HRS Bibliography

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Journal Article

Gawronski KAB, Kim ES, Miller LE. Potentially traumatic events and serious life stressors are prospectively associated with frequency of doctor visits and overnight hospital visits. Journal of Psychosomatic Research. 2014;77(2):90-96. doi:10.1016/j.jpsychores.2014.05.009.
Bernstein SFae, Rehkopf D, Tuljapurkar S, Horvitz CC. Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model. Komarova NL, ed. PLOS ONE. 2018;13(5):e0195734. doi:10.1371/journal.pone.0195734.
Curnutt G, Sun Q, Guillemette MA. Practical Applications of Post-Retirement Labor and Non-Retirement Risky Asset Allocation. Practical Applications. 2021;9(2). doi:10.3905/pa.2021.pa473.
Barcelo H, Faul J, Crimmins EM, Thyagarajan B. A Practical Cryopreservation and Staining Protocol for Immunophenotyping in Population Studies. Current Protocols in Cytometry. 2018;84(1):e35. doi:10.1002/cpcy.35.
http://www.ncbi.nlm.nih.gov/pubmed/30040214?dopt=Abstract
Donnelly R. Precarious Work in Midlife: Long-Term Implications for the Health and Mortality of Women and Men. Journal of Health and Social Behavior. 2022;63(1):142-158. doi:10.1177/00221465211055090.
Donnelly R. Precarious Work, Marital Quality, and Divorce: A Gendered Dyadic Analysis of Aging Couples. Innovation in Aging. 2020;4(Suppl 1):605 . doi:10.1093/geroni/igaa057.2043.
Yilmazer T, Scharff RL. Precautionary Savings Against Health Risks: Evidence From the Health and Retirement Study. Research on Aging. 2013;36(2):180-206. doi:10.1177/0164027512473487.
Giustinelli P, Manski CF, Molinari F. Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia. National Bureau of Economic Research Working Paper Series. 2019;No. 26125. doi:10.3386/w26125.PDF icon w26125.pdf (535.1 KB)
Giustinelli P, Manski CF, Molinari F. Precise or Imprecise Probabilities? Evidence from Survey Response Related to Late-onset Dementia. Journal of the European Economic Association. 2022;20(1):187-221. doi:10.1093/jeea/jvab023.
Bobo JKay, Greek AA, Klepinger DH, Herting JR. Predicting 10-year alcohol use trajectories among men age 50 years and older. Am J Geriatr Psychiatry. 2013;21(2):204. doi:10.1097/JGP.0b013e3182423b4b.
Cruz M, Covinsky KE, Widera EW, Stijacic-Cenzer I, Lee SJ. Predicting 10-year mortality for older adults. JAMA. 2013;309(9):874-6. doi:10.1001/jama.2013.1184.
http://www.ncbi.nlm.nih.gov/pubmed/23462780?dopt=Abstract
Aschwanden D, Aichele S, Ghisletta P, et al. Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer's disease : JAD. 2020;75(3):717-728. doi:10.3233/JAD-190967.
Hill NL, Mogle J, Bell TReed, Bhargava S, Wion RK, Bhang I. Predicting current and future anxiety symptoms in cognitively intact older adults with memory complaints. International Journal of Geriatric Psychiatry. 2019;34(12):1874-1882. doi:10.1002/gps.5204.
Boveda I, Metz AJ. Predicting End-of-Career Transitions for Baby Boomers Nearing Retirement Age. The Career Development Quarterly. 2016;64(2):153 - 168. doi:10.1002/cdq.2016.64.issue-210.1002/cdq.12048.
Puterman E, Weiss J, Hives BA, et al. Predicting mortality from 57 economic, behavioral, social, and psychological factors. Proceedings of the National Academy of Sciences. 2020. doi:10.1073/pnas.1918455117.
Banaszak-Holl J, A. Fendrick M, Foster NL, et al. Predicting Nursing Home Admission: Estimates from a seven-year follow-up of a nationally representative sample of older Americans. Alzheimer Disease and Associated Disorders. 2004;18(2):83-89.
Waddell EL, Jacobs-Lawson JM. Predicting positive well-being in older men and women. Int J Aging Hum Dev. 2010;70(3):181-97. doi:10.2190/AG.70.3.a.
http://www.ncbi.nlm.nih.gov/pubmed/20503804?dopt=Abstract
Leaf DErmini, Tysinger B, Goldman DP, Lakdawalla D. Predicting quantity and quality of life with the Future Elderly Model. Health Economics. 2020. doi:10.1002/hec.4169.
Blue L, Gill L, Faul J, Bradway K, Stapleton D. Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures: Evidence From the Health and Retirement Study. Journal of Aging and Health. 2019;31(4):555-579. doi:10.1177/0898264317737893.
http://www.ncbi.nlm.nih.gov/pubmed/29254420?dopt=Abstract
Geiger JR, Wilks SE, Livermore MM. Predicting SNAP Participation in Older Adults: Do Age Categorizations Matter?. Educational Gerontology. 2014;40(12):932-946. doi:10.1080/03601277.2014.912837.
Choi NG, Bohman TM. Predicting the changes in depressive symptomatology in later life: how much do changes in health status, marital and caregiving status, work and volunteering, and health-related behaviors contribute?. J Aging Health. 2007;19(1):152-77. doi:10.1177/0898264306297602.
http://www.ncbi.nlm.nih.gov/pubmed/17215206?dopt=Abstract
Preisser JS, Moss K, Finlayson TL, Jones JA, Weintraub JA. Prediction Model Development and Validation of 12-Year Incident Edentulism of Older Adults in the United States. JDR Clinical & Translational Research. Forthcoming. doi:10.1177/23800844221112062.
M. Islam A, Chowdhury RI. Prediction of disease status: A regressive model approach for repeated measures. Statistical Methodology. 2010;7(5):520-540. doi:https://doi.org/10.1016/j.stamet.2010.03.001.
Chowdhury RI, M. Islam A. Prediction of Disease Status: Transition Model Approach for Repeated Measures. Pakistan Journal of Statistics . 2014;30(2):181-196.PDF icon PAKJSTAT.pdf (403.25 KB)
Chowdhury RI, M. Islam A. Predictive Models for Trajectory Risks Prediction from Repeated Ordinal Outcomes. Bulletin of the Malaysian Mathematical Sciences Society. Forthcoming. doi:10.1007/s40840-022-01277-1.