Stages of Biological Development across Age: An Analysis of Canadian Health Measure Survey 2007–2011

Chao, Yi-Sheng; Wu, Hsing‐Chien; Wu, Chao-Jung; Chen, Wei-Chih

doi:10.3389/fpubh.2017.00355

Cited by 8 publications

(12 citation statements)

References 41 publications

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“…Syndrome scores were the numerical sums of input symptoms weighted equally or by the PCA loadings. The process was similar to the indices or composite measures identified through index mining, a science with an aim to improve methods to design, generate, and validate composite measures and indices 20,23,42,43 . The PCA loadings weighted input variables to generate PCs linearly 20,44 .…”

Section: Principal Component-based Syndromesmentioning

confidence: 99%

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes

Chao¹,

et al. 2020

Sci Rep

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Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PCbased and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most deathaverse 4-item equal-weight syndromes. In addition to chance alone, input symptoms' variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.Conceptually, frailty syndromes are similar to composite measures or indices that are sums of multiple input variables with equal or unequal weights 20 . Given how differently they are measured and the distinctive theories that inspired them, it is surprising that most frailty syndromes are significantly associated with major health outcomes, especially mortality. One reason is that significant health outcomes may be more likely to be published 21 . Alternatively, there are numerous candidate syndromes to screen, test, and publish. Recent findings in index mining suggest that syndromes can be searched systematically using large data sets and pre-specified rules 20 . For example, there are 72 frailty symptoms identified to form frailty syndromes in the Health and Retirement Study (HRS), and a large number of possible combinations are available 20,22 . Facing this large number of candidate syndromes, there are no well-established criteria to select clinically meaningful syndromes regardless of its statistical significance 18 . The underlying causes associated with new statistically significant frailty syndromes with important outcomes have not been identified. It is necessary to identify the factors contributing to statistical significances of frailty syndromes before assessing the importance of statistical significances in frailty syndromes. Then, a set of criteria for the selection of clinical meaningful syndromes could be developed. This study aims to identify the factors related to the statistical significances of newly generated syndromes, taking frailty syndromes as an example. The characteristics of the newly identified f...

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Section: Principal Component-based Syndromesmentioning

confidence: 99%

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes

Chao¹,

et al. 2020

Sci Rep

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“…We plan to investigate the usefulness of these highly relevant variables identified with PC approximation. Our preliminary research finding is that lung function represents a unique trajectory across individuals aged 3-79 years and FEV0.5/FVC remains important to explain the lung function trajectory throughout the age spectrum (Chao et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…For example, the food frequencies were reported in numbers per year and derived from daily, weekly, and annually intake. The spirometry could be tried for multiple times and the measurements of eight trials were documented in different variables (Chao et al, 2018). The details in the CHMS variables could be found in Data Sheet 2.…”

Section: Data Setsmentioning

confidence: 99%

“…The fourth one included non-redundant variables from the Canadian Health Measures Survey (CHMS), we first selected variables with a correlation-based method that was designed in part to remove redundant variables and increasing computational feasibility (Hall and Smith, 1997;Saeys et al, 2007;Chao et al, 2018). The data redundancy might be created for the ease of survey implementation or data processing or concerns in measurement failures.…”

Section: Data Setsmentioning

confidence: 99%

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Principal Component Approximation and Interpretation in Health Survey and Biobank Data

Chao

et al. 2018

Front. Digit. Humanit.

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“…It has been noticed that the input variables of the three frailty indices better predicted mortality than the indices ( 1 ). In addition, it is very likely that there are numerous alternative indices that can be mined and better predict outcomes ( 1 , 17 ). If the conditions defined by indices are used as outcomes, the danger is that unnecessary interventions may be designed and tested among patients that should be treated otherwise ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Interpretable Trials: Is Interpretability a Reason Why Clinical Trials Fail?

Chao¹,

et al. 2021

Front. Med.

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Background: There are clinical trials using composite measures, indices, or scales as proxy for independent variables or outcomes. Interpretability of derived measures may not be satisfying. Adopting indices of poor interpretability in clinical trials may lead to trial failure. This study aims to understand the impact of using indices of different interpretability in clinical trials.Methods: The interpretability of indices was categorized as: fair-to-poor, good, and unknown. In the literature, frailty indices were considered fair to poor interpretability. Body mass index (BMI) was highly interpretable. The other indices were of unknown interpretability. The trials were searched at clinicaltrials.gov on October 2, 2018. The use of indices as conditions/diseases or other terms was searched. The trials were grouped as completed, terminated, active, and other status. We tabulated the frequencies of frailty, BMI, and other indices.Results: There were 263,928 clinical trials found and 155,606 were completed or terminated. Among 2,115 trials adopting indices or composite measures as condition or disease, 244 adopted frailty and 487 used BMI without frailty indices. Significantly higher proportions of trials of unknown status used indices as conditions/diseases or other terms, compared to completed and terminated trials. The proportions of active trials using frailty indices were significantly higher than those of completed or terminated trials.Discussion: Clinical trial databases can be used to understand why trials may fail. Based on the findings, we suspect that using indices of poor interpretability may be associated with trial failure. Interpretability has not been conceived as an essential criterion for outcomes or proxy measures in trials. We will continue verifying the findings in other databases or data sources and apply this research method to improve clinical trial design. To prevent patients from experiencing trials likely to fail, we suggest further examining the interpretability of the indices in trials.

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Stages of Biological Development across Age: An Analysis of Canadian Health Measure Survey 2007–2011

Cited by 8 publications

References 41 publications

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes

Principal Component Approximation and Interpretation in Health Survey and Biobank Data

Interpretable Trials: Is Interpretability a Reason Why Clinical Trials Fail?

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