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

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

doi:10.1136/bmjopen-2020-037022

Cited by 11 publications

(61 citation statements)

References 27 publications

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“…[25] Only weighted statistics were released[25], and they needed to meet a minimum sample size requirement. [26][27][28][29][30] To protect participant confidentiality and prevent participants from being identified using the released data, Statistics Canada did not allow the release of calculated proportions based on 10 or fewer unweighted participants. At least one participant in each sampling site for each cycle was required.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The correlations between the disease and the other associated disease were 0, 0.3, and 0.7. We assumed the prevalence rates of the disease were 0.05, 0.1, 0.2, 0.4, and 0.8, similar to the values adopted in a previous study 2 . Several conditions have been observed in more than 80% of selected populations, for example, Epstein–Barr virus infection 12 , 13 and the herpes simplex virus type 2 infection 14 .…”

Section: Methodsmentioning

confidence: 99%

“…Symptoms that are considered signs of certain diseases have been used for diagnostic purposes such as those used for the diagnosis of frailty and mental illnesses 1 , 2 . The diagnostic accuracy of these symptoms varies over a wide range 3 .…”

Section: Introductionmentioning

confidence: 99%

“…There are incidence rates and risk ratios for presenting both symptoms, one, or none. The correlations between the symptoms can influence the co-occurrence of multiple symptoms and thus joint incidence ( ir both ) and joint risk ratios ( rr both ) 2 . The sensitivity and specificity 11 of presenting two symptoms for the detection of the disease are ir both × rr both and 1 − ir both respectively.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic accuracy of symptoms for an underlying disease: a simulation study

Chao¹,

Lai

et al. 2022

Sci Rep

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Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms’ diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.

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“…However, there are issues related to the use of composite measures or indices, such as frailty indices and three diagnoses of mental illnesses in clinical trials ( 1 , 14 ). One of the critical issues is interpretability.…”

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

Cited by 11 publications

References 27 publications

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

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

Diagnostic accuracy of symptoms for an underlying disease: a simulation study

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

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