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Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011

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  • Yi-Sheng Chao
  • Chao-Jung Wu

Abstract

Producing indices composed of multiple input variables has been embedded in some data processing and analytical methods. We aim to test the feasibility of creating data-driven indices by aggregating input variables according to principal component analysis (PCA) loadings. To validate the significance of both the theory-based and data-driven indices, we propose principles to review innovative indices. We generated weighted indices with the variables obtained in the first years of the two-year panels in the Medical Expenditure Panel Survey initiated between 1996 and 2011. Variables were weighted according to PCA loadings and summed. The statistical significance and residual deviance of each index to predict mortality in the second years was extracted from the results of discrete-time survival analyses. There were 237,832 surviving the first years of panels, represented 4.5 billion civilians in the United States, of which 0.62% (95% CI = 0.58% to 0.66%) died in the second years of the panels. Of all 134,689 weighted indices, there were 40,803 significantly predicting mortality in the second years with or without the adjustment of age, sex and races. The significant indices in the both models could at most lead to 10,200 years of academic tenure for individual researchers publishing four indices per year or 618.2 years of publishing for journals with annual volume of 66 articles. In conclusion, if aggregating information based on PCA loadings, there can be a large number of significant innovative indices composing input variables of various predictive powers. To justify the large quantities of innovative indices, we propose a reporting and review framework for novel indices based on the objectives to create indices, variable weighting, related outcomes and database characteristics. The indices selected by this framework could lead to a new genre of publications focusing on meaningful aggregation of information.

Suggested Citation

  • Yi-Sheng Chao & Chao-Jung Wu, 2017. "Principal component-based weighted indices and a framework to evaluate indices: Results from the Medical Expenditure Panel Survey 1996 to 2011," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-20, September.
    • Handle: RePEc:plo:pone00:0183997
    DOI: 10.1371/journal.pone.0183997
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    References listed on IDEAS

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    1. Angela Hawken & Gerardo Munck, 2013. "Cross-National Indices with Gender-Differentiated Data: What Do They Measure? How Valid Are They?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 111(3), pages 801-838, May.
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    6. Milan Christian de Wet, 2021. "Modelling the Australasian Financial Cycle: A Markov-Regime Switching Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 14(1), pages 69-79, June.
    7. Yi-Sheng Chao & Hsing-Chien Wu & Chao-Jung Wu & Wei-Chih Chen, 2018. "Index or illusion: The case of frailty indices in the Health and Retirement Study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    8. Andrea Serge & Johana Quiroz Montoya & Francisco Alonso & Luis Montoro, 2021. "Socioeconomic Status, Health and Lifestyle Settings as Psychosocial Risk Factors for Road Crashes in Young People: Assessing the Colombian Case," IJERPH, MDPI, vol. 18(3), pages 1-22, January.
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