Summarize this content to 100 words: Averitt, A. J., Ryan, P. B., Weng, C. & Perotte, A. A conceptual framework for external validity. J. Biomed. Inform. 121, 103870 (2021).
Google Scholar
Rothwell, P. M. External validity of randomised controlled trials: “To whom do the results of this trial apply? Lancet 365, 82–93 (2005).
Google Scholar
Filbey, L. et al. Improving representativeness in trials: a call to action from the global cardiovascular clinical trialists forum. Eur. Heart J. 44, 921–930 (2023).
Google Scholar
Kennedy-Martin, T., Curtis, S., Faries, D., Robinson, S. & Johnston, J. A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results. Trials 16, 495 (2015).
Google Scholar
Ranganathan, M. & Bhopal, R. Exclusion and inclusion of nonwhite ethnic minority groups in 72 North American and European cardiovascular cohort studies. PLoS Med. 3, e44 (2006).
Google Scholar
Sardar, M. R., Badri, M., Prince, C. T., Seltzer, J. & Kowey, P. R. Underrepresentation of women, elderly patients, and racial minorities in the randomized trials used for cardiovascular guidelines. JAMA Intern. Med. 174, 1868–1870 (2014).
Google Scholar
DeFilippis, E. M. et al. Improving enrollment of underrepresented racial and ethnic populations in heart failure trials: a call to action from the heart failure collaboratory. JAMA Cardiol. 7, 540–548 (2022).
Google Scholar
Oikonomou, E. K., Spatz, E. S., Suchard, M. A. & Khera, R. Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials. Lancet Digit. Health 4, e796–e805 (2022).
Google Scholar
Oikonomou, E. K., Suchard, M. A., McGuire, D. K. & Khera, R. Phenomapping-derived tool to individualize the effect of canagliflozin on cardiovascular risk in type 2 diabetes. Diab. Care 45, 965–974 (2022).
Google Scholar
Oikonomou, E. K. et al. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). Eur. Heart J. 42, 2536–2548 (2021).
Google Scholar
Patel, H. C. et al. Assessing the eligibility criteria in phase iii randomized controlled trials of drug therapy in heart failure with preserved ejection fraction: the critical play-off between a “pure” patient phenotype and the generalizability of trial findings. J. Card. Fail. 23, 517–524 (2017).
Google Scholar
Lim, Y. M. F. et al. Generalizability of randomized controlled trials in heart failure with reduced ejection fraction. Eur. Heart J. Qual. Care Clin. Outcomes 8, 761–769 (2022).
Google Scholar
SPRINT Research Group et al. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103–2116 (2015).
Google Scholar
ACCORD Study Group et al. Effects of intensive blood-pressure control in type 2 diabetes mellitus. N. Engl. J. Med. 362, 1575–1585 (2010).
Google Scholar
Carson, J. L. et al. Restrictive or liberal transfusion strategy in myocardial infarction and anemia. N. Engl. J. Med. 389, 2446–2456 (2023).
Google Scholar
Ducrocq, G. et al. Effect of a restrictive vs liberal blood transfusion strategy on major cardiovascular events among patients with acute myocardial infarction and anemia: the REALITY randomized clinical trial. JAMA 325, 552–560 (2021).
Google Scholar
Joosten, L. P. T. et al. Safety of switching from a vitamin K antagonist to a non-vitamin K antagonist oral anticoagulant in frail older patients with atrial fibrillation: results of the FRAIL-AF randomized controlled trial. Circulation https://doi.org/10.1161/CIRCULATIONAHA.123.066485 (2023).Granger, C. B. et al. Apixaban versus warfarin in patients with atrial fibrillation. N. Engl. J. Med. 365, 981–992 (2011).
Google Scholar
Jane-wit, D., Horwitz, R. I. & Concato, J. Variation in results from randomized, controlled trials: stochastic or systematic? J. Clin. Epidemiol. 63, 56–63 (2010).
Google Scholar
Krakoff, L. R. A tale of 3 trials: ACCORD, SPRINT, and SPS3. What happened? Am. J. Hypertens. 29, 1020–1023 (2016).
Google Scholar
Chobanian, A. V. Hypertension in 2017-what is the right target? JAMA 317, 579–580 (2017).
Google Scholar
Huang, C. et al. Systolic blood pressure response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to control cardiovascular risk in diabetes): a possible explanation for discordant trial results. J. Am. Heart Assoc. 6, e007509 (2017).Laffin, L. J., Besser, S. A. & Alenghat, F. J. A data-zone scoring system to assess the generalizability of clinical trial results to individual patients. Eur. J. Prev. Cardiol. 26, 569–575 (2019).
Google Scholar
Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).
Google Scholar
Ge, Q. et al. Conditional generative adversarial networks for individualized treatment effect estimation and treatment selection. Front. Genet. 11, 585804 (2020).
Google Scholar
Yoon, J., Jordon, J. & Van Der Schaar, M. Ganite: estimation of individualized treat- ment effects using generative adversarial nets. https://openreview.net/pdf?id=ByKWUeWA- (2018).Li, J., Cairns, B. J., Li, J. & Zhu, T. Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications. NPJ Digit. Med. 6, 98 (2023).
Google Scholar
Xu, L., Skoularidou, M., Cuesta-Infante, A. & Veeramachaneni, K. Modeling tabular data using conditional GAN. Preprint at https://doi.org/10.48550/arXiv.1907.00503 (2019).Lederrey, G., Hillel, T. & Bierlaire, M. ciDATGAN: conditional inputs for tabular GANs. Preprint at https://doi.org/10.48550/arXiv.2210.02404 (2022).He, Z. et al. Clinical trial generalizability assessment in the big data era: a review. Clin. Transl. Sci. 13, 675–684 (2020).
Google Scholar
Tripepi, G., Jager, K. J., Dekker, F. W. & Zoccali, C. Stratification for confounding–part 2: direct and indirect standardization. Nephron Clin. Pract. 116, c322–c325 (2010).
Google Scholar
Duan, T., Rajpurkar, P., Laird, D., Ng, A. Y. & Basu, S. Clinical value of predicting individual treatment effects for intensive blood pressure therapy. Circ. Cardiovasc. Qual. Outcomes 12, e005010 (2019).
Google Scholar
Brantner, C. L. et al. Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects. Stat. Med. https://doi.org/10.1002/sim.9955 (2024).Raghavan, S. et al. Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control. Ann. Epidemiol. 65, 101–108 (2022).
Google Scholar
Fisher, C. K., Smith, A. M., Walsh, J. R., Coalition Against Major Diseases & Abbott, Alliance for Aging Research, Alzheimer’s Association, Alzheimer’s Foundation of America, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Critical Path Institute, CHDI Foundation, Inc., Eli Lilly and Company, F. Hoffmann-La Roche Ltd, Forest Research Institute, Genentech, Inc., GlaxoSmithKline, Johnson & Johnson, National Health Council, Novartis Pharmaceuticals Corporation, Parkinson’s Action Network, Parkinson’s Disease Foundation, Pfizer, Inc., sanofi-aventis, Collaborating Organizations: Clinical Data Interchange Standards Consortium (CDISC), Ephibian & Metrum Institute. Machine learning for comprehensive forecasting of Alzheimer’s Disease progression. Sci. Rep. 9, 13622 (2019).Walsh, J. R. et al. Generating digital twins with multiple sclerosis using probabilistic neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.2002.02779 (2020).Bertolini, D. et al. Modeling disease progression in mild cognitive impairment and Alzheimer’s disease with digital twins. Preprint at arXiv https://doi.org/10.48550/arXiv.2012.13455 (2020).Eckardt, J.-N. et al. Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. NPJ Digit. Med. 7, 76 (2024).
Google Scholar
Degtiar, I. & Rose, S. A review of generalizability and transportability. Annu. Rev. Stat. Appl. 10, 501–524 (2023).
Google Scholar
Liu, J. et al. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial. Lancet 404, 245–255 (2024).
Google Scholar
Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER) U.S. Food and Drug Administration. Framework for FDA’s Real World Evidence Program. US Food & Drug Administration https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence (2018).Lederrey, G., Hillel, T. & Bierlaire, M. DATGAN: integrating expert knowledge into deep learning for synthetic tabular data. Preprint at arXiv https://doi.org/10.48550/arXiv.2203.03489 (2022).Khera, R. et al. Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies. BMJ Open 12, e057977 (2022).
Google Scholar
Goodfellow, I. J. et al. Generative adversarial networks. Generative Adversarial Nets. Advances in Neural Information Processing Systems; Curran Associates, Inc. (2014)Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
Google Scholar
Zhao, Z., Kunar, A., Van der Scheer, H., Birke, R. & Chen, L. Y. Ctab-gan: Eff ective table data synthesizing. In Proc. 13th Asian Conference on Machine Learning, Vol. 157, 97–112 (2021).Arjovsky, M., Chintala, S. & Bottou, L. In Proc. 34th International Conference on Machine Learning, Vol. 70, 214–223 (PMLR, 2017).Normand, S. T. et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J. Clin. Epidemiol. 54, 387–398 (2001).
Google Scholar
Patki, N., Wedge, R. & Veeramachaneni, K. The synthetic data vault. In Proc. 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (IEEE, 2016).Zhao Z., Kunar A., Birke R., Van der Scheer, H. & Chen, L.Y. CTAB-GAN+: enhancing tabular data synthesis. Front. Big Data 6, 1296508 (2024).Kamthe, S., Assefa, S. & Deisenroth, M. Copula flows for synthetic data generation. Preprint at arXiv https://doi.org/10.48550/arXiv.2101.00598 (2021).
Source link
A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations
Leave a Comment