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Evaluating Community-Based Translational Interventions Using Historical Controls: Propensity Score vs. Disease Risk Score Approach

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Abstract

Many community-based translations of evidence-based interventions are designed as one-arm studies due to ethical and other considerations. Evaluating the impacts of such programs is challenging. Here, we examine the effectiveness of the lifestyle intervention implemented by the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project, a translational lifestyle intervention among American Indian and Alaska Native communities. Data from the landmark Diabetes Prevention Program placebo group was used as a historical control. We compared the use of propensity score (PS) and disease risk score (DRS) matching to adjust for potential confounder imbalance between groups. The unadjusted hazard ratio (HR) for diabetes risk was 0.35 for SDPI-DP lifestyle intervention vs. control. However, when relevant diabetes risk factors were considered, the adjusted HR estimates were attenuated toward 1, ranging from 0.56 (95% CI 0.44–0.71) to 0.69 (95% CI 0.56–0.96). The differences in estimated HRs using the PS and DRS approaches were relatively small but DRS matching resulted in more participants being matched and smaller standard errors of effect estimates. Carefully employed, publicly available randomized clinical trial data can be used as a historical control to evaluate the intervention effectiveness of one-arm community translational initiatives. It is critical to use a proper statistical method to balance the distributions of potential confounders between comparison groups in this kind of evaluations.

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References

  • Arbogast, P. G., Kaltenbach, L., Ding, H., & Ray, W. A. (2008). Adjustment for multiple cardiovascular risk factors using a summary risk score. Epidemiology, 19, 30–37. https://doi.org/10.1097/EDE.0b013e31815be000.

    Article  PubMed  Google Scholar 

  • Arbogast, P. G., & Ray, W. A. (2009). Use of disease risk scores in pharmacoepidemiologic studies. Statistical Methods in Medical Research, 18, 67–80. https://doi.org/10.1177/0962280208092347.

    Article  PubMed  Google Scholar 

  • Arbogast, P. G., & Ray, W. A. (2011). Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders. American Journal of Epidemiology, 174, 613–620. https://doi.org/10.1093/aje/kwr143.

    Article  PubMed  Google Scholar 

  • Baker, S. G., & Lindeman, K. S. (2001). Rethinking historical controls. Biostatistics, 2, 383–396. https://doi.org/10.1093/biostatistics/2.4.383.

    Article  CAS  PubMed  Google Scholar 

  • Buntin, M. B., Jain, A. K., Mattke, S., & Lurie, N. (2009). Who gets disease management? Journal of General Internal Medicine, 24, 649–655. https://doi.org/10.1007/s11606-009-0950-8.

    Article  PubMed  PubMed Central  Google Scholar 

  • Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research on teaching. Chicago: Rand McNally.

    Google Scholar 

  • Cefalu, W. T., Buse, J. B., Tuomilehto, J., Fleming, G. A., Ferrannini, E., Gerstein, H. C., et al. (2016). Update and next steps for real-world translation of interventions for type 2 diabetes prevention: Reflections from a diabetes care editors’ expert forum. Diabetes Care, 39, 1186–1201. https://doi.org/10.2337/dc16-0873.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cepeda, M. S., Boston, R., Farrar, J. T., & Strom, B. L. (2003). Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. American Journal of Epidemiology, 158, 280–287.

    Article  PubMed  Google Scholar 

  • Chevreul, K., Brunn, M., Cadier, B., Nolte, E., & Durand-Zaleski, I. (2014). Evaluating structured care for diabetes: Can calibration on margins help to avoid overestimation of the benefits? An illustration from French diabetes provider networks using data from the ENTRED survey. Diabetes Care, 37, 1892–1899. https://doi.org/10.2337/dc13-2141.

    Article  PubMed  Google Scholar 

  • Collaboration, N. C. D. R. F. (2015). Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: A pooled analysis of 96 population-based studies with 331,288 participants. The Lancet Diabetes and Endocrinology, 3, 624–637. https://doi.org/10.1016/S2213-8587(15)00129-1.

    Article  Google Scholar 

  • Cuticchia, A. J., Cooley, P. C., Hall, R. D., & Qin, Y. (2006). NIDDK data repository: A central collection of clinical trial data. BMC Medical Informatics and Decision Making, 6, 19. https://doi.org/10.1186/1472-6947-6-19.

    Article  PubMed  PubMed Central  Google Scholar 

  • D'Agostino, R. B., Jr. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.

    Article  PubMed  Google Scholar 

  • D'Agostino, R. B., Sr., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008). General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 117, 743–753. https://doi.org/10.1161/CIRCULATIONAHA.107.699579.

    Article  PubMed  Google Scholar 

  • Drake, C. (1993). Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics, 49, 1231–1236. https://doi.org/10.2307/2532266.

    Article  Google Scholar 

  • Dunkley, A. J., Bodicoat, D. H., Greaves, C. J., Russell, C., Yates, T., Davies, M. J., & Khunti, K. (2014). Diabetes prevention in the real world: Effectiveness of pragmatic lifestyle interventions for the prevention of type 2 diabetes and of the impact of adherence to guideline recommendations: A systematic review and meta-analysis. Diabetes Care, 37, 922–933. https://doi.org/10.2337/dc13-2195.

    Article  PubMed  Google Scholar 

  • Flamm, M., Panisch, S., Winkler, H., & Sonnichsen, A. C. (2012). Impact of a randomized control group on perceived effectiveness of a disease management programme for diabetes type 2. European Journal of Public Health, 22, 625–629. https://doi.org/10.1093/eurpub/ckr147.

    Article  PubMed  Google Scholar 

  • Grodstein, F., Stampfer, M. J., Manson, J. E., Colditz, G. A., Willett, W. C., Rosner, B., . . . Hennekens, C. H. (1996). Postmenopausal estrogen and progestin use and the risk of cardiovascular disease. The New England Journal of Medicine, 335, 453–461. https://doi.org/10.1056/NEJM199608153350701.

    Article  CAS  PubMed  Google Scholar 

  • Guo, S. Y., & Fraser, M. W. (2009). Propensity score analysis: Statistical methods and applications: SAGE Publications, Inc.

  • Hansen, B. (2008). The prognostic analogue of the propensity score. Biometrika, 95, 481–488.

    Article  Google Scholar 

  • Harrell, F. E., Jr., Lee, K. L., Matchar, D. B., & Reichert, T. A. (1985). Regression models for prognostic prediction: Advantages, problems, and suggested solutions. Cancer Treat Rep, 69, 1071–1077.

    PubMed  Google Scholar 

  • Henry, D., Tolan, P., Gorman-Smith, D., & Schoeny, M. (2017). Alternatives to randomized control trial designs for community-based prevention evaluation. Prevention Science, 18, 671–680. https://doi.org/10.1007/s11121-016-0706-8.

    Article  PubMed  Google Scholar 

  • Inman, H. F., & Bradley, E. L. (1989). The overlapping coefficient as a measure of agreement between probability-distributions and point estimation of the overlap of 2 normal densities. Communications in Statistics-Theory and Methods, 18, 3851–3874.

    Article  Google Scholar 

  • Jiang, L., Manson, S. M., Beals, J., Henderson, W. G., Huang, H., Acton, K. J., et al. (2013). Translating the diabetes prevention program into American Indian and Alaska native communities: Results from the special diabetes program for Indians diabetes prevention demonstration project. Diabetes Care, 36, 2027–2034. https://doi.org/10.2337/dc12-1250.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kahn, H. S., Cheng, Y. J., Thompson, T. J., Imperatore, G., & Gregg, E. W. (2009). Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Annals of Internal Medicine, 150, 741–751.

    Article  PubMed  Google Scholar 

  • Klein, S., Allison, D. B., Heymsfield, S. B., Kelley, D. E., Leibel, R. L., Nonas, C., et al. (2007). Waist circumference and cardiometabolic risk: A consensus statement from shaping America’s health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Diabetes Care, 30, 1647–1652. https://doi.org/10.2337/dc07-9921.

    Article  PubMed  Google Scholar 

  • Knowler, W. C., Barrett-Connor, E., Fowler, S. E., Hamman, R. F., Lachin, J. M., Walker, E. A., & Nathan, D. M. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. The New England Journal of Medicine, 346, 393–403.

    Article  CAS  Google Scholar 

  • Lee, E. T., Howard, B. V., Wang, W., Welty, T. K., Galloway, J. M., Best, L. G., et al. (2006). Prediction of coronary heart disease in a population with high prevalence of diabetes and albuminuria: The strong heart study. Circulation, 113, 2897–2905. https://doi.org/10.1161/CIRCULATIONAHA.105.593178.

    Article  PubMed  Google Scholar 

  • Lindstrom, J., & Tuomilehto, J. (2003). The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26, 725–731.

    Article  PubMed  Google Scholar 

  • Mann, D. M., Bertoni, A. G., Shimbo, D., Carnethon, M. R., Chen, H., Jenny, N. S., & Muntner, P. (2010). Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US cohort: The multi-ethnic study of atherosclerosis. American Journal of Epidemiology, 171, 980–988. https://doi.org/10.1093/aje/kwq030.

    Article  PubMed  PubMed Central  Google Scholar 

  • Manson, J. E., Hsia, J., Johnson, K. C., Rossouw, J. E., Assaf, A. R., Lasser, N. L., et al. (2003). Estrogen plus progestin and the risk of coronary heart disease. The New England Journal of Medicine, 349, 523–534. https://doi.org/10.1056/NEJMoa030808.

    Article  CAS  PubMed  Google Scholar 

  • Meijnikman, A. S., De Block, C. E. M., Dirinck, E., Verrijken, A., Mertens, I., Corthouts, B., & Van Gaal, L. F. (2017). Not performing an OGTT results in significant underdiagnosis of (pre)diabetes in a high risk adult Caucasian population. International Journal of Obesity, 41, 1615–1620. https://doi.org/10.1038/ijo.2017.165.

    Article  CAS  PubMed  Google Scholar 

  • Miettinen, O. S. (1976). Stratification by a multivariate confounder score. American Journal of Epidemiology, 104, 609–620.

    Article  CAS  PubMed  Google Scholar 

  • Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: Systematic review. BMJ, 343, d7163. https://doi.org/10.1136/bmj.d7163.

    Article  PubMed  PubMed Central  Google Scholar 

  • Norris, S. L., Zhang, X., Avenell, A., Gregg, E., Bowman, B., Schmid, C. H., & Lau, J. (2005). Long-term effectiveness of weight-loss interventions in adults with pre-diabetes: A review. American Journal of Preventive Medicine, 28, 126–139. https://doi.org/10.1016/j.amepre.2004.08.006.

    Article  PubMed  Google Scholar 

  • Pan, X. R., Li, G. W., Hu, Y. H., Wang, J. X., Yang, W. Y., An, Z. X., et al. (1997). Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care, 20, 537–544.

    Article  CAS  PubMed  Google Scholar 

  • Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49, 1373–1379.

    Article  CAS  Google Scholar 

  • Prentice, R. L., Pettinger, M., & Anderson, G. L. (2005). Statistical issues arising in the Women’s Health Initiative. Biometrics, 61, 899–911; discussion 911-841. https://doi.org/10.1111/j.0006-341X.2005.454_1.x.

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41–55. https://doi.org/10.1093/biomet/70.1.41.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control-group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33–38.

    Google Scholar 

  • Schmidt, M. I., Duncan, B. B., Bang, H., Pankow, J. S., Ballantyne, C. M., Golden, S. H., et al. (2005). Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care, 28, 2013–2018.

    Article  PubMed  Google Scholar 

  • Stern, M. P., Williams, K., & Haffner, S. M. (2002). Identification of persons at high risk for type 2 diabetes mellitus: Do we need the oral glucose tolerance test? Annals of Internal Medicine, 136, 575–581.

    Article  PubMed  Google Scholar 

  • Sturmer, T., Schneeweiss, S., Brookhart, M. A., Rothman, K. J., Avorn, J., & Glynn, R. J. (2005). Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. American Journal of Epidemiology, 161, 891–898. https://doi.org/10.1093/aje/kwi106.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sussman, J. B., Kent, D. M., Nelson, J. P., & Hayward, R. A. (2015). Improving diabetes prevention with benefit based tailored treatment: Risk based reanalysis of Diabetes Prevention Program. BMJ, 350, h454. https://doi.org/10.1136/bmj.h454.

    Article  PubMed  PubMed Central  Google Scholar 

  • The Diabetes Prevention Program. (1999). The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care, 22, 623–634.

    Article  Google Scholar 

  • Tuomilehto, J., Lindstrom, J., Eriksson, J. G., Valle, T. T., Hamalainen, H., Ilanne-Parikka, P., et al. (2001). Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. The New England Journal of Medicine, 344, 1343–1350.

    Article  CAS  PubMed  Google Scholar 

  • Varas-Lorenzo, C., Garcia-Rodriguez, L. A., Perez-Gutthann, S., & Duque-Oliart, A. (2000). Hormone replacement therapy and incidence of acute myocardial infarction. A population-based nested case-control study. Circulation, 101, 2572–2578.

    Article  CAS  PubMed  Google Scholar 

  • Wareham, N. J. (2015). Mind the gap: Efficacy versus effectiveness of lifestyle interventions to prevent diabetes. The Lancet Diabetes and Endocrinology, 3, 160–161. https://doi.org/10.1016/S2213-8587(15)70015-X.

    Article  PubMed  Google Scholar 

  • Wilson, P. W., D'Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998). Prediction of coronary heart disease using risk factor categories. Circulation, 97, 1837–1847.

    Article  CAS  PubMed  Google Scholar 

  • Wilson, P. W., Meigs, J. B., Sullivan, L., Fox, C. S., Nathan, D. M., & D'Agostino, R. B., Sr. (2007). Prediction of incident diabetes mellitus in middle-aged adults: The Framingham Offspring Study. Archives of Internal Medicine, 167, 1068–1074. https://doi.org/10.1001/archinte.167.10.1068.

    Article  PubMed  Google Scholar 

  • Wyss, R., Ellis, A. R., Brookhart, M. A., Jonsson Funk, M., Girman, C. J., Simpson, R. J., Jr., & Sturmer, T. (2015). Matching on the disease risk score in comparative effectiveness research of new treatments. Pharmacoepidemiology and Drug Safety, 24, 951–961. https://doi.org/10.1002/pds.3810.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wyss, R., Hansen, B. B., Ellis, A. R., Gagne, J. J., Desai, R. J., Glynn, R. J., & Sturmer, T. (2017). The “dry-run” analysis: A method for evaluating risk scores for confounding control. American Journal of Epidemiology, 185, 842–852. https://doi.org/10.1093/aje/kwx032.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors would like to express our gratitude to the Indian Health Service (IHS) as well as tribal and urban Indian health programs and participants involved in the SDPI-DP. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the IHS. The DPP was conducted by the DPP Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This manuscript was not prepared in collaboration with the Investigators of the DPP study and does not necessarily reflect the opinions or views of the DPP study or the NIDDK. The authors would also like to thank Dr. James Hill for his valuable scientific suggestions and comments.

Grant programs participating in the Special Diabetes Program for Indians Diabetes Prevention Program are as follows: Confederated Tribes of the Chehalis Reservation, Cherokee Nation, Cheyenne River Sioux Tribe, the Chickasaw Nation, Coeur d’Alene Tribe, Colorado River Indian Tribes, Colville Confederated Tribes, Cow Creek Band of Umpqua Tribe, Klamath Tribes, and Coquille Tribe, Fond du Lac Reservation, Gila River Health Care, Haskell Health Center, Ho-Chunk Nation, Indian Health Board of Minneapolis, Indian Health Center of Santa Clara Valley, Native American Rehabilitation Association of the NW, Hunter Health, Kenaitze Indian Tribe IRA, Lawton IHS Service Unit, Menominee Indian Tribe of Wisconsin, Mississippi Band of Choctaw Indians, Norton Sound Health Corporation, Pine Ridge IHS Service Unit, Pueblo of San Felipe, Quinault Indian Nation, Rapid City IHS Diabetes Program, Red Lake Comprehensive Health Services, Rocky Boy Health Board, Seneca Nation of Indians, Sonoma County Indian Health Project, South East Alaska Regional Health Consortium, Southcentral Foundation, Trenton Indian Service Area, Tuba City Regional Health Care Corporation, United American Indian Involvement, Inc., United Indian Health Services, Inc., Warm Springs Health & Wellness Center, Winnebago Tribe of Nebraska, Zuni Pueblo.

Funding

Funding for the SDPI-DP project was provided by the Indian Health Service (HHSI242200400049C, S.M. Manson). Manuscript preparation was supported in part by American Diabetes Association (ADA #7-12-CT-36, L. Jiang), and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (1P30DK092923, S.M. Manson, and R21DK108187, L. Jiang).

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Authors and Affiliations

  1. Department of Epidemiology, School of Medicine, University of California Irvine, Irvine, CA, 92697-7550, USA

    Luohua Jiang

  2. Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA, USA

    Shuai Chen

  3. Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Janette Beals & Spero M. Manson

  4. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Juned Siddique

  5. Department of Epidemiology, Colorado School of Public Health, LEAD Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Richard F. Hamman

  6. Division of Diabetes Treatment and Prevention, Indian Health Service, Rockville, MD, USA

    Ann Bullock

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Consortia

the Special Diabetes Program for Indians Diabetes Prevention Demonstration Project

Grant programs participating in the Special Diabetes Program for Indians Diabetes Prevention Program are as follows: Confederated Tribes of the Chehalis Reservation, Cherokee Nation, Cheyenne River Sioux Tribe, the Chickasaw Nation, Coeur d’Alene Tribe, Colorado River Indian Tribes, Colville Confederated Tribes, Cow Creek Band of Umpqua Tribe, Klamath Tribes, and Coquille Tribe, Fond du Lac Reservation, Gila River Health Care, Haskell Health Center, Ho-Chunk Nation, Indian Health Board of Minneapolis, Indian Health Center of Santa Clara Valley, Native American Rehabilitation Association of the NW, Hunter Health, Kenaitze Indian Tribe IRA, Lawton IHS Service Unit, Menominee Indian Tribe of Wisconsin, Mississippi Band of Choctaw Indians, Norton Sound Health Corporation, Pine Ridge IHS Service Unit, Pueblo of San Felipe, Quinault Indian Nation, Rapid City IHS Diabetes Program, Red Lake Comprehensive Health Services, Rocky Boy Health Board, Seneca Nation of Indians, Sonoma County Indian Health Project, South East Alaska Regional Health Consortium, Southcentral Foundation, Trenton Indian Service Area, Tuba City Regional Health Care Corporation, United American Indian Involvement, Inc., United Indian Health Services, Inc., Warm Springs Health & Wellness Center, Winnebago Tribe of Nebraska, Zuni Pueblo.

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Correspondence to Luohua Jiang.

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Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The SDPI-DP protocol was approved by the institutional review board (IRB) of the University of Colorado and the National IHS IRB. The use of the DPP data from the NIDDK Central Repositories was approved by the University of California Irvine IRB.

Informed Consent

All participants provided written informed consent and Health Insurance Portability and Accountability Act authorization.

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Availability of Data

De-identified DPP data can be obtained from the NIDDK Data Repository following the data request instructions posted on the Repository’s website: https://repository.niddk.nih.gov/pages/overall_instructions/. Due to confidentiality concerns and previous tribal agreements, the SDPI-DP data cannot be made publicly available. Access to the SDPI-DP data can only be requested by contacting the Division of Diabetes Treatment and Prevention of the Indian Health Service.

Availability of SAS Code

The SAS code for DRS matching and dry-run analysis used in the statistical analysis section of this study is included in Appendix 1 of the supplementary materials

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Jiang, L., Chen, S., Beals, J. et al. Evaluating Community-Based Translational Interventions Using Historical Controls: Propensity Score vs. Disease Risk Score Approach. Prev Sci 20, 598–608 (2019). https://doi.org/10.1007/s11121-019-0980-3

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