Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P = 0.003), at substantially lower cost per case prevented ($16 627 vs $36 048; adjusted difference = -$18 598 [95% CI, -156 953 to -3120]; P = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. This article is part of a Special Collection on Mental Health.

Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.

BACKGROUND: Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice - essential heterogeneity. METHODS: We simulated eleven populations with identical treatment effect distributions based on patient factors. The populations varied in the extent that treatment effect heterogeneity influenced treatment choice. We used the generalized random forest application (CFA-GRF) to estimate patient-specific treatment effects for each population. Average differences between true and estimated effects for patient subsets were evaluated. RESULTS: CFA-GRF performed well across the population when treatment effect heterogeneity did not influence treatment choice. Under essential heterogeneity, however, CFA-GRF yielded treatment effect estimates that reflected true treatment effects only for treated patients and were on average greater than true treatment effects for untreated patients. CONCLUSIONS: Patient-specific estimates produced by CFAs are sensitive to why patients in real-world practice make different treatment choices. Researchers using CFAs should develop conceptual frameworks of treatment choice prior to estimation to guide estimate interpretation ex post.

Iterative Causal Forest: A Novel Algorithm for Subgroup Identification.

Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors (SGLT2i) or glucagon-like peptide-1 receptor agonists (GLP1RA), we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from SGLT2i, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design.

Performance metrics for models designed to predict treatment effect.

BACKGROUND: Measuring the performance of models that predict individualized treatment effect is challenging because the outcomes of two alternative treatments are inherently unobservable in one patient. The C-for-benefit was proposed to measure discriminative ability. However, measures of calibration and overall performance are still lacking. We aimed to propose metrics of calibration and overall performance for models predicting treatment effect in randomized clinical trials (RCTs). METHODS: Similar to the previously proposed C-for-benefit, we defined observed pairwise treatment effect as the difference between outcomes in pairs of matched patients with different treatment assignment. We match each untreated patient with the nearest treated patient based on the Mahalanobis distance between patient characteristics. Then, we define the Eavg-for-benefit, E50-for-benefit, and E90-for-benefit as the average, median, and 90th quantile of the absolute distance between the predicted pairwise treatment effects and local-regression-smoothed observed pairwise treatment effects. Furthermore, we define the cross-entropy-for-benefit and Brier-for-benefit as the logarithmic and average squared distance between predicted and observed pairwise treatment effects. In a simulation study, the metric values of deliberately "perturbed models" were compared to those of the data-generating model, i.e., "optimal model". To illustrate these performance metrics, different modeling approaches for predicting treatment effect are applied to the data of the Diabetes Prevention Program: 1) a risk modelling approach with restricted cubic splines; 2) an effect modelling approach including penalized treatment interactions; and 3) the causal forest. RESULTS: As desired, performance metric values of "perturbed models" were consistently worse than those of the "optimal model" (Eavg-for-benefit ≥ 0.043 versus 0.002, E50-for-benefit ≥ 0.032 versus 0.001, E90-for-benefit ≥ 0.084 versus 0.004, cross-entropy-for-benefit ≥ 0.765 versus 0.750, Brier-for-benefit ≥ 0.220 versus 0.218). Calibration, discriminative ability, and overall performance of three different models were similar in the case study. The proposed metrics were implemented in a publicly available R-package "HTEPredictionMetrics". CONCLUSION: The proposed metrics are useful to assess the calibration and overall performance of models predicting treatment effect in RCTs.

Targeting the Efficacy of Intensive Blood Pressure Treatment in Hypertensive Patients　- An Exploratory Analysis of SPRINT.

BACKGROUND: Hypertensive patients show highly heterogeneous treatment effects (HTEs) and cardiovascular prognosis, and not all benefit from intensive blood pressure treatment.Methods and Results: We used the causal forest model to identify potential HTEs of patients in the Systolic Blood Pressure Intervention Trial (SPRINT). Cox regression was performed to assess hazard ratios (HRs) for cardiovascular disease (CVD) outcomes and to compare the effects of intensive treatment among groups. The model revealed 3 representative covariates and patients were partitioned into 4 subgroups: Group 1 (baseline body mass index [BMI] ≤28.32 kg/m2and estimated glomerular filtration rate [eGFR] ≤69.53 mL/min/1.73 m2); Group 2 (baseline BMI ≤28.32 kg/m2and eGFR >69.53 mL/min/1.73 m2); Group 3 (baseline BMI >28.32 kg/m2and 10-year CVD risk ≤15.8%); Group 4 (baseline BMI >28.32 kg/m2and 10-year CVD risk >15.8%). Intensive treatment was shown to be beneficial only in Group 2 (HR 0.54, 95% confidence interval [CI] 0.35-0.82; P=0.004) and Group 4 (HR 0.69, 95% CI 0.52-0.91; P=0.009). CONCLUSIONS: Intensive treatment was effective for patients with high BMI and 10-year CVD risk, or low BMI and normal eGFR, but not for those with low BMI and eGFR, or high BMI and low 10-year CVD risk. Our study could facilitate the categorization of hypertensive patients, ensuring individualized therapy.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

OBJECTIVE: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

The impact of COVID-19 on mobility choices in Switzerland.

We study the effect of the COVID-19 pandemic and the associated government measures on individual mobility choices in Switzerland. Our data is based on over 1,600 people for which we observe all trips during eight weeks before the pandemic and until May 2021. We find an overall reduction of travel distances by 60 percent, followed by a gradual recovery during the subsequent re-opening of the economy. Whereas driving distances have almost completely recovered, public transport re-mains under-used. The introduction of a requirement to wear a mask in public transport had no measurable impact on ridership. The individual travel response to the pandemic varies along socio-economic dimensions such as education and house-hold size, with mobility tool ownership, and with personal values and lifestyles. We find no evidence for a significant substitution of leisure travel to compensate for the reduction in work-related travel.

Assessing the ability of an instrumental variable causal forest algorithm to personalize treatment evidence using observational data: the case of early surgery for shoulder fracture.

BACKGROUND: Comparative effectiveness research (CER) using observational databases has been suggested to obtain personalized evidence of treatment effectiveness. Inferential difficulties remain using traditional CER approaches especially related to designating patients to reference classes a priori. A novel Instrumental Variable Causal Forest Algorithm (IV-CFA) has the potential to provide personalized evidence using observational data without designating reference classes a priori, but the consistency of the evidence when varying key algorithm parameters remains unclear. We investigated the consistency of IV-CFA estimates through application to a database of Medicare beneficiaries with proximal humerus fractures (PHFs) that previously revealed heterogeneity in the effects of early surgery using instrumental variable estimators. METHODS: IV-CFA was used to estimate patient-specific early surgery effects on both beneficial and detrimental outcomes using different combinations of algorithm parameters and estimate variation was assessed for a population of 72,751 fee-for-service Medicare beneficiaries with PHFs in 2011. Classification and regression trees (CART) were applied to these estimates to create ex-post reference classes and the consistency of these classes were assessed. Two-stage least squares (2SLS) estimators were applied to representative ex-post reference classes to scrutinize the estimates relative to known 2SLS properties. RESULTS: IV-CFA uncovered substantial early surgery effect heterogeneity across PHF patients, but estimates for individual patients varied with algorithm parameters. CART applied to these estimates revealed ex-post reference classes consistent across algorithm parameters. 2SLS estimates showed that ex-post reference classes containing older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to benefit and more likely to have detriments from higher rates of early surgery. CONCLUSIONS: IV-CFA provides an illuminating method to uncover ex-post reference classes of patients based on treatment effects using observational data with a strong instrumental variable. Interpretation of treatment effect estimates within each ex-post reference class using traditional CER methods remains conditional on the extent of measured information in the data.

Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection.

BACKGROUND: Influenza is associated with excess morbidity and mortality of individuals each year. Few therapies exist for treatment of influenza infection, and each require initiation as early as possible in the course of infection, making efficacy difficult to estimate in the hospitalized patient with lower respiratory tract infection. Using causal machine learning methods, we re-analyze data from a randomized trial of oseltamivir versus standard of care aimed at reducing clinical failure in hospitalized patients with lower respiratory tract infection during the influenza season. METHODS: This was a secondary analysis of the Rapid Empiric Treatment with Oseltamivir Study (RETOS). Conditional average treatment effects (CATE) and 95% confidence intervals were computed from causal forest including 85 clinical and demographic variables. RETOS was a multicenter, randomized, unblinded, trial of adult patients hospitalized with lower respiratory tract infections in Kentucky from 2009 through 2012. Adult hospitalized patients with lower respiratory tract infection were randomized to standard of care or standard of care plus oseltamivir as early as possible after hospital admission but within 24 h of enrollment. After randomization, oseltamivir was initiated in the treatment arm per package insert. The primary outcome was clinical failure, a composite measure including failure to reach clinical improvement within 7 days, transfer to intensive care 24 h after admission, or rehospitalization or death within 30 days. RESULTS: A total of 691 hospitalized patients with lower respiratory tract infections were included in the study. The only subgroup of patients with a statistically significant CATE was those with laboratory-confirmed influenza infection with a 26% lower risk of clinical failure when treated with oseltamivir (95% CI 3.2-48.0%). CONCLUSIONS: This study suggests that addition of oseltamivir to standard of care may decrease clinical failure in hospitalized patients with influenza-associated lower respiratory tract infection versus standard of care alone. These results are supportive of current recommendations to initiate antiviral treatment in hospitalized patients with confirmed or suspected influenza as soon as possible after admission. Trial registration Original trial: Clinical Trials.Gov; Rapid Empiric Treatment With Oseltamivir Study (RETOS) (RETOS); ClinicalTrials.gov Identifier: NCT01248715 https://clinicaltrials.gov/ct2/show/NCT01248715.

Using causal forests to assess heterogeneity in cost-effectiveness analysis.

We develop a method for data-driven estimation and analysis of heterogeneity in cost-effectiveness analyses (CEA) with experimental or observational individual-level data. Our implementation uses causal forests and cross-fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual-level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost-effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data.

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19.

Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.

The impact of COVID-19 on industry-related characteristics and risk contagion.

We use the cutting-edge causal forest algorithm to analyze the heterogeneous treatment effects of the COVID-19 outbreak on China's industry indexes. The variable importance index is used with the causal forest and complex network methods to analyze the characteristics of industrial relations and the types of industry risk contagion before and after the COVID-19 outbreak. The results show that the heterogeneity of industries was significantly weakened during the COVID-19 outbreak. In addition, the COVID-19 outbreak changed the original structure of the industry-related network, which shifted to a star network structure with leisure services at the core. It also changed the type of risk contagion between industries, from the original middleman risk type to the input risk type.

A prospective exploration of the urban exposome in relation to headache in the Dutch population-based Occupational and environmental health cohort study (AMIGO).

OBJECTIVE: Headache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO). MATERIAL AND METHODS: Participants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline. RESULTS: Occurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004). CONCLUSION: Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

Persistence and heterogeneity of the effects of educating mothers to improve child immunisation uptake: Experimental evidence from Uttar Pradesh in India.

Childhood vaccinations are among the most cost-effective health interventions. Yet, in India, where immunisation services are widely available free of charge, a substantial proportion of children remain unvaccinated. We revisit households 30 months after a randomised experiment of a health information intervention designed to educate mothers on the benefits of child vaccination in Uttar Pradesh, India. We find that the large short-term effects on the uptake of diphtheria-pertussis-tetanus and measles vaccination were sustained at 30 months, suggesting the intervention did not simply bring forward vaccinations. We apply causal forests and find that the intervention increased vaccination uptake, but that there was substantial variation in the magnitude of the estimated effects. We conclude that characterising those who benefited most and conversely those who benefited least provides policy-makers with insights on how the intervention worked, and how the targeting of households could be improved.

Identifying the Heterogeneity in the Association between Workforce Diversity and Retention in Opioid Treatment among Black clients.

Background: This study investigates the impact of workforce diversity, specifically staff identified as Black/African American, on retention in opioid use disorder (OUD) treatment, aiming to enhance patient outcomes. Employing a novel machine learning technique known as 'causal forest,' we explore heterogeneous treatment effects on retention. Methods: We relied on four waves of the National Drug Abuse Treatment System Survey (NDATSS), a nationally representative longitudinal dataset of treatment programs. We analyzed OUD program data from the years 2000, 2005, 2014 and 2017 (n = 627). Employing the 'causal forest' method, we analyzed the heterogeneity in the relationship between workforce diversity and retention in OUD treatment. Interviews with program directors and clinical supervisors provided the data for this study. Results: The results reveal diversity-related variations in the association with retention across 61 out of 627 OUD treatment programs (less than 10%). These programs, associated with positive impacts of workforce diversity, were more likely private-for-profit, newer, had lower percentages of Black and Latino clients, lower staff-to-client ratios, higher proportions of staff with graduate degrees, and lower percentages of unemployed clients. Conclusions: While workforce diversity is crucial, our findings underscore that it alone is insufficient for improving retention in addiction health services research. Programs with characteristics typically linked to positive outcomes are better positioned to maximize the benefits of a diverse workforce in client retention. This research has implications for policy and program design, guiding decisions on resource allocation and workforce diversity to enhance retention rates among Black clients with OUDs.

Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk.

Background: Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods: The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results: At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion: PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Estimating the Heterogeneous Causal Effects of Parent-Child Relationships among Chinese Children with Oppositional Defiant Symptoms: A Machine Learning Approach.

Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder. Research has addressed the close association of the parent-child relationship (PCR) with oppositional defiant symptoms. However, it is necessary to further investigate the underlying mechanism for forming targeted intervention strategies. By using a machine learning-based causal forest (CF) model, we investigated the heterogeneous causal effects of the PCR on oppositional defiant symptoms in children in Chinese elementary schools. Based on the PCR improvement in two consecutive years, 423 children were divided into improved and control groups. The assessment of oppositional defiant symptoms (AODS) in the second year was set as the dependent variable. Additionally, several factors based on the multilevel family model and the baseline AODS in the first year were included as covariates. Consistent with expectations, the CF model showed a significant causal effect between the PCR and oppositional defiant symptoms in the samples. Moreover, the causality exhibited heterogeneity. The causal effect was greater in those children with higher baseline AODS, a worse family atmosphere, and lower emotion regulation abilities in themselves or their parents. Conversely, the parenting style played a positive role in causality. These findings enhance our understanding of how the PCR contributes to the development of oppositional defiant symptoms conditioned by factors from a multilevel family system. The heterogeneous causality in the observation data, established using the machine learning approach, could be helpful in forming personalized family-oriented intervention strategies for children with oppositional defiant symptoms.

Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management.

BACKGROUND: In medicine, clinicians treat individuals under an implicit assumption that high-risk patients would benefit most from the treatment ('high-risk approach'). However, treating individuals with the highest estimated benefit using a novel machine-learning method ('high-benefit approach') may improve population health outcomes. METHODS: This study included 10 672 participants who were randomized to systolic blood pressure (SBP) target of either <120 mmHg (intensive treatment) or <140 mmHg (standard treatment) from two randomized controlled trials (Systolic Blood Pressure Intervention Trial, and Action to Control Cardiovascular Risk in Diabetes Blood Pressure). We applied the machine-learning causal forest to develop a prediction model of individualized treatment effect (ITE) of intensive SBP control on the reduction in cardiovascular outcomes at 3 years. We then compared the performance of high-benefit approach (treating individuals with ITE >0) versus the high-risk approach (treating individuals with SBP ≥130 mmHg). Using transportability formula, we also estimated the effect of these approaches among 14 575 US adults from National Health and Nutrition Examination Surveys (NHANES) 1999-2018. RESULTS: We found that 78.9% of individuals with SBP ≥130 mmHg benefited from the intensive SBP control. The high-benefit approach outperformed the high-risk approach [average treatment effect (95% CI), +9.36 (8.33-10.44) vs +1.65 (0.36-2.84) percentage point; difference between these two approaches, +7.71 (6.79-8.67) percentage points, P-value <0.001]. The results were consistent when we transported the results to the NHANES data. CONCLUSIONS: The machine-learning-based high-benefit approach outperformed the high-risk approach with a larger treatment effect. These findings indicate that the high-benefit approach has the potential to maximize the effectiveness of treatment rather than the conventional high-risk approach, which needs to be validated in future research.