Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark

Ling, Yaobin; Upadhyaya, Pulakesh; Chen, Luyao; Jiang, Xiaoqian; Kim, Yejin

doi:10.1016/j.jbi.2022.104256

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…Future studies need to further ascertain the heterogeneity of treatment effects using methods such as double machine learning, single learner, and X-learner, and identify thresholds of NSAID days that may reduce the risk of depression. 73 Future prospective research studies are needed to confirm these findings and identify a subgroup of older adults with OA and cancer who may benefit from treatment with NSAIDs. 29 A number of other variables (eg, age, education, care fragmentation, poverty, and polypharmacy) predicted incident depression, consistent with bivariate findings.…”

Section: Discussionmentioning

confidence: 98%

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis

et al. 2023

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ObjectiveS: This study examined prescription NSAIDs as one of the leading predictors of incident depression and assessed the direction of the association among older cancer survivors with osteoarthritis. Methods: This study used a retrospective cohort (N = 14, 992) of older adults with incident cancer (breast, prostate, colorectal cancers, or non-Hodgkin’s lymphoma) and osteoarthritis. We used the longitudinal data from the linked Surveillance, Epidemiology, and End Results -Medicare data for the study period from 2006 through 2016, with a 12-month baseline and 12-month follow-up period. Cumulative NSAIDs days was assessed during the baseline period and incident depression was assessed during the follow-up period. An eXtreme Gradient Boosting (XGBoost) model was built with 10-fold repeated stratified cross-validation and hyperparameter tuning using the training dataset. The final model selected from the training data demonstrated high performance (Accuracy: 0.82, Recall: 0.75, Precision: 0.75) when applied to the test data. SHapley Additive exPlanations (SHAP) was used to interpret the output from the XGBoost model. Results: Over 50% of the study cohort had at least one prescption of NSAIDs. Nearly 13% of the cohort were diagnosed with incident depression, with the rates ranging between 7.4% for prostate cancer and 17.0% for colorectal cancer. The highest incident depression rate of 25% was observed at 90 and 120 cumulative NSAIDs days thresholds. Cumulative NSAIDs days was the sixth leading predictor of incident depression among older adults with OA and cancer. Age, education, care fragmentation, polypharmacy, and zip code level poverty were the top 5 predictors of incident depression. Conclusion: Overall, 1 in 8 older adults with cancer and OA were diagnosed with incident depression. Cumulative NSAIDs days was the sixth leading predictor with an overall positive association with incident depression. However, the association was complex and varied by the cumulative NSAIDs days.

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Section: Discussionmentioning

confidence: 98%

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis

et al. 2023

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“…Estimation of HTEs plays an essential role in randomized clinical trials, in particular, to identify patients who benefit most by the intervention for developing personalized treatment (Imai and Ratkovic, 2013;Ling et al, 2023). Meanwhile, traditional statistical approaches like descriptive statistics and regression analysis have a strong tendency to focus on the significance of the estimated overall ATE rather than systematically evaluate the variation in treatment effects across subgroups.…”

Section: Discussionmentioning

confidence: 99%

Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy: post hoc analysis of the CEREHETIS trial

Kalinin,

Khasanova

2024

Front. Pharmacol.

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Background: There has been intensive research into enhancing the effects of reperfusion therapy to mitigate hemorrhagic transformation (HT) in stroke patients. Using neuroprotective agents alongside intravenous thrombolysis (IVT) appears a promising approach. Cerebrolysin is one of the candidates since it consists of neuropeptides mimicking the action of neurotrophic factors on brain protection and repair.Objectives: We looked at treatment effects of Cerebrolysin as an early add-on to IVT in stroke patients with varying HT risk.Methods: It was post hoc analysis of the CEREHETIS trial (ISRCTN87656744). Patients with middle cerebral artery infarction (n = 238) were selected from the intention-to-treat population. To stratify participants according to their HT risk, the DRAGON, SEDAN and HTI scores were computed for each eligible subject using on-admission data. The study endpoints were any and symptomatic HT, and functional outcome measured with the modified Rankin Scale (mRS) on day 90. Favorable functional outcome (FFO) was defined as an mRS ≤2. The performance of each stratification tool was estimated with regression approaches. Heterogeneous treatment effect analysis was conducted using techniques of meta-analysis and the matching-smoothing method.Results: The HTI score outperformed other tools in terms of HT risk stratification. Heterogeneity of Cerebrolysin treatment effects was moderate (I2, 35.8%–56.7%; H2, 1.56–2.31) and mild (I2, 10.9%; H2, 1.12) for symptomatic and any HT, respectively. A significant positive impact of Cerebrolysin on HT and functional outcome was observed in the moderate (HTI = 1) and high (HTI ≥2) HT risk patients, but it was neutral in those with the low (HTI = 0) risk. In particular, there was a steady decline in the rate of symptomatic (HTI = 0 vs. HTI = 4: by 4.3%, p = 0.077 vs. 21.1%, p < 0.001) and any HT (HTI = 0 vs. HTI = 4: by 1.2%, p = 0.737 vs. 32.7%, p < 0.001). Likewise, an mRS score reduction (HTI = 0 vs. HTI = 4: by 1.8%, p = 0.903 vs. 126%, p < 0.001) with a reciprocal increase of the fraction of FFO patients (HTI = 0 vs. HTI = 4: by 1.2% p = 0.757 vs. 35.5%, p < 0.001) was found.Conclusion: Clinically meaningful heterogeneity of Cerebrolysin treatment effects on HT and functional outcome was established in stroke patients. The beneficial effects were significant in those whose estimated on-admission HT risk was either moderate or high.

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“…This billing-purpose claim data is inherently noisy and sparse, thus requires careful data preparation (e.g., setting observation windows to exclude subjects who are not old enough to have AD onset, identifying AD onset via billing-purpose diagnosis and medication codes, grouping high-dimensional diagnosis codes into clinical comorbidities as confounding variables). We followed the data preparation process in our previous work (Ling et al, 2021) based on target trials (Hernán and Robins, 2016). We calculated the average treatment effect among treated (ATT) using inverse probability of treatment weighting (IPTW) (Pearl, 2014, 2000).…”

Section: Methodsmentioning

confidence: 99%

Synthesize Heterogeneous Biological Knowledge via Representation Learning for Alzheimer’s Disease Drug Repurposing

Hsieh

Plascencia-Villa

Lin

et al. 2021

Preprint

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Developing drugs for treating Alzheimer’s disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies. To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates. We developed and computationally validated a heterogeneous graph representation model with transfer learning from universal biomedical databases and with joint optimization with AD risk genes. Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase II/III clinical trials. We experimentally validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations (e.g., Galantamine + Mifepristone). This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

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Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark

Cited by 12 publications

References 38 publications

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis

Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy: post hoc analysis of the CEREHETIS trial

Synthesize Heterogeneous Biological Knowledge via Representation Learning for Alzheimer’s Disease Drug Repurposing

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