Curbing COVID-19 progression and mortality with traditional Chinese medicine among hospitalized patients with COVID-19: A propensity score-matched analysis.

BACKGROUND: Viral- and host-targeted traditional Chinese medicine (TCM) formulae NRICM101 and NRICM102 were administered to hospitalized patients with COVID-19 during the mid-2021 outbreak in Taiwan. We report the outcomes by measuring the risks of intubation or admission to intensive care unit (ICU) for patients requiring no oxygen support, and death for those requiring oxygen therapy. METHODS: This multicenter retrospective study retrieved data of 840 patients admitted to 9 hospitals between May 1 and July 26, 2021. After propensity score matching, 302 patients (151 received NRICM101 and 151 did not) and 246 patients (123 received NRICM102 and 123 did not) were included in the analysis to assess relative risks. RESULTS: During the 30-day observation period, no endpoint occurred in the patients receiving NRICM101 plus usual care while 14 (9.27%) in the group receiving only usual care were intubated or admitted to ICU. The numbers of deceased patients were 7 (5.69%) in the group receiving NRICM102 plus usual care and 27 (21.95%) in the usual care group. No patients receiving NRICM101 transitioned to a more severe status; NRICM102 users were 74.07% less likely to die than non-users (relative risk= 25.93%, 95% confidence interval 11.73%-57.29%). CONCLUSION: NRICM101 and NRICM102 were significantly associated with a lower risk of intubation/ICU admission or death among patients with mild-to-severe COVID-19. This study provides real-world evidence of adopting broad-spectrum oral therapeutics and shortening the gap between outbreak and effective response. It offers a new vision in our preparation for future pandemics.

Goodness-of-fit tests for a logistic regression model with missing covariates.

Model checking for logistic regression with covariates missing at random is considered. Based on the ideas of Copas (1989) and Osius and Rojek (1992) and studies of Homser et al. (1997), proposed are the two-type goodness-of-fit tests, Pearson chi-squared and unweighted residual sum-of-squares tests, in which their test statistics are centralized by subtracting their estimated mean to be mean-zero-form test statistics via the inverse probability weighting (IPW) and nonparametric multiple imputation (MI) methods to solve the missing value problem. The asymptotic properties of these test statistics are established under the null hypothesis and some regularity conditions. The test statistics conducted by using the IPW and MI estimators are asymptotically equivalent. Proposed are the IPW method and two bootstrap re-sampling approaches for estimation of the variances of the proposed test statistics to solve the issue of underestimating their variances by the MI method of Rubin (1987). Simulation studies are carried out to assess the finite-sample power performances of these proposed tests. Two real data examples are used to illustrate the applicability of the proposed tests.

A traditional Chinese medicine formula NRICM101 to target COVID-19 through multiple pathways: A bedside-to-bench study.

COVID-19 is a global pandemic, with over 50 million confirmed cases and 1.2 million deaths as of November 11, 2020. No therapies or vaccines so far are recommended to treat or prevent the new coronavirus. A novel traditional Chinese medicine formula, Taiwan Chingguan Yihau (NRICM101), has been administered to patients with COVID-19 in Taiwan since April 2020. Its clinical outcomes and pharmacology have been evaluated. Among 33 patients with confirmed COVID-19 admitted in two medical centers, those (n = 12) who were older, sicker, with more co-existing conditions and showing no improvement after 21 days of hospitalization were given NRICM101. They achieved 3 consecutive negative results within a median of 9 days and reported no adverse events. Pharmacological assays demonstrated the effects of the formula in inhibiting the spike protein/ACE2 interaction, 3CL protease activity, viral plaque formation, and production of cytokines interleukin (IL)-6 and tumor necrosis factor (TNF)-α. This bedside-to-bench study suggests that NRICM101 may disrupt disease progression through its antiviral and anti-inflammatory properties, offering promise as a multi-target agent for the prevention and treatment of COVID-19.

The use of time-domain analysis on the choice of measurement location for pulse diagnosis research: A pilot study.

BACKGROUND: Pulse diagnosis researches acquiring pulse waves from the wrist radial artery has not yet addressed the issue of whether this information is affected by differences in the hemodynamic characteristics of pressure waves derived from different locations. This study aimed to clarify whether the blood dynamic states are identical with regard to the "three positions and nine indicators" ((Equation is included in full-text article.)) listed in traditional Chinese medicine (TCM). METHODS: A total of 37 participants of CAD group and 20 participants of healthy group were recruited, and pressure pulse waves were measured at 18 locations on both hands. A multivariate analysis (MANOVA) was performed with a "randomized block design" using SPSS 22.0 and R 3.4.1 to examine the time-domain parameters that represented certain hemodynamic characteristics. RESULTS: In CAD group, the results showed significant differences (p < 0.05) among the h1, h2, h3, h1/t, and h3/h1 measurements of the pulse waves using different indicators at the same position; the h1, h2, h3, and h1/t measurements of the pulse waves at different positions using the indicator "Superficial"; and the h1, h2, h3, h1/t, and h3/h1 measurements of the pulse waves at different positions using the indicator "Medium". In healthy group, the results showed significant differences (p < 0.05) among the h1, h2, h3, and h1/t measurements of the pulse waves using different indicators at the same position; the h1, h2, and h1/t measurements of the pulse waves at different positions using the same indicator. CONCLUSION: Because of the differences in the hemodynamic characteristics among the different positions and indicators, the article might provide a new opinion for future pulse diagnosis investigations to carefully consider the measurement location to ensure the completeness of the information.

Different Harmonic Characteristics Were Found at Each Location on TCM Radial Pulse Diagnosis by Spectrum Analysis.

PURPOSE: This study aimed to clarify whether it is appropriate to choose any measurement location for pulse diagnosis research. METHODS: A total of 37 subjects were recruited and measured for pulse pressure waves at 18 locations (9 per hand of "three positions and nine indicators"). These data were Fourier-transformed to the frequency spectrum, and the harmonics of C0-C10 of each location were obtained. Box plots of the harmonics were generated using SPSS v.22.0 and R v.3.4.1. Data were compared with multivariate analysis of variance (MANOVA) with a randomized block design. RESULTS: The results showed that certain harmonics were different at different positions and different indicators; the harmonics of the same indicator at different positions (except for C8 and C10) and those of different indicators for the same position (except for C4 and C5) were significantly different (p<0.05). CONCLUSIONS: In future researches of pulse diagnosis, due to the significant differences between positions and indicators, it is recommended that the measurement position should be carefully chosen instead of choosing any measurement location to ensure the integrity of the acquired information for further analyzing physiological or pathological status.

Estimation in closed capture-recapture models when covariates are missing at random.

Individual covariates are commonly used in capture-recapture models as they can provide important information for population size estimation. However, in practice, one or more covariates may be missing at random for some individuals, which can lead to unreliable inference if records with missing data are treated as missing completely at random. We show that, in general, such a naive complete-case analysis in closed capture-recapture models with some covariates missing at random underestimates the population size. We develop methods for estimating regression parameters and population size using regression calibration, inverse probability weighting, and multiple imputation without any distributional assumptions about the covariates. We show that the inverse probability weighting and multiple imputation approaches are asymptotically equivalent. We present a simulation study to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. We also illustrate an analysis using data on the bird species yellow-bellied prinia collected in Hong Kong.

An alternative to unrelated randomized response techniques with logistic regression analysis.

The randomized response technique (RRT) is an important tool that is commonly used to protect a respondent's privacy and avoid biased answers in surveys on sensitive issues. In this work, we consider the joint use of the unrelated-question RRT of Greenberg et al. (J Am Stat Assoc 64:520-539, 1969) and the related-question RRT of Warner (J Am Stat Assoc 60:63-69, 1965) dealing with the issue of an innocuous question from the unrelated-question RRT. Unlike the existing unrelated-question RRT of Greenberg et al. (1969), the approach can provide more information on the innocuous question by using the related-question RRT of Warner (1965) to effectively improve the efficiency of the maximum likelihood estimator of Scheers and Dayton (J Am Stat Assoc 83:969-974, 1988). We can then estimate the prevalence of the sensitive characteristic by using logistic regression. In this new design, we propose the transformation method and provide large-sample properties. From the case of two survey studies, an extramarital relationship study and a cable TV study, we develop the joint conditional likelihood method. As part of this research, we conduct a simulation study of the relative efficiencies of the proposed methods. Furthermore, we use the two survey studies to compare the analysis results under different scenarios.

Expected estimating equation using calibration data for generalized linear models with a mixture of Berkson and classical errors in covariates.

Data collected in many epidemiological or clinical research studies are often contaminated with measurement errors that may be of classical or Berkson error type. The measurement error may also be a combination of both classical and Berkson errors and failure to account for both errors could lead to unreliable inference in many situations. We consider regression analysis in generalized linear models when some covariates are prone to a mixture of Berkson and classical errors, and calibration data are available only for some subjects in a subsample. We propose an expected estimating equation approach to accommodate both errors in generalized linear regression analyses. The proposed method can consistently estimate the classical and Berkson error variances based on the available data, without knowing the mixture percentage. We investigated its finite-sample performance numerically. Our method is illustrated by an application to real data from an HIV vaccine study.

Approximate nonparametric corrected-score method for joint modeling of survival and longitudinal data measured with error.

We consider the problem of jointly modeling survival time and longitudinal data subject to measurement error. The survival times are modeled through the proportional hazards model and a random effects model is assumed for the longitudinal covariate process. Under this framework, we propose an approximate nonparametric corrected-score estimator for the parameter, which describes the association between the time-to-event and the longitudinal covariate. The term nonparametric refers to the fact that assumptions regarding the distribution of the random effects and that of the measurement error are unnecessary. The finite sample size performance of the approximate nonparametric corrected-score estimator is examined through simulation studies and its asymptotic properties are also developed. Furthermore, the proposed estimator and some existing estimators are applied to real data from an AIDS clinical trial.

Joint modeling of survival time and longitudinal data with subject-specific changepoints in the covariates.

Joint models are frequently used in survival analysis to assess the relationship between time-to-event data and time-dependent covariates, which are measured longitudinally but often with errors. Routinely, a linear mixed-effects model is used to describe the longitudinal data process, while the survival times are assumed to follow the proportional hazards model. However, in some practical situations, individual covariate profiles may contain changepoints. In this article, we assume a two-phase polynomial random effects with subject-specific changepoint model for the longitudinal data process and the proportional hazards model for the survival times. Our main interest is in the estimation of the parameter in the hazards model. We incorporate a smooth transition function into the changepoint model for the longitudinal data and develop the corrected score and conditional score estimators, which do not require any assumption regarding the underlying distribution of the random effects or that of the changepoints. The estimators are shown to be asymptotically equivalent and their finite-sample performance is examined via simulations. The methods are applied to AIDS clinical trial data.

Semiparametric methods in the proportional odds model for ordinal response data with missing covariates.

We consider the estimation problem of a proportional odds model with missing covariates. Based on the validation and nonvalidation data sets, we propose a joint conditional method that is an extension of Wang et al. (2002, Statistica Sinica 12, 555-574). The proposed method is semiparametric since it requires neither an additional model for the missingness mechanism, nor the specification of the conditional distribution of missing covariates given observed variables. Under the assumption that the observed covariates and the surrogate variable are categorical, we derived the large sample property. The simulation studies show that in various situations, the joint conditional method is more efficient than the conditional estimation method and weighted method. We also use a real data set that came from a survey of cable TV satisfaction to illustrate the approaches.

Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates.

Imputation, weighting, direct likelihood, and direct Bayesian inference (Rubin, 1976) are important approaches for missing data regression. Many useful semiparametric estimators have been developed for regression analysis of data with missing covariates or outcomes. It has been established that some semiparametric estimators are asymptotically equivalent, but it has not been shown that many are numerically the same. We applied some existing methods to a bladder cancer case-control study and noted that they were the same numerically when the observed covariates and outcomes are categorical. To understand the analytical background of this finding, we further show that when observed covariates and outcomes are categorical, some estimators are not only asymptotically equivalent but also actually numerically identical. That is, although their estimating equations are different, they lead numerically to exactly the same root. This includes a simple weighted estimator, an augmented weighted estimator, and a mean-score estimator. The numerical equivalence may elucidate the relationship between imputing scores and weighted estimation procedures.

Estimating the number of Cubans infected sexually by human immunodeficiency virus using contact tracing data.

BACKGROUND: To estimate the yearly number of people in Cuba who are living with human immunodeficiency virus (HIV) and were infected through sexual contact but who have not developed acquired immunodeficiency syndrome (AIDS). Estimation was made directly from the yearly HIV seroprevalence data of the Cuban Partner Notification Programme from 1991 to 2000. METHODS: The generalized removal model for open populations is utilized for the estimation. The total number of known HIV-infected Cubans at each sampling time is used in the prior to provide more reasonable approximations. RESULTS: We estimated a yearly survival rate of 93%. The median estimates for the number of all living asymptomatic HIV-positive Cubans, infected by sexual contact, tripled from 714 in 1991 to 2170 in 2000. The number of unknown HIV-positive Cubans infected sexually is estimated to range from 174 in 1991 to 401 in 2000. CONCLUSIONS: A consistent increase in the number of sexually infected HIV-positive individuals in Cuba from 1991 to 2000 is evident from the estimates. From 1996 onwards more sexually active homosexual/bisexual contacts were traced and consequently more sexually-infected HIV-positives were detected. A consequence of increased detection is the levelling off and subsequent decrease in the number of unknown HIV-positives during this time period. The estimation procedure is useful in estimating prevalent population sizes of epidemiological and public health interest.