The treatment of major depressive disorder (MDD) often involves antidepressants, yet non-response to initial therapies remains a significant clinical and economic burden. This research aims to evaluate the comparative efficacy and cost-efficiency of 13 commonly prescribed antidepressants, spanning four major drug classes: SSRIs, SNRIs, NaSSAs, and TCAs. By employing machine learning and simulated patient data, we model non-response rates over two years, highlighting each drug’s cumulative risk trajectories. This study also investigates the direct correlation between non-response rates and estimated healthcare costs, offering insights into the economic implications of antidepressant inefficacy. The analysis reveals distinct patterns of non-response across classes, with SSRIs exhibiting the lowest cumulative risk and cost variability. Conversely, NaSSA and TCA classes demonstrate higher non-response rates, contributing to greater financial strain. Visual representations, including line plots with confidence intervals, bar plots, scatter diagrams, and box plots, provide an intuitive breakdown of risk distribution and economic impact. The primary goal of this research is to guide clinicians and policymakers in selecting cost-effective and efficacious antidepressants, ultimately improving patient outcomes while minimizing unnecessary healthcare expenditure. This study addresses the dual challenges of clinical efficacy and economic sustainability in MDD treatment by integrating statistical modelling with visual analytics. Future work will focus on incorporating real-world demographic and clinical data to enhance the precision and applicability of the findings.
Cite this paper
Filippis, R. D. and Foysal, A. A. (2025). Cost-Optimized and Efficacy-Driven Analysis of Antidepressants in Major Depressive Disorder: A Machine Learning and Visualization Approach. Open Access Library Journal, 12, e2896. doi: http://dx.doi.org/10.4236/oalib.1112896.
Blackburn, T.P. (2019) Depressive Disorders: Treatment Failures and Poor Prognosis over the Last 50 Years. Pharmacology Research & Perspectives, 7, e00472. https://doi.org/10.1002/prp2.472
Voineskos, D., Daskalakis, Z.J. and Blumberger, D.M. (2020) Management of Treatment-Resistant Depression: Challenges and Strategies. Neuropsychiatric Disease and Treatment, 16, 221-234. https://doi.org/10.2147/ndt.s198774
Kekic, A. (2023) Phar-macogenomics in Psychiatric Diseases. In: Primorac, D., Höppner, W. and Bach-Rojecky, L., Eds., Pharmacogenomics in Clinical Practice, Springer, 147-185. https://doi.org/10.1007/978-3-031-45903-0_9
Kendrick, T., Taylor, D. and John-son, C.F. (2019) Which First-Line Antidepressant? British Journal of General Practice, 69, 114-115. https://doi.org/10.3399/bjgp19x701405
Santarsieri, D. and Schwartz, T. (2015) Antidepressant Efficacy and Side-Effect Burden: A Quick Guide for Cli-nicians. Drugs in Context, 4, 1-12. https://doi.org/10.7573/dic.212290
Dauchy, S., Dolbeault, S. and Reich, M. (2013) Depression in Cancer Patients. European Journal of Cancer Supple-ments, 11, 205-215. https://doi.org/10.1016/j.ejcsup.2013.07.006
Werneke, U., Northey, S. and Bhugra, D. (2006) Antidepressants and Sexual Dysfunction. Acta Psychiat-rica Scandinavica, 114, 384-397. https://doi.org/10.1111/j.1600-0447.2006.00890.x
Djordjevic, L. (2014) Household Portfolio Choices and Health Care Systems: What Does Item Non-Response Add to the Picture? In: Varazdin, V.D., Ed., Scientific Book of Proceedings from the 5th International Scientific Conference on Economic, 61.
Macinati, M.S. and Anessi-Pessina, E. (2014) Management Accounting Use and Financial Performance in Public Health-Care Organisations: Evidence from the Italian National Health Service. Health Policy, 117, 98-111. https://doi.org/10.1016/j.healthpol.2014.03.011
Crow, R., Gage, H., Hampson, S., Hart, J., Kimber, A., Storey, L., et al. (2002) The Measurement of Satisfaction with Healthcare: Implications for Practice from a Systematic Review of the Literature. Health Technology Assessment, 6, 1-244. https://doi.org/10.3310/hta6320
Gyllensten, H., Rehnberg, C., Jönsson, A.K., Petzold, M., Carlsten, A. and Andersson Sundell, K. (2013) Cost of Illness of Patient-Reported Adverse Drug Events: A Population-Based Cross-Sectional Survey. BMJ Open, 3, e002574. https://doi.org/10.1136/bmjopen-2013-002574
Desai, P.R., Lawson, K.A., Barner, J.C. and Rascati, K.L. (2013) Estimating the Direct and Indirect Costs for Community-Dwelling Patients with Schizophrenia. Journal of Pharma-ceutical Health Services Research, 4, 187-194. https://doi.org/10.1111/jphs.12027
Chan, E., Zhan, C. and Homer, C.J. (2002) Health Care Use and Costs for Children with Atten-tion-Deficit/Hyperactivity Disorder. Archives of Pediatrics & Adolescent Medi-cine, 156, 504-511. https://doi.org/10.1001/archpedi.156.5.504
Simon, G.E., Manning, W.G., Katzelnick, D.J., Pearson, S.D., Henk, H.J. and Helstad, C.P. (2001) Cost-Effectiveness of Systematic Depression Treatment for High Utilizers of General Medical Care. Archives of General Psychiatry, 58, 181-187. https://doi.org/10.1001/archpsyc.58.2.181
Gupta, S., Isherwood, G., Jones, K. and Van Impe, K. (2015) Productivity Loss and Resource Utilization, and Associated Indirect and Direct Costs in Individuals Providing Care for Adults with Schizophrenia in the EU5. ClinicoEconomics and Outcomes Re-search, 7, 593-602. https://doi.org/10.2147/ceor.s94334
Andersson, A., Levin, L.Å. and Emtinger, B.G. (2002) The Economic Burden of Informal Care. International Journal of Technology Assessment in Health Care, 18, 46-54.
Goettler, A., Grosse, A. and Sonntag, D. (2017) Productivity Loss Due to Overweight and Obesity: A Systematic Review of Indirect Costs. BMJ Open, 7, e014632. https://doi.org/10.1136/bmjopen-2016-014632
O'Hagan, A., Stevenson, M. and Madan, J. (2006) Monte Carlo Probabilistic Sensitivity Analysis for Pa-tient Level Simulation Models: Efficient Estimation of Mean and Variance Using Anova. Health Economics, 16, 1009-1023. https://doi.org/10.1002/hec.1199
Roberts, J.A., Kirkpatrick, C.M.J. and Lipman, J. (2010) Monte Carlo Simulations: Maximizing Antibiotic Pharmacoki-netic Data to Optimize Clinical Practice for Critically Ill Patients. Journal of Anti-microbial Chemotherapy, 66, 227-231. https://doi.org/10.1093/jac/dkq449
Jacobson, S.H. and Sewell, E.C. (2002) Using Monte Carlo Simulation to Determine Combination Vaccine Price Distributions for Childhood Diseases. Health Care Management Science, 5, 135-145. https://doi.org/10.1023/a:1014437201340
Lin, E., Kuo, P., Liu, Y., Yu, Y.W., Yang, A.C. and Tsai, S. (2018) A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers. Frontiers in Psychiatry, 9, Article 290. https://doi.org/10.3389/fpsyt.2018.00290
Perna, G., Alciati, A., Daccò, S., Grassi, M. and Caldirola, D. (2020) Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investigation, 17, 193-206. https://doi.org/10.30773/pi.2019.0289
Johnston, K.M., Powell, L.C., Anderson, I.M., Szabo, S. and Cline, S. (2019) The Burden of Treatment-Resistant Depression: A Systematic Review of the Economic and Quality of Life Literature. Journal of Affective Disorders, 242, 195-210. https://doi.org/10.1016/j.jad.2018.06.045
Driscoll, H.C., Karp, J.F., Dew, M.A. and Reynolds, C.F. (2007) Getting Better, Getting Well: Understand-ing and Managing Partial and Non-Response to Pharmacological Treatment of Non-Psychotic Major Depression in Old Age. Drugs & Aging, 24, 801-814. https://doi.org/10.2165/00002512-200724100-00002
Kendrick, T., Chatwin, J., Dowrick, C., Tylee, A., Morriss, R., Peveler, R., et al. (2009) Ran-domised Controlled Trial to Determine the Clinical Effectiveness and Cost-Effectiveness of Selective Serotonin Reuptake Inhibitors Plus Supportive Care, versus Supportive Care Alone, for Mild to Moderate Depression with So-matic Symptoms in Primary Care: The Thread (Threshold for Antidepressant Response) Study. Health Technology Assessment, 13, 1-159. https://doi.org/10.3310/hta13220
Kessing, L.V., Ziersen, S.C., Andersen, F.M., Gerds, T. and Budtz-Jørgensen, E. (2024) Comparative Responses to 17 Different Antide-pressants in Major Depressive Disorder: Results from a 2‐Year Long‐Term Na-tion‐Wide Population‐Based Study Emulating a Randomized Trial. Acta Psychi-atrica Scandinavica, 149, 378-388. https://doi.org/10.1111/acps.13673
Marks, M. (2018) Psychedelic Medicine for Mental Illness and Substance Use Disorders: Overcoming Social and Legal Obstacles. Journal of Legislation and Public Policy, 21, 69-140.
Pujari, S., Patel, A., Joshi, S.R., Gangakhedkar, R., Ku-marasamy, N. and Gupta, S.B. (2008) Guidelines for Use of Antiretroviral Therapy for HIV Infected Individuals in India (ART Guidelines 2008). The Journal of the Association of Physicians of Indian, 56, 339-371.
Welberry, H. (2021) Using Linked Health and Social Care Data to Monitor Dementia Incidence and Evaluate Dementia Care in Australia. Ph.D. Thesis, UNSW Sydney.
Sartorius, N., Baghai, T.C., Baldwin, D.S., Barrett, B., Brand, U., Fleischhacker, W., et al. (2007) Antidepressant Medications and Other Treatments of Depressive Disorders: A CINP Task Force Report Based on a Review of Evidence. The International Journal of Neuropsychopharmacology, 10, S1-S207. https://doi.org/10.1017/s1461145707008255
Baghai, T.C., Blier, P., Baldwin, D.S., Bauer, M., Goodwin, G.M., Fountoulakis, K.N., et al. (2011) General and Comparative Efficacy and Effectiveness of Antidepressants in the Acute Treatment of Depressive Disorders: A Report by the WPA Section of Pharmacopsychiatry. European Archives of Psychiatry and Clinical Neurosci-ence, 261, 207-245. https://doi.org/10.1007/s00406-011-0259-6
Bandelow, B., Zohar, J., Hollander, E., Kasper, S., Möller, H., WFSBP Task Force on Treatment Guide, et al. (2008) World Federation of Societies of Biological Psychiatry (WFSBP) Guidelines for the Pharmacological Treatment of Anxiety, Obsessive-Compulsive and Post-Traumatic Stress Disorders—First Revision. The World Journal of Bio-logical Psychiatry, 9, 248-312. https://doi.org/10.1080/15622970802465807
Dold, M., Kautzky, A., Bartova, L., Rabl, U., Souery, D., Mendlewicz, J., et al. (2016) Pharmacological Treatment Strategies in Unipolar Depression in European Tertiary Psychiatric Treatment Centers—A Pharmacoepidemiological Cross-Sectional Multicenter Study. European Neuropsychopharmacology, 26, 1960-1971. https://doi.org/10.1016/j.euroneuro.2016.10.005
Resende, S., Deschrijver, C., Van De Velde, E. and Verstraete, A. (2015) Development and Validation of an Analytical Method for Quantification of 15 Non-Tricyclic Anti-depressants in Serum with UPLC-MS/MS. Toxicologie Analytique et Clinique, 27, S54-S55. https://doi.org/10.1016/j.toxac.2015.03.083
Kruger, T. and Christophersen, E. (2006) An Open Label Study of the Use of Dronabinol (Marinol) in the Management of Treatment-Resistant Self-Injurious Behavior in 10 Retarded Adolescent Patients. Journal of Developmental & Behavioral Pedi-atrics, 27, 433. https://doi.org/10.1097/00004703-200610000-00029
Bailey, R.K. (2013) A Doctor’s Prescription for Health Care Reform: The National Medical Association Tackles Disparities, Stigma, and the Status Quo. West Bow Press.
Crews, B.O. and Pesce, A.J. (2024) Drug testing in pain manage-ment. In: Dasgupta, A., Ed., Therapeutic Drug Monitoring, Elsevier, 299-329. https://doi.org/10.1016/b978-0-443-18649-3.00010-0
Filippis, R.d. and Foysal, A.A. (2024) Securing Predictive Psychological Assessments: The Synergy of Blockchain Technology and Artificial Intelligence. Open Access Li-brary Journal, 11, 1-23. https://doi.org/10.4236/oalib.1112378
Filippis, R.d. and Foysal, A.A. (2024) Comparative Analysis of Gabaergics vs. Opioids in Chronic Pain Management. Open Access Library Journal, 11, 1-25. https://doi.org/10.4236/oalib.1112388
