Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups. HIGHLIGHTS: New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Collaborative effect of the energy conservation and emission reduction fiscal policy in China.

Government-led national comprehensive demonstration cities for Energy Conservation and Emission Reduction Fiscal Policy (ECERFP) are pivotal for China in addressing environmental governance. Using a panel dataset covering 278 Chinese cities from 2003 to 2019, this study adopts the staggered difference-in-differences (DID) approach to investigate the synergistic impacts of ECERFP on pollution and carbon reduction. The findings indicate that ECERFP contributes to a 3% improvement in pollution reduction performance, a 1.5% enhancement in carbon reduction performance, and a 4% overall increase in combined pollution and carbon reduction efforts. Furthermore, the study examines the heterogeneous effects of ECERFP on environmental performance. ECERFP significantly influences the synergistic efforts in pollution and carbon reduction by fostering green innovation, enhancing energy allocation, and optimizing industrial structures. This study both theoretically and empirically outlines the specific pathways and mechanisms through which "incentive-based" green fiscal policy promotes synergistic pollution and carbon reduction, thus providing a pragmatic foundation for enhancing the role of fiscal policy in environmental governance.

The cost of influence: How gifts to physicians shape prescriptions and drug costs.

This paper investigates the influence of gifts - monetary and in-kind payments - from drug firms to US physicians on prescription behavior and drug costs. Using causal models and machine learning, we estimate physicians' heterogeneous responses to payments on antidiabetic prescriptions. We find that payments lead to increased prescription of brand drugs, resulting in a cost rise of $23 per dollar value of transfer received. Paid physicians show higher responses when they treat higher proportions of patients receiving a government-funded low-income subsidy that lowers out-of-pocket drug costs. We estimate that introducing a national gift ban would reduce diabetes drug costs by 2%.

Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.

[Cerebrolysin as an early add-on to reperfusion therapy: heterogeneous treatment effect analysis in ischemic stroke patients with varying risk of hemorrhagic transformation]. / Effektivnost' rannego primeneniya Tserebrolizina pri reperfuzionnoi terapii: analiz geterogennykh effektov vozdeistviya u bol'nykh s ishemicheskim insul'tom pri razlichnom riske gemorragicheskoi transformatsii.

OBJECTIVE: The study goal was the assessment of heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy in stroke patients with varying risk of hemorrhagic transformation (HT). MATERIAL AND METHODS: It was post hoc analysis of the CEREHETIS trial (ISRCTN87656744). Patients with middle cerebral artery infarction (n=238) were stratified by HT risk with the HTI score. The study outcomes were symptomatic and any HT, and functional outcome measured with the modified Rankin Scale (mRS) on day 90. Favorable outcome was defined as an mRS score of ≤2. Heterogeneous treatment effect analysis was performed using techniques of meta-analysis and the matching-smoothing method. RESULTS: Heterogeneity of Cerebrolysin treatment effects was moderate (I2=36.98-69.3%, H2=1.59-3.26) and mild (I2=18.33-32.39%, H2=1.22-1.48) for symptomatic and any HT, respectively. A positive impact of the Cerebrolysin treatment on HT and functional outcome was observed in patients with moderate (HTI=1) and high (HTI≥2) HT risk. However, the effect was neutral in those with low risk (HTI=0). In high HT risk patients, there was a steady decline in the rate of symptomatic (HTI=0 vs. HTI≥2: by 3.8%, p=0.120 vs. 14.3%, p<0.001) and any HT (HTI=0 vs. HTI≥2: by 0.6%, p=0.864 vs. 19.5%, p<0.001). Likewise, Cerebrolysin treatment resulted in an overall decrease in the mRS scores (HTI=0 vs. HTI≥2: by 2.1%, p=0.893 vs. 63%, p<0.001) with a reciprocal increase of the fraction with favorable outcome (HTI=0 vs. HTI≥2: by 2% p=0.634 vs. 19.2%, p<0.001). CONCLUSION: Clinically meaningful heterogeneity of Cerebrolysin treatment effects on HT and functional outcome was established in stroke patients. The Cerebrolysin positive impact was significant in those whose estimated on-admission HT risk was either moderate or high.

Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects.

Psychologists are increasingly interested in whether treatment effects vary in randomized controlled trials. A number of tests have been proposed in the causal inference literature to test for such heterogeneity, which differ in the sample statistic they use (either using the variance terms of the experimental and control group, their empirical distribution functions, or specific quantiles), and in whether they make distributional assumptions or are based on a Fisher randomization procedure. In this manuscript, we present the results of a simulation study in which we examine the performance of the different tests while varying the amount of treatment effect heterogeneity, the type of underlying distribution, the sample size, and whether an additional covariate is considered. Altogether, our results suggest that researchers should use a randomization test to optimally control for type 1 errors. Furthermore, all tests studied are associated with low power in case of small and moderate samples even when the heterogeneity of the treatment effect is substantial. This suggests that current tests for treatment effect heterogeneity require much larger samples than those collected in current research.

Harnessing causal forests for epidemiologic research: key considerations.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research.

Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes.

AIMS/HYPOTHESIS: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

Heterogeneous treatment effect estimation for observational data using model-based forests.

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. Model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis.

The impact of chronic disease diagnoses on smoking behavior change and maintenance: Evidence from China.

INTRODUCTION: Managing chronic diseases and tobacco use is a formidable challenge in low- and middle-income countries (LMICs) with limited health literacy and access to quality healthcare. This study examines the empirical evidence from China, utilizing quasi-experimental approaches to assess the causal effect of chronic disease diagnoses on smoking behavior. METHODS: Employing the diagnosis of chronic disease in the older cohorts of the population as a natural experiment, this study utilizes recent advancements in difference-in-difference estimation methods (CS-DID) to investigate the effect of a diagnosis on smoking behavior. Self-reported new diagnoses of conditions ascertained chronic disease diagnoses. CS-DID was run using the study sample from the 2011 to 2018 waves of the China Health and Retirement Longitudinal Study, comparing results with traditional two-way fixed effects and event-study models. RESULTS: The average treatment effect (ATT) of CS-DID is slightly greater than the effects reported using conventional difference-in-difference methods. We found that diagnoses of cancer, heart disease, and stroke reduced smoking rates by 16% (95% CI: -24 - -8), smoking intensity by 0.31 (95% CI: -0.46 - -0.15), and had lasting impacts on smoking cessation behavior (one wave after diagnosis ATT= -0.17; 95% CI: -0.34 - -0.00, two waves after diagnosis ATT= -0.17; 95% CI: -0.37-0.03). A diagnosis of a mild chronic disease, such as hypertension, diabetes, asthma, chronic lung disease, liver disease, or gastric disease, had more negligible and transient effects on smoking behavior. CONCLUSIONS: Efforts to enhance smoking cessation in middle-aged and elderly patients with chronic diseases are crucial to improving health outcomes. The 'teachable moment' of chronic disease diagnosis should be seized to provide smoking cessation assistance to achieve the goal of healthy ageing.

The Collision of digital and green: Digital transformation and green economic efficiency.

Enhancing the green economy efficiency (GEE) is crucial for building a sustainable economy. How can the rapidly advancing digital transformation contribute to this process? The paper empirically examines the direct and spatial spillover effects of digital transformation on cities' GEE in China. This study utilizes the National E-commerce Pilot City (NEPC) policy as a quasi-natural experiment of regional digital transformation and employs the staggered difference-in-differences (DID) method with heterogeneous effects. The findings reveal that (i) implementing the NEPC policy significantly increases urban GEE by 2.6%, corresponding to a 16% increase in the mean of GEE. This effect is particularly pronounced in non-resource-based cities and cities with high Internet penetration. (ii) The mechanism test shows that the pilot policy positively affects GEE by promoting green structural transformation, enhancing green innovation, and strengthening public environmental concerns. (iii) The study highlights a positive spatial spillover effect of the NEPC policy on the GEE of nonpilot cities. (iv) The adoption of the NEPC plays a pivotal role in advancing energy use and carbon emission efficiency. This paper expands the existing knowledge on the green development effects of the digital economy while offering valuable policy insights for building an "Inclusive Green Economy".

Designing Optimal, Data-Driven Policies from Multisite Randomized Trials.

Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.

Fiscal centralization and urban industrial pollution emissions reduction: Evidence from the vertical reform of environmental administrations in China.

The relationship between fiscal regimes and urban industrial pollution emissions is unclear. This paper aims to explore the effects and mechanisms of fiscal centralization on urban industrial pollution emissions and environmental quality. Using the vertical reform of environmental administrations (VREA) in China as a quasi-natural experiment of fiscal centralization, this study applies a staggered difference-in-differences (DID) model to explore the differences in industrial pollution emissions between centralization cities and decentralization cities. The main findings are: (1) VREA significantly inhibits regional industrial pollution emissions, and the reform effect increases over time. This conclusion still holds after considering a series of robustness issues. (2) Industrial sulfur dioxide (SO2) and solid particulate emissions in the fiscal centralization cities have decreased significantly by 0.3281% and 0.2240%, respectively. However, there is no significant change in industrial wastewater discharges. (3) Environmental regulations, environmental expenditures, and pollution control investments of local governments are the main channels through which VREA reduces industrial pollution emissions. (4) The effects of VREA are more significant in central and western cities and small cities. (5) Relative to decentralization cities, centralization cities have improved air and water quality by 0.0825% and 0.1628%, respectively. These findings help to accurately assess the effects of fiscal centralization on regional environmental governance and provide a decision-making reference for further deepening environmental centralization reform in China.

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.

Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome.

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.

Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances.

An important consideration in the design and analysis of randomized trials is the need to account for outcome observations being positively correlated within groups or clusters. Two notable types of designs with this consideration are individually randomized group treatment trials and cluster randomized trials. While sample size methods for testing the average treatment effect are available for both types of designs, methods for detecting treatment effect modification are relatively limited. In this article, we present new sample size formulas for testing treatment effect modification based on either a univariate or multivariate effect modifier in both individually randomized group treatment and cluster randomized trials with a continuous outcome but any types of effect modifier, while accounting for differences across study arms in the outcome variance, outcome intracluster correlation coefficient (ICC) and the cluster size. We consider cases where the effect modifier can be measured at either the individual level or cluster level, and with a univariate effect modifier, our closed-form sample size expressions provide insights into the optimal allocation of groups or clusters to maximize design efficiency. Overall, our results show that the required sample size for testing treatment effect heterogeneity with an individual-level effect modifier can be affected by unequal ICCs and variances between arms, and accounting for such between-arm heterogeneity can lead to more accurate sample size determination. We use simulations to validate our sample size formulas and illustrate their application in the context of two real trials: an individually randomized group treatment trial (the AWARE study) and a cluster randomized trial (the K-DPP study).

Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement savings in Mexico.

Individuals regularly struggle to save for retirement. Using a large-scale field experiment ( N = 97 , 149 ) in Mexico, we test the effectiveness of several behavioral interventions relative to existing policy and each other geared toward improving voluntary retirement savings contributions. We find that an intervention framing savings as a way to secure one's family future significantly improves contribution rates. We leverage recursive partitioning techniques and identify that the overall positive treatment effect masks subpopulations where the treatment is even more effective and other groups where the treatment has a significant negative effect, decreasing contribution rates. Accounting for this variation is significant for theoretical and policy development as well as firm profitability. Our work also provides a methodological framework for how to better design, scale, and deploy behavioral interventions to maximize their effectiveness.

The effect of COVID-19 on self-reported safety incidents in aviation: An examination of the heterogeneous effects using causal machine learning.

INTRODUCTION: Disruptions to aviation operations occur daily on a micro-level with negligible impacts beyond the inconvenience of rebooking and changing aircrew schedules. The unprecedented disruption in global aviation due to COVID-19 highlighted a need to evaluate emergent safety issues rapidly. METHOD: This paper uses causal machine learning to examine the heterogeneous effects of COVID-19 on reported aircraft incursions/excursions. The analysis utilized self report data from NASA Aviation Safety Reporting System collected from 2018 to 2020. The report attributes include self identified group characteristics and expert categorization of factors and outcomes. The analysis identified attributes and subgroup characteristics that were most sensitive to COVID-19 in inducing incursions/excursions. The method included the generalized random forest and difference-in-difference techniques to explore causal effects. RESULTS: The analysis indicates first officers are more prone to experiencing incursion/excursion events during the pandemic. In addition, events categorized with the human factors confusion, distraction, and the causal factor fatigue increased incursion/excursion events. PRACTICAL APPLICATIONS: Understanding the attributes associated with the likelihood of incursion/excursion events provides policymakers and aviation organizations insights to improve prevention mechanisms for future pandemics or extended periods of reduced aviation operations.

Refined moderation analysis with binary outcomes in precision medicine research.

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant. METHOD: The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits. RESULTS: The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use. CONCLUSIONS: The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.