Composite diagnostic criteria are problematic for linking potentially distinct populations: the case of frailty

Chao, Yi-Sheng; Wu, Chao-Jung; Wu, Hsing‐Chien; Hsu, Hui‐Ting; Tsao, Lien-Cheng; Cheng, Ya‐Fu; Lai, Yi-Chun; Chen, Wei-Chih

doi:10.1038/s41598-020-58782-1

Cited by 10 publications

(27 citation statements)

References 30 publications

(106 reference statements)

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“…Indices are possible sources of measurement errors and an important factor for inferior outcome predictability ( 1 , 7 ). Indices themselves can also serve as illusory outcomes ( 1 , 16 ). The correlation between index use and trial failure needs to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Second, the frailty statuses are in fact the sum of continuous frailty indices and biases induced by variable dichotomization ( 1 ). It has been well-recognized that variable categorization can be associated with information bias ( 1 , 15 ) and misclassification bias ( 16 ). Third, the threshold for dichotomization is also problematic.…”

Section: Introductionmentioning

confidence: 99%

“…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). Taking metabolic syndrome as example, it requires the information on five conditions to confirm the diagnosis (18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chao¹,

et al. 2021

Front. Med.

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“…In cycle 2, 46 HPV genotypes were tested using urine samples (6,11,13,16,18,26,30,31,32,33,35,39,40,42,43,44,45,51,52,53,54,56,58,59,61,62,66,67,68,69,70,71,72,73,74,81,82,83,84,85,86,87,89,90,91, and 97) (see Figure 1). [22] Samples were obtained from 1,770 female respondents aged 14 to 59 years.…”

Section: Methodsmentioning

confidence: 99%

Human Papillomavirus Infection Rate by Genotype and Vaccination Rates in Canada: Canadian Health Measures Survey 2009 to 2013

Chao

2022

Preprint

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BackgroundAn infection with certain HPV genotypes can lead to cancer or genital warts. HPV can be detected with PCR-based tests, and some genotypes can be prevented by vaccines. However, since the infection rates of various HPV genotypes have not been well reported, the present study aims to provide this information.MethodsThe Canadian Health Measures Survey (CHMS) is an ongoing biannual national survey. Between 2009 and 2011, it sampled a nationally representative sample of females aged 14 to 59 years to determine the infection rates of 46 HPV genotypes. Females aged 9 to 29 years and 9 to 59 years were asked whether they received HPV vaccines between 2009 to 2011 (cycle 2) and 2012 to 2013 (cycle 3), respectively. The reported infection rates and vaccination proportions were weighted and adjusted for the survey design.ResultsAmong the estimated 10,592,968 females aged 14 to 59 years at cycle 2, the HPV genotypes with the highest infection rates were 16, 62, 74, and 54, and the rates were 3.42% (95% CI = 1.67% to 5.17%), 2.14% (95% CI = 0.68% to 3.59%), 2.1% (95% CI = 0.51% to 3.69%), and 2.04% (95% CI = 0.38% to 3.7%), respectively. There were an estimated 6,569,100 and 11,603,752 females aged 9 to 29 and 9 to 59 years at cycles 2 and 3, respectively. The proportions receiving a HPV vaccine were 13.55% (11.18% to 15.92%) and 12.3% (9.8% to 14.79%), respectively. The estimated numbers of females that received HPV vaccines were 890,197 and 1,427,000, respectively.ConclusionCanada is one of the few countries that conduct national surveys to determine HPV infection rates by genotype, which are not limited to the surveillance of carcinogenic genotypes. Our study found discrepancies between the HPV genotypes whose infections were the most common, that could be detected by PCR tests, that were carcinogenic, and that could be prevented by vaccines. For example, 5 of the 7 genotypes (42, 54, 62, 66, and 74) with infection rates of more than 1% cannot be detected by PCR tests and are not targeted by vaccines. HPV 51 is carcinogenic, associated with genital warts, and can be detected by PCR tests, but it is not targeted by vaccines. We recommend a better alignment of the genotypes targeted by HPV tests and vaccines with those genotypes with the highest infection rates in Canada.

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“…These criteria often involve symptoms reported by the patients. [1][2][3] For example, the diagnosis of major depressive disorder defined in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) requires at least one major depressive episode. 1 2 For each major depressive episode, the major criteria are 'depressive mood and/or loss of interest or pleasure in life activities for at least 2 weeks'.…”

Section: Introductionmentioning

confidence: 99%

Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes

Chao¹,

Lin

Wu³

et al. 2020

BMJ Open

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ObjectivesComposite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes).SettingsGeneral psychiatric care.ParticipantsWithout real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses.Primary and secondary outcomesThe relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured.ResultsThe prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses.ConclusionsThere are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

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Composite diagnostic criteria are problematic for linking potentially distinct populations: the case of frailty

Cited by 10 publications

References 30 publications

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

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

Human Papillomavirus Infection Rate by Genotype and Vaccination Rates in Canada: Canadian Health Measures Survey 2009 to 2013

Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes

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