An introduction of common dynamic predictive modeling methods in medical research_Chinese Journal of Evidence-Based Medicine

Authors：

SONG Ruoqi ^1,2 , WU Shutong ³ ,  WANG Chuangshi ¹

1. Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102300, P. R. China;
2. Department of Statistics and Division of Biostatistics, College of Arts and Sciences and College of Public Health, The Ohio State University, Columbus OH43210, USA;
3. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100730, P. R. China;

Corresponding?author：

WANG Chuangshi, Email: wangchuangshi@fuwai.com

Keywords：

Dynamic prediction model; Joint model; Landmark model; Bayesian approach

DOI：

10.7507/1672-2531.202205137

Video：

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The risk prediction model (RPM) can be used to predict the risks of disease for individuals, playing an extremely important role in decision-making regarding disease prevention, treatment, and prognostic management. Most of the existing RPMs only utilize a single-time cross-section of variable data, so-called static models, which fail to consider the many changes during disease progression and lead to limited prediction accuracy. Dynamic prediction models can incorporate longitudinal data such as repeated measurements of variables during follow-up to capture the longitudinal changes in individual characteristics over time, describe the dynamic trajectory of individual disease risk and improve the prediction accuracy of the models; however, their application in medical research is still relatively small. In this paper, we conducted a systematic literature search to summarize the commonly used dynamic models: joint model, landmark model, and Bayesian dynamic model. By introducing their application scenarios, advantages and disadvantages, and software implementations and conducting comparisons, we aimed to provide methodological references for the future application of dynamic prediction models in medical research.

Citation： SONG Ruoqi, WU Shutong, WANG Chuangshi. An introduction of common dynamic predictive modeling methods in medical research. Chinese Journal of Evidence-Based Medicine, 2022, 22(10): 1224-1232. doi: 10.7507/1672-2531.202205137 Copy

1.	Steyerberg, EW. Clinical prediction models: a practical approach to development, validation, and updating. Switzerland: Springer Cham, 2019.
2.	中國心血管病風險評估和管理指南編寫聯合委員會. 中國心血管病風險評估和管理指南. 中國循環雜志, 2019, 34(1): 4-28.
3.	Bull LM, Lunt M, Martin GP, et al. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagn Progn Res, 2020, 4: 9.
4.	Li L, Astor BC, Lewis J, et al. Longitudinal progression trajectory of GFR among patients with CKD. Am J Kidney Dis, 2012, 59(4): 504-512.
5.	Hickey GL, Grant SW, Caiado C, et al. Dynamic prediction modeling approaches for cardiac surgery. Circ Cardiovasc Qual Outcomes, 2013, 6(6): 649-658.
6.	Rizopoulos D, Molenberghs G, Lesaffre EMEH. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biom J, 2017, 59(6): 1261-1276.
7.	Li L, Yang Z, Hou Y, et al. Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study. BMJ Open, 2020, 10(7): e033965.
8.	Davis SE, Lasko TA, Chen G, et al. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc, 2017, 24(6): 1052-1061.
9.	Suresh K, Taylor JMG, Spratt DE, et al. Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model. Biom J, 2017, 59(6): 1277-1300.
10.	Goldstein BA, Navar AM, Pencina MJ, et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc, 2017, 24(1): 198-208.
11.	Welten M, de Kroon MLA, Renders CM, et al. Repeatedly measured predictors: a comparison of methods for prediction modeling. Diagn Progn Res, 2018, 2: 5.
12.	Goldstein BA, Pomann GM, Winkelmayer WC, et al. A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med, 2017, 36(17): 2750-2763.
13.	翟映紅, 陳琪, 韓賀東, 等. 聯合模型介紹及在醫學研究中的應用. 中華流行病學雜志, 2019, 40(11): 1456-1460.
14.	Ferrer L, Putter H, Proust-Lima C. Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment. Stat Methods Med Res, 2019, 28(12): 3649-3666.
15.	Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica, 2004, 14: 809-834.
16.	Rizopoulos D, Hatfield LA, Carlin BP, et al. Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging. J Am Stat Assoc, 2014, 109(508): 1385-1397.
17.	Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics, 1997, 53(1): 330-339.
18.	Barrett JK, Sweeting MJ, Wood AM. Dynamic risk prediction for cardiovascular disease: an illustration using the ARIC study. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0169716117300044.
19.	McCrink LM, Marshall AH, Cairns KJ. Advances in joint modelling: a review of recent developments with application to the survival of end stage renal disease patients. International Statistical Review, 2013, 81(2): 249-269.
20.	Gleiss A, Oberbauer R, Heinze G. An unjustified benefit: immortal time bias in the analysis of time-dependent events. Transpl Int, 2018, 31(2): 125-130.
21.	van Houwelingen HC, Putter H. Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal, 2008, 14(4): 447-463.
22.	van Houwelingen HC. Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics, 2007, 34(1): 70-85.
23.	Anderson JR, Cain KC, Gelber RD. Analysis of survival by tumor response. J Clin Oncol, 1983, 1(11): 710-719.
24.	Huang X, Yan F, Ning J, et al. A two-stage approach for dynamic prediction of time-to-event distributions. Stat Med, 2016, 35(13): 2167-2182.
25.	Zheng Y, Heagerty PJ. Partly conditional survival models for longitudinal data. Biometrics, 2005, 61(2): 379-391.
26.	Song X, Davidian M, Tsiatis AA. An estimator for the proportional hazards model with multiple longitudinal covariates measured with error. Biostatistics, 2002, 3(4): 511-528.
27.	Nicolaie MA, van Houwelingen JC, de Witte TM, et al. Dynamic pseudo-observations: a robust approach to dynamic prediction in competing risks. Biometrics, 2013, 69(4): 1043-1052.
28.	Bansal A, Heagerty PJ. A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes. Diagn Progn Res, 2019, 3: 14.
29.	Dafni U. Landmark analysis at the 25-year landmark point. Circ Cardiovasc Qual Outcomes, 2011, 4(3): 363-371.
30.	Halabi S, Li C, Luo S. Developing and validating risk assessment models of clinical outcomes in modern oncology. JCO Precis Oncol, 2019, 3: PO.19.00068.
31.	Morgan CJ. Landmark analysis: a primer. J Nucl Cardiol, 2019, 26(2): 391-393.
32.	Grand MK, de Witte TJM, Putter H. Dynamic prediction of cumulative incidence functions by direct binomial regression. Biom J, 2018, 60(4): 734-747.
33.	Kim S, Chen MH, Dey DK, et al. Bayesian dynamic models for survival data with a cure fraction. Lifetime Data Anal, 2007, 13(1): 17-35.
34.	Howle LE, Weber PW, Nichols JM. Bayesian approach to decompression sickness model parameter estimation. Comput Biol Med, 2017, 82: 3-11.
35.	Alsefri M, Sudell M, García-Fi?ana M, et al. Bayesian joint modelling of longitudinal and time to event data: a methodological review. BMC Med Res Methodol, 2020, 20(1): 94.
36.	Yang M, Luo S, DeSantis S. Bayesian quantile regression joint models: inference and dynamic predictions. Stat Methods Med Res, 2019, 28(8): 2524-2537.
37.	He J, McGee DL, Niu X. Application of the Bayesian dynamic survival model in medicine. Stat Med, 2010, 29(3): 347-360.
38.	Li H, Weng J, Mao Y, et al. Adaptive dropout method based on biological principles. IEEE Trans Neural Netw Learn Syst, 2021, 32(9): 4267-4276.
39.	Andrinopoulou ER, Rizopoulos D, Jin R, et al. An introduction to mixed models and joint modeling: analysis of valve function over time. Ann Thorac Surg, 2012, 93(6): 1765-1772.
40.	Parast L, Mathews M, Friedberg MW. Dynamic risk prediction for diabetes using biomarker change measurements. BMC Med Res Methodol, 2019, 19(1): 175.

1. Steyerberg, EW. Clinical prediction models: a practical approach to development, validation, and updating. Switzerland: Springer Cham, 2019.
2. 中國心血管病風險評估和管理指南編寫聯合委員會. 中國心血管病風險評估和管理指南. 中國循環雜志, 2019, 34(1): 4-28.
3. Bull LM, Lunt M, Martin GP, et al. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagn Progn Res, 2020, 4: 9.
4. Li L, Astor BC, Lewis J, et al. Longitudinal progression trajectory of GFR among patients with CKD. Am J Kidney Dis, 2012, 59(4): 504-512.
5. Hickey GL, Grant SW, Caiado C, et al. Dynamic prediction modeling approaches for cardiac surgery. Circ Cardiovasc Qual Outcomes, 2013, 6(6): 649-658.
6. Rizopoulos D, Molenberghs G, Lesaffre EMEH. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biom J, 2017, 59(6): 1261-1276.
7. Li L, Yang Z, Hou Y, et al. Moving beyond the Cox proportional hazards model in survival data analysis: a cervical cancer study. BMJ Open, 2020, 10(7): e033965.
8. Davis SE, Lasko TA, Chen G, et al. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc, 2017, 24(6): 1052-1061.
9. Suresh K, Taylor JMG, Spratt DE, et al. Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model. Biom J, 2017, 59(6): 1277-1300.
10. Goldstein BA, Navar AM, Pencina MJ, et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc, 2017, 24(1): 198-208.
11. Welten M, de Kroon MLA, Renders CM, et al. Repeatedly measured predictors: a comparison of methods for prediction modeling. Diagn Progn Res, 2018, 2: 5.
12. Goldstein BA, Pomann GM, Winkelmayer WC, et al. A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med, 2017, 36(17): 2750-2763.
13. 翟映紅, 陳琪, 韓賀東, 等. 聯合模型介紹及在醫學研究中的應用. 中華流行病學雜志, 2019, 40(11): 1456-1460.
14. Ferrer L, Putter H, Proust-Lima C. Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment. Stat Methods Med Res, 2019, 28(12): 3649-3666.
15. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica, 2004, 14: 809-834.
16. Rizopoulos D, Hatfield LA, Carlin BP, et al. Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging. J Am Stat Assoc, 2014, 109(508): 1385-1397.
17. Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics, 1997, 53(1): 330-339.
18. Barrett JK, Sweeting MJ, Wood AM. Dynamic risk prediction for cardiovascular disease: an illustration using the ARIC study. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0169716117300044.
19. McCrink LM, Marshall AH, Cairns KJ. Advances in joint modelling: a review of recent developments with application to the survival of end stage renal disease patients. International Statistical Review, 2013, 81(2): 249-269.
20. Gleiss A, Oberbauer R, Heinze G. An unjustified benefit: immortal time bias in the analysis of time-dependent events. Transpl Int, 2018, 31(2): 125-130.
21. van Houwelingen HC, Putter H. Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal, 2008, 14(4): 447-463.
22. van Houwelingen HC. Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics, 2007, 34(1): 70-85.
23. Anderson JR, Cain KC, Gelber RD. Analysis of survival by tumor response. J Clin Oncol, 1983, 1(11): 710-719.
24. Huang X, Yan F, Ning J, et al. A two-stage approach for dynamic prediction of time-to-event distributions. Stat Med, 2016, 35(13): 2167-2182.
25. Zheng Y, Heagerty PJ. Partly conditional survival models for longitudinal data. Biometrics, 2005, 61(2): 379-391.
26. Song X, Davidian M, Tsiatis AA. An estimator for the proportional hazards model with multiple longitudinal covariates measured with error. Biostatistics, 2002, 3(4): 511-528.
27. Nicolaie MA, van Houwelingen JC, de Witte TM, et al. Dynamic pseudo-observations: a robust approach to dynamic prediction in competing risks. Biometrics, 2013, 69(4): 1043-1052.
28. Bansal A, Heagerty PJ. A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes. Diagn Progn Res, 2019, 3: 14.
29. Dafni U. Landmark analysis at the 25-year landmark point. Circ Cardiovasc Qual Outcomes, 2011, 4(3): 363-371.
30. Halabi S, Li C, Luo S. Developing and validating risk assessment models of clinical outcomes in modern oncology. JCO Precis Oncol, 2019, 3: PO.19.00068.
31. Morgan CJ. Landmark analysis: a primer. J Nucl Cardiol, 2019, 26(2): 391-393.
32. Grand MK, de Witte TJM, Putter H. Dynamic prediction of cumulative incidence functions by direct binomial regression. Biom J, 2018, 60(4): 734-747.
33. Kim S, Chen MH, Dey DK, et al. Bayesian dynamic models for survival data with a cure fraction. Lifetime Data Anal, 2007, 13(1): 17-35.
34. Howle LE, Weber PW, Nichols JM. Bayesian approach to decompression sickness model parameter estimation. Comput Biol Med, 2017, 82: 3-11.
35. Alsefri M, Sudell M, García-Fi?ana M, et al. Bayesian joint modelling of longitudinal and time to event data: a methodological review. BMC Med Res Methodol, 2020, 20(1): 94.
36. Yang M, Luo S, DeSantis S. Bayesian quantile regression joint models: inference and dynamic predictions. Stat Methods Med Res, 2019, 28(8): 2524-2537.
37. He J, McGee DL, Niu X. Application of the Bayesian dynamic survival model in medicine. Stat Med, 2010, 29(3): 347-360.
38. Li H, Weng J, Mao Y, et al. Adaptive dropout method based on biological principles. IEEE Trans Neural Netw Learn Syst, 2021, 32(9): 4267-4276.
39. Andrinopoulou ER, Rizopoulos D, Jin R, et al. An introduction to mixed models and joint modeling: analysis of valve function over time. Ann Thorac Surg, 2012, 93(6): 1765-1772.
40. Parast L, Mathews M, Friedberg MW. Dynamic risk prediction for diabetes using biomarker change measurements. BMC Med Res Methodol, 2019, 19(1): 175.

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