and literacy interventions on scam susceptibility over six years. Microsimulations can simulate a randomized trial to estimate intervention effects using observational data.

Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures.

In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time.

Examining engagement effects in an adaptive preventive intervention for college student drinking.

Objective: This study determined the characteristics of engagement and whether engagement in an adaptive preventive intervention (API) was associated with reduced binge drinking and alcohol-related consequences.

Method: Incoming students were recruited for a sequential multiple assignment randomized trial (SMART; = 891, 62.4% female, 76.

Optimizing Longitudinal Tobacco Cessation Treatment in Lung Cancer Screening: A Sequential, Multiple Assignment, Randomized Trial.

Importance: Nearly half of the 14.8 million US adults eligible for lung cancer screening (LCS) smoke cigarettes. The optimal smoking cessation program components for the LCS setting are unclear.

Transportability of causal inference under random dynamic treatment regimes for kidney-pancreas transplantation.

A difficult decision for patients in need of kidney-pancreas transplant is whether to seek a living kidney donor or wait to receive both organs from one deceased donor. The framework of dynamic treatment regimes (DTRs) can inform this choice, but a patient-relevant strategy such as "wait for deceased-donor transplant" is ill-defined because there are multiple versions of treatment (i.e.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.

Objective: Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC).

Materials & Methods: 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified.

Diagnosed Gonorrhea Among Privately Insured Women: Analysis of United States Claims Data.

Gonorrhea incidence in the United States has risen by nearly 50% in the last decade, while screening rates have increased. Gonorrhea sequelae rates could indicate whether increased gonorrhea incidence is due to better screening. We estimated the association of gonorrhea diagnosis with pelvic inflammatory disease (PID), ectopic pregnancy (EP), and tubal factor infertility (TFI) in women and detected changes in associations over time.

The Minnesota attributable risk of kidney donation (MARKD) study: a retrospective cohort study of long-term (> 50 year) outcomes after kidney donation compared to well-matched healthy controls.

Background: There is uncertainty about the long-term risks of living kidney donation. Well-designed studies with controls well-matched on risk factors for kidney disease are needed to understand the attributable risks of kidney donation.

Methods: The goal of the Minnesota Attributable Risk of Kidney Donation (MARKD) study is to compare the long-term (> 50 years) outcomes of living donors (LDs) to contemporary and geographically similar controls that are well-matched on health status.

Practical guidance on modeling choices for the virtual twins method.

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a highly cited and implemented method for subgroup identification because of its intuitive framework.

Long-term Outcomes Associated With Post-kidney Donation Pregnancy Complications.

Background: Kidney donors have increased risk of postdonation gestational hypertension (gHTN) and preeclampsia. In the general population, pregnancy complications are associated with long-term maternal risk. However, little data exist on whether donors with postdonation pregnancy-related complications have similar increased long-term risks.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials.

Background: A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.

Design considerations and analytical framework for reliably identifying a beneficial individualized treatment rule.

An individualized treatment rule (ITR) formalizes personalized medicine by assigning treatment as a function of patients' clinical information, which contrasts with a static treatment rule that assigns everyone the same treatment. ITR identification has become a common aim in randomized clinical trials but sample size considerations for this aim are lacking. One approach is to select a sample size that will reliably identify an ITR with a performance close to the theoretical optimal rule.

PRescribing Interventions for Chronic pain using the Electronic health record (PRINCE): Study protocol.

Background: Primary care is a frequent source of pain treatment and opioid prescribing. The objective of the Prescribing Interventions for Chronic Pain using the Electronic health record (PRINCE) study is to assess the effects of two behavioral economics-informed interventions embedded within the electronic health record (EHR) on guideline-concordant pain treatment and opioid prescribing decisions in primary care settings.

Methods: Setting: The setting for this study is 43 primary care clinics in Minnesota.