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Functional additive models for optimizing individualized treatment rules

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.
PMCID:9043034
PMID: 34704622
ISSN: 1541-0420
CID: 5231012

A sparse additive model for treatment effect-modifier selection

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.
PMID: 32808656
ISSN: 1468-4357
CID: 4566752

Logistic regression error-in-covariate models for longitudinal high-dimensional covariates

Park, Hyung; Lee, Seonjoo
We consider a logistic regression model for a binary response where part of its covariates are subject-specific random intercepts and slopes from a large number of longitudinal covariates. These random effect covariates must be estimated from the observed data, and therefore, the model essentially involves errors in covariates. Because of high dimension and high correlation of the random effects, we employ longitudinal principal component analysis to reduce the total number of random effects to some manageable number of random effects. To deal with errors in covariates, we extend the conditional-score equation approach to this moderate dimensional logistic regression model with random effect covariates. To reliably solve the conditional-score equations in moderate/high dimension, we apply a majorization on the first derivative of the conditional-score functions and a penalized estimation by the smoothly clipped absolute deviation. The method was evaluated through a set of simulation studies and applied to a data set with longitudinal cortical thickness of 68 regions of interest to identify biomarkers that are related to dementia transition.
PMCID:7654973
PMID: 33177749
ISSN: 0038-9986
CID: 5231002

Optimizing the use of ketamine to reduce chronic postsurgical pain in women undergoing mastectomy for oncologic indication: study protocol for the KALPAS multicenter randomized controlled trial

Wang, Jing; Doan, Lisa V; Axelrod, Deborah; Rotrosen, John; Wang, Binhuan; Park, Hyung G; Edwards, Robert R; Curatolo, Michele; Jackman, Carina; Perez, Raven; ,
BACKGROUND:Mastectomies are commonly performed and strongly associated with chronic postsurgical pain (CPSP), more specifically termed postmastectomy pain syndrome (PMPS), with 25-60% of patients reporting pain 3 months after surgery. PMPS interferes with function, recovery, and compliance with adjuvant therapy. Importantly, it is associated with chronic opioid use, as a recent study showed that 1 in 10 patients continue to use opioids at least 3 months after curative surgery. The majority of PMPS patients are women, and, over the past 10 years, women have outpaced men in the rate of growth in opioid dependence. Standard perioperative multimodal analgesia is only modestly effective in prevention of CPSP. Thus, interventions to reduce CPSP and PMPS are urgently needed. Ketamine is well known to improve pain and reduce opioid use in the acute postoperative period. Additionally, ketamine has been shown to control mood in studies of anxiety and depression. By targeting acute pain and improving mood in the perioperative period, ketamine may be able to prevent the development of CPSP. METHODS:Ketamine analgesia for long-lasting pain relief after surgery (KALPAS) is a phase 3, multicenter, randomized, placebo-controlled, double-blind trial to study the effectiveness of ketamine in reducing PMPS. The study compares continuous perioperative ketamine infusion vs single-dose ketamine in the postanesthesia care unit vs placebo for reducing PMPS. Participants are followed for 1 year after surgery. The primary outcome is pain at the surgical site at 3 months after the index surgery as assessed with the Brief Pain Inventory-short form pain severity subscale. DISCUSSION/CONCLUSIONS:This project is part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, a nationwide effort to address the opioid public health crisis. This study can substantially impact perioperative pain management and can contribute significantly to combatting the opioid epidemic. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT05037123. Registered on September 8, 2021.
PMCID:10797799
PMID: 38243266
ISSN: 1745-6215
CID: 5624462

Single-Dose of Postoperative Ketamine for Postoperative Pain After Mastectomy: A Pilot Randomized Controlled Trial

Doan, Lisa V.; Li, Anna; Brake, Lee; Ok, Deborah; Jee, Hyun Jung; Park, Hyung; Cuevas, Randy; Calvino, Steven; Guth, Amber; Schnabel, Freya; Hiotis, Karen; Axelrod, Deborah; Wang, Jing
Background and Objectives: Perioperative ketamine has been shown to reduce opioid consumption and pain after surgery. Ketamine is most often given as an infusion, but an alternative is single-dose ketamine. Single-dose ketamine at up to 1 mg/kg has been shown to reduce symptoms of depression, and a wide range of dosages has been used for pain in the emergency department. However, limited data exists on the tolerability and efficacy of a single-dose of ketamine at 0.6 mg/kg for pain when administered immediately after surgery. We conducted a pilot study of single-dose ketamine in patients undergoing mastectomy with reconstruction, hypothesizing that a single-dose of ketamine is well tolerated and can relieve postoperative pain and improve mood and recovery. Methods: This is a randomized, single-blind, placebo-controlled, two-arm parallel, single-center study. Thirty adult women undergoing mastectomy with reconstruction for oncologic indication received a single-dose of ketamine (0.6mg/kg) or placebo after surgery in the post-anesthesia care unit (PACU). Patients were followed through postoperative day (POD) 7. The primary outcome was postoperative pain measured by the Brief Pain Inventory (BPI) pain subscale on POD 1 and 2. Secondary outcomes include effects on opioid use, PROMIS fatigue and sleep, mood, Quality of Recovery-15, and the Breast Cancer Pain Questionnaire. Results: Side effects were minor and not significantly different in frequency between groups. The ketamine group reported lower scores on the BPI pain severity subscale, especially at POD 7; however, the difference was not statistically significant. There were no statistically significant differences between ketamine and placebo groups for the secondary outcomes. Conclusion: A single-dose of ketamine at 0.6mg/kg administered postoperatively in the PACU is well tolerated in women undergoing mastectomy and may confer better pain control up to one week after surgery. Future studies with larger sample sizes are necessary to adequately characterize the effect of postoperative single-dose ketamine on pain control in this population.
SCOPUS:85150750594
ISSN: 1178-7090
CID: 5447712

Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome

Park, Hyung G.; Wu, Danni; Petkova, Eva; Tarpey, Thaddeus; Ogden, R. Todd
This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.
SCOPUS:85159656547
ISSN: 1867-1764
CID: 5501852

Advancing scalability and impacts of a teacher training program for promoting child mental health in Ugandan primary schools: protocol for a hybrid-type II effectiveness-implementation cluster randomized trial

Huang, Keng-Yen; Nakigudde, Janet; Kisakye, Elizabeth Nsamba; Sentongo, Hafsa; Dennis-Tiwary, Tracy A; Tozan, Yesim; Park, Hyung; Brotman, Laurie Miller
BACKGROUND:Children in low-and-middle-income countries (LMICs) are facing tremendous mental health challenges. Numerous evidence-based interventions (EBIs) have been adapted to LMICs and shown effectiveness in addressing the needs, but most EBIs have not been adopted widely using scalable and sustainable implementation models that leverage and strengthen existing structures. There is a need to apply implementation science methodology to study strategies to effectively scale-up EBIs and sustain the practices in LMICs. Through a cross-sector collaboration, we are carrying out a second-generation investigation of implementation and effectiveness of a school-based mental health EBI, ParentCorps Professional Development (PD), to scale-up and sustain the EBI in Uganda to promote early childhood students' mental health. Our previous studies in Uganda supported that culturally adapted PD resulted in short-term benefits for classrooms, children, and families. However, our previous implementation of PD was relied on mental health professionals (MHPs) to provide PD to teachers. Because of the shortage of MHPs in Uganda, a new scalable implementation model is needed to provide PD at scale. OBJECTIVES/OBJECTIVE:This study tests a new scalable and sustainable PD implementation model and simultaneously studies the effectiveness. This paper describes use of collaboration, task-shifting, and Train-the-Trainer strategies for scaling-up PD, and protocol for studying the effectiveness-implementation of ParentCorps-PD for teachers in urban and rural Ugandan schools. We will examine whether the new scale-up implementation approach will yield anticipated impacts and investigate the underlying effectiveness-implementation mechanisms that contribute to success. In addition, considering the effects of PD on teachers and students will influence by teacher wellness. This study also examines the added value (i.e. impact and costs) of a brief wellness intervention for teachers and students. METHODS:Using a hybrid-type II effectiveness-implementation cluster randomized controlled trial (cRCT), we will randomize 36 schools (18 urban and 18 rural) with 540 teachers and nearly 2000 families to one of three conditions: PD + Teacher-Wellness (PDT), PD alone (PD), and Control. Primary effectiveness outcomes are teachers' use of mental health promoting strategies, teacher stress management, and child mental health. The implementation fidelity/quality for the scale-up model will be monitored. Mixed methods will be employed to examine underlying mechanisms of implementation and impact as well as cost-effectiveness. DISCUSSION/CONCLUSIONS:This research will generate important knowledge regarding the value of an EBI in urban and rural communities in a LMIC, and efforts toward supporting teachers to prevent and manage early signs of children's mental health issues as a potentially cost-effective strategy to promote child population mental health in low resource settings. TRIAL REGISTRATION/BACKGROUND:This trial was registered with ClinicalTrials.gov (registration number: NCT04383327; https://clinicaltrials.gov/ct2/show/NCT04383327 ) on May13, 2020.
PMCID:9206883
PMID: 35718782
ISSN: 1752-4458
CID: 5281762

Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma

Park, Hyung; Tarpey, Thaddeus; Liu, Mengling; Goldfeld, Keith; Wu, Yinxiang; Wu, Danni; Li, Yi; Zhang, Jinchun; Ganguly, Dipyaman; Ray, Yogiraj; Paul, Shekhar Ranjan; Bhattacharya, Prasun; Belov, Artur; Huang, Yin; Villa, Carlos; Forshee, Richard; Verdun, Nicole C; Yoon, Hyun Ah; Agarwal, Anup; Simonovich, Ventura Alejandro; Scibona, Paula; Burgos Pratx, Leandro; Belloso, Waldo; Avendaño-Solá, Cristina; Bar, Katharine J; Duarte, Rafael F; Hsue, Priscilla Y; Luetkemeyer, Anne F; Meyfroidt, Geert; Nicola, André M; Mukherjee, Aparna; Ortigoza, Mila B; Pirofski, Liise-Anne; Rijnders, Bart J A; Troxel, Andrea; Antman, Elliott M; Petkova, Eva
Importance:Identifying which patients with COVID-19 are likely to benefit from COVID-19 convalescent plasma (CCP) treatment may have a large public health impact. Objective:To develop an index for predicting the expected relative treatment benefit from CCP compared with treatment without CCP for patients hospitalized for COVID-19 using patients' baseline characteristics. Design, Setting, and Participants:This prognostic study used data from the COMPILE study, ie, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) evaluating CCP vs control in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. A combination of baseline characteristics, termed the treatment benefit index (TBI), was developed based on 2287 patients in COMPILE using a proportional odds model, with baseline characteristics selected via cross-validation. The TBI was externally validated on 4 external data sets: the Expanded Access Program (1896 participants), a study conducted under Emergency Use Authorization (210 participants), and 2 RCTs (with 80 and 309 participants). Exposure:Receipt of CCP. Main Outcomes and Measures:World Health Organization (WHO) 11-point ordinal COVID-19 clinical status scale and 2 derivatives of it (ie, WHO score of 7-10, indicating mechanical ventilation to death, and WHO score of 10, indicating death) at day 14 and day 28 after randomization. Day 14 WHO 11-point ordinal scale was used as the primary outcome to develop the TBI. Results:A total of 2287 patients were included in the derivation cohort, with a mean (SD) age of 60.3 (15.2) years and 815 (35.6%) women. The TBI provided a continuous gradation of benefit, and, for clinical utility, it was operationalized into groups of expected large clinical benefit (B1; 629 participants in the derivation cohort [27.5%]), moderate benefit (B2; 953 [41.7%]), and potential harm or no benefit (B3; 705 [30.8%]). Patients with preexisting conditions (diabetes, cardiovascular and pulmonary diseases), with blood type A or AB, and at an early COVID-19 stage (low baseline WHO scores) were expected to benefit most, while those without preexisting conditions and at more advanced stages of COVID-19 could potentially be harmed. In the derivation cohort, odds ratios for worse outcome, where smaller odds ratios indicate larger benefit from CCP, were 0.69 (95% credible interval [CrI], 0.48-1.06) for B1, 0.82 (95% CrI, 0.61-1.11) for B2, and 1.58 (95% CrI, 1.14-2.17) for B3. Testing on 4 external datasets supported the validation of the derived TBIs. Conclusions and Relevance:The findings of this study suggest that the CCP TBI is a simple tool that can quantify the relative benefit from CCP treatment for an individual patient hospitalized with COVID-19 that can be used to guide treatment recommendations. The TBI precision medicine approach could be especially helpful in a pandemic.
PMCID:8790670
PMID: 35076698
ISSN: 2574-3805
CID: 5153212

A single-index model with a surface-link for optimizing individualized dose rules

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression. The primary motivation is to estimate an optimal individualized dose rule and individualized treatment effects. To model possibly nonlinear interaction effects between patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear projection of the covariates. The method is illustrated using two applications as well as simulation experiments. A unique contribution of this work is in the parsimonious (single-index) parametrization specifically defined for the interaction effect term.
PMCID:9306450
PMID: 35873662
ISSN: 1061-8600
CID: 5387832

A constrained single-index regression for estimating interactions between a treatment and covariates

Park, Hyung; Petkova, Eva; Tarpey, Thaddeus; Ogden, R Todd
We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals zero, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single-index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinical trial. This article is protected by copyright. All rights reserved.
PMID: 32573759
ISSN: 1541-0420
CID: 4493012