Optimizing personalized treatments for targeted patient populations across multiple domains
-
Yuan Chen
,
Donglin Zeng
Abstract
Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient’s underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients’ underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.
Funding source: Division of Cancer Prevention, National Cancer Institute
Award Identifier / Grant number: P30-CA008748
Funding source: National Institute of Neurological Disorders and Stroke
Award Identifier / Grant number: NS073671
Funding source: National Institute of Mental Health
Award Identifier / Grant number: MH123487
Funding source: National Institute of General Medical Sciences
Award Identifier / Grant number: GM124104
Acknowledgments
This research is supported by U.S. NIH grants P30-CA008748, NS073671, GM124104, and MH123487.
-
Research ethics: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared
-
Competing interests: The authors state no conflict of interest.
-
Research funding: This research is supported by U.S. NIH grants P30-CA008748, NS073671, GM124104, and MH123487.
-
Data availability: The data that support the findings of this study are available from the National Institute of Mental Health Data Archive (NDA). Data access is subject to approval by NDA.
References
1. Moodie, EE, Richardson, TS, Stephens, DA. Demystifying optimal dynamic treatment regimes. Biometrics 2007;63:447–55. https://doi.org/10.1111/j.1541-0420.2006.00686.x.Search in Google Scholar PubMed
2. Zhao, Y, Zeng, D, Rush, AJ, Kosorok, MR. Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc 2012;107:1106–18. https://doi.org/10.1080/01621459.2012.695674.Search in Google Scholar PubMed PubMed Central
3. Liu, Y, Wang, Y, Kosorok, M, Zhao, Y, Zeng, D. Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens. Stat Med 2018;36:1099–117. https://doi.org/10.1002/sim.7182.Search in Google Scholar PubMed PubMed Central
4. Sugiyama, M, Suzuki, T, Nakajima, S, Kashima, H, von Bünau, P, Kawanabe, M. Direct importance estimation for covariate shift adaptation. Ann Inst Stat Math 2008;60:699–746. https://doi.org/10.1007/s10463-008-0197-x.Search in Google Scholar
5. Shimodaira, H. Improving predictive inference under covariate shift by weighting the log-likelihood function. J Stat Plann Inference 2000;90:227–44. https://doi.org/10.1016/s0378-3758(00)00115-4.Search in Google Scholar
6. Dahabreh, IJ, Robertson, SE, Steingrimsson, JA, Stuart, EA, Hernan, MA. Extending inferences from a randomized trial to a new target population. Stat Med 2020;39:1999–2014. https://doi.org/10.1002/sim.8426.Search in Google Scholar PubMed
7. Degtiar, I, Rose, S. A review of generalizability and transportability. arXiv preprint arXiv:2102.11904 2021.Search in Google Scholar
8. Nguyen, TQ, Ebnesajjad, C, Cole, SR, Stuart, EA. Sensitivity analysis for an unobserved moderator in rct-to-target-population generalization of treatment effects. Ann Appl Stat 2017:225–47. https://doi.org/10.1214/16-aoas1001.Search in Google Scholar
9. Alaa, A, Schaar, M. Limits of estimating heterogeneous treatment effects: guidelines for practical algorithm design. In: International conference on machine learning; 2018:129–38 pp.Search in Google Scholar
10. Zhao, YQ, Zeng, D, Tangen, CM, LeBlanc, ML. Robustifying trial-derived optimal treatment rules for a target population. Electron J Stat 2019;13:1717. https://doi.org/10.1214/19-ejs1540.Search in Google Scholar PubMed PubMed Central
11. Colnet, B, Mayer, I, Chen, G, Dieng, A, Li, R, Varoquaux, G, et al.. Causal inference methods for combining randomized trials and observational studies: a review. arXiv preprint arXiv:2011.08047 2020.Search in Google Scholar
12. Uher, R, Zwicker, A. Etiology in psychiatry: embracing the reality of poly-gene-environmental causation of mental illness. World Psychiatr 2017;16:121–9. https://doi.org/10.1002/wps.20436.Search in Google Scholar PubMed PubMed Central
13. APA. Diagnostic and statistical manual of mental disorders, 5th ed. Washington, DC: American Psychiatric Association; 2013.Search in Google Scholar
14. Hamilton, M. The Hamilton rating scale for depression. In: Assessment of depression. Berlin, Heidelberg: Springer; 1986:143–52 pp.10.1007/978-3-642-70486-4_14Search in Google Scholar
15. Cole, DA, Cai, L, Martin, NC, Findling, RL, Youngstrom, EA, Garber, J, et al.. Structure and measurement of depression in youths: applying item response theory to clinical data. Psychol Assess 2011;23:819. https://doi.org/10.1037/a0023518.Search in Google Scholar PubMed PubMed Central
16. Chekroud, AM, Gueorguieva, R, Krumholz, HM, Trivedi, MH, Krystal, JH, McCarthy, G. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatr 2017;74:370–8. https://doi.org/10.1001/jamapsychiatry.2017.0025.Search in Google Scholar PubMed PubMed Central
17. Rush, AJ, Fava, M, Wisniewski, SR, Lavori, PW, Trivedi, MH, Sackeim, HA, et al.. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Contr Clin Trials 2004;25:119–42. https://doi.org/10.1016/s0197-2456(03)00112-0.Search in Google Scholar PubMed
18. Trivedi, MH, Kocsis, JH, Thase, ME, Morris, DW, Wisniewski, SR, Leon, AC, et al.. REVAMP – research evaluating the value of augmenting medication with psychotherapy: rationale and design. Psychopharmacol Bull 2008;41:5–33.Search in Google Scholar
19. Mo, W, Qi, Z, Liu, Y. Learning optimal distributionally robust individualized treatment rules. J Am Stat Assoc 2020;116:659–74. https://doi.org/10.1080/01621459.2020.1796359.Search in Google Scholar PubMed PubMed Central
20. Chen, Y, Zeng, D, Wang, Y. Learning individualized treatment rules for multiple-domain latent outcomes. J Am Stat Assoc 2021;116:269–82. https://doi.org/10.1080/01621459.2020.1817751.Search in Google Scholar PubMed PubMed Central
21. Harman, HH. Modern factor analysis. Chicago: University of Chicago Press; 1976.Search in Google Scholar
22. Dai, B, Shen, X, Wang, J. Embedding learning. J Am Stat Assoc 2020;117:307–19. https://doi.org/10.1080/01621459.2020.1775614.Search in Google Scholar PubMed PubMed Central
23. Rubin, DB. Bayesian inference for causal effects: the role of randomization. Ann Stat 1978:34–58. https://doi.org/10.1214/aos/1176344064.Search in Google Scholar
24. Liang, S, Lu, W, Song, R. Deep advantage learning for optimal dynamic treatment regime. Stat Theory Relat Fields 2018;2:80–8. https://doi.org/10.1080/24754269.2018.1466096.Search in Google Scholar PubMed PubMed Central
25. Mi, X, Zou, F, Zhu, R. Bagging and deep learning in optimal individualized treatment rules. Biometrics 2019;75:674–84. https://doi.org/10.1111/biom.12990.Search in Google Scholar PubMed
26. Hamilton, M. A rating scale for depression. J Neurol Neurosurg Psychiatr 1960;23:56. https://doi.org/10.1136/jnnp.23.1.56.Search in Google Scholar PubMed PubMed Central
27. Kingma, DP, Ba, J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 2014.Search in Google Scholar
28. Cole, SR, Hernán, MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008;168:656–64. https://doi.org/10.1093/aje/kwn164.Search in Google Scholar PubMed PubMed Central
29. Hainmueller, J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit Anal 2012:25–46. https://doi.org/10.1093/pan/mpr025.Search in Google Scholar
30. Rubin, DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 1974;66:688. https://doi.org/10.1037/h0037350.Search in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijb-2024-0068).
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Random forests for survival data: which methods work best and under what conditions?
- Flexible variable selection in the presence of missing data
- An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
- MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
- Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
- Hypothesis testing for detecting outlier evaluators
- Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
- Commentary
- Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
- Research Articles
- Optimizing personalized treatments for targeted patient populations across multiple domains
- Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
- History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
- Revisiting incidence rates comparison under right censorship
- Ensemble learning methods of inference for spatially stratified infectious disease systems
- The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
- Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
- Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
- Improving the mixed model for repeated measures to robustly increase precision in randomized trials
- Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data
- A modified rule of three for the one-sided binomial confidence interval
- Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
- Bayesian estimation and prediction for network meta-analysis with contrast-based approach
- Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
Articles in the same Issue
- Frontmatter
- Research Articles
- Random forests for survival data: which methods work best and under what conditions?
- Flexible variable selection in the presence of missing data
- An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
- MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
- Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
- Hypothesis testing for detecting outlier evaluators
- Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
- Commentary
- Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
- Research Articles
- Optimizing personalized treatments for targeted patient populations across multiple domains
- Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
- History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
- Revisiting incidence rates comparison under right censorship
- Ensemble learning methods of inference for spatially stratified infectious disease systems
- The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
- Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
- Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
- Improving the mixed model for repeated measures to robustly increase precision in randomized trials
- Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data
- A modified rule of three for the one-sided binomial confidence interval
- Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
- Bayesian estimation and prediction for network meta-analysis with contrast-based approach
- Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
