Statistics in Biosciences最新文献

The statistical analysis of enzyme kinetic reactions usually involves models of the response functions which are well defined on the basis of Michaelis-Menten type equations. The error structure, however, is often without good reason assumed as additive Gaussian noise. This simple assumption may lead to undesired properties of the analysis, particularly when simulations are involved and consequently negative simulated reaction rates may occur. In this study, we investigate the effect of assuming multiplicative log normal errors instead. While there is typically little impact on the estimates, the experimental designs and their efficiencies are decisively affected, particularly when it comes to model discrimination problems.

In 2020, the whole planet was plagued by the extremely deadly COVID-19 pandemic. More than 83 million people had been infected with COVID-19 while more than 1.9 million people around the planet had died from this virus in the first year of the pandemic. From the first moment, the medical community started working to deal with this pandemic. For this reason, many clinical trials have been and continue to be conducted to find a safe and efficient cure for the virus. In this paper, we review the 96 clinical trials, registered in the ClinicalTrials.gov database, that had been completed by the end of the first year of the pandemic. Although the clinical trials contained significant heterogeneity in the main methodological features (enrollment, duration, allocation, intervention model, and masking) they seemed to be conducted based on an appropriate methodological basis.

Biomedical studies, such as clinical trials, often require the comparison of measurements from two correlated tests in which each unit of observation is associated with a binary outcome of interest via relative risk. The associated confidence interval is crucial because it provides an appreciation of the spectrum of possible values, allowing for a more robust interpretation of relative risk. Of the available confidence interval methods for relative risk, the asymptotic score interval is the most widely recommended for practical use. We propose a modified score interval for relative risk and we also extend an existing nonparametric U-statistic-based confidence interval to relative risk. In addition, we theoretically prove that the original asymptotic score interval is equivalent to the constrained maximum likelihood-based interval proposed by Nam and Blackwelder. Two clinically relevant oncology trials are used to demonstrate the real-world performance of our methods. The finite sample properties of the new approaches, the current standard of practice, and other alternatives are studied via extensive simulation studies. We show that, as the strength of correlation increases, when the sample size is not too large the new score-based intervals outperform the existing intervals in terms of coverage probability. Moreover, our results indicate that the new nonparametric interval provides the coverage that most consistently meets or exceeds the nominal coverage probability.

The outbreak of the novel coronavirus (COVID-19) was declared to be a global emergency in January of 2020, and everyday life throughout the world was disrupted. Among many questions about COVID-19 that remain unanswered, it is of interest for society to identify whether there is any significant difference in daily case counts between males and females. The daily case count sequences are correlated due to the nature of a contagious disease, and contain a nonlinear trend owing to several unexpected events, such as vaccinations and the appearance of the delta variant. It is possible that these unexpected events have changed the dynamical system that generates data. The classic t-test is not appropriate to analyze such correlated data with a nonconstant trend. This study applies a simultaneous confidence band approach in an attempt to overcome these difficulties; that is, a simultaneous confidence band for the trend of an autoregressive moving-average time series is constructed using B-spline estimation. The proposed method is applied to the daily case count data of seniors of both genders (at least 60 years old) in the State of Ohio from April 1, 2020 to March 31, 2022, and the result shows that there is a significant difference at the 95% confidence level between the two gender case counts adjusted for the population sizes.

The spatial scan statistics based on the Poisson and binomial models are the most common methods to detect spatial clusters in disease surveillance. These models rely on Monte-Carlo simulation which are time consuming. Moreover, frequently, datasets present over-dispersion which cannot be handled by them. Thus, we have the following goals. First, we propose irregularly shaped spatial scan for the Bell, Poisson, and binomial. The Bell distribution has just one parameter but it is capable of handling over-dispersed datasets. Second, we apply these scan statistics to big maps. A fast version, without Monte-Carlo simulation, for the proposed Poisson and binomial scans is introduced. Intensive simulation studies are carried out to assess the quality of the proposals. In addition, we show the time improvement of the fast scan versions over their traditional ones. Finally, we end the paper with an application on the detection of irregular shape small nodules in a medical image.

Supplementary information: The online version contains supplementary material available at 10.1007/s12561-022-09353-7.

This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.

Time-dependent covariates are often measured intermittently and with measurement errors. Motivated by the AIDS Clinical Trials Group (ACTG) 175 trial, this paper develops statistical inferences for the Cox model for partly interval censored failure times and longitudinal covariates with measurement errors. The conditional score methods developed for the Cox model with measurement errors and right censored data are no longer applicable to interval censored data. Assuming an additive measurement error model for a longitudinal covariate, we propose a nonparametric maximum likelihood estimation approach by deriving the measurement error induced hazard model that shows the attenuating effect of using the plug-in estimate for the true underlying longitudinal covariate. An EM algorithm is devised to facilitate maximum likelihood estimation that accounts for the partly interval censored failure times. The proposed methods can accommodate different numbers of replicates for different individuals and at different times. Simulation studies show that the proposed methods perform well with satisfactory finite-sample performances and that the naive methods ignoring measurement error or using the plug-in estimate can yield large biases. A hypothesis testing procedure for the measurement error model is proposed. The proposed methods are applied to the ACTG 175 trial to assess the associations of treatment arm and time-dependent CD4 cell count on the composite clinical endpoint of AIDS or death.

Supplementary information: The online version contains supplementary material available at 10.1007/s12561-023-09372-y.

Novel biomarkers, in combination with currently available clinical information, have been sought to enhance clinical decision making in many branches of medicine, including screening, surveillance and prognosis. An individualized clinical decision rule (ICDR) is a decision rule that matches subgroups of patients with tailored medical regimen based on patient characteristics. We proposed new approaches to identify ICDRs by directly optimizing a risk-adjusted clinical benefit function that acknowledges the tradeoff between detecting disease and over-treating patients with benign conditions. In particular, we developed a novel plug-in algorithm to optimize the risk-adjusted clinical benefit function, which leads to the construction of both nonparametric and linear parametric ICDRs. In addition, we proposed a novel approach based on the direct optimization of a smoothed ramp loss function to further enhance the robustness of a linear ICDR. We studied the asymptotic theories of the proposed estimators. Simulation results demonstrated good finite sample performance for the proposed estimators and improved clinical utilities when compared to standard approaches. The methods were applied to a prostate cancer biomarker study.