Statistics in Biosciences最新文献

The purpose of this work is to improve the efficiency in estimating the average causal effect (ACE) on the survival scale where right-censoring exists and high-dimensional covariate information is available. We propose new estimators using regularized survival regression and survival Random Forest (RF) to adjust for the high-dimensional covariate to improve efficiency. We study the behavior of the adjusted estimators under mild assumptions and show theoretical guarantees that the proposed estimators are more efficient than the unadjusted ones asymptotically when using RF for the adjustment. In addition, these adjusted estimators are $\sqrt{n}$ - consistent and asymptotically normally distributed. The finite sample behavior of our methods is studied by simulation. The simulation results are in agreement with the theoretical results. We also illustrate our methods by analyzing the real data from transplant research to identify the relative effectiveness of identical sibling donors compared to unrelated donors with the adjustment of cytogenetic abnormalities.

Eye tracking (ET) experiments commonly record the continuous trajectory of a subject's gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal components analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.

Correction for systematic measurement error in self-reported data is an important challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been used for this purpose when an objectively measured biomarker is available. However, a big limitation of the regression calibration method is that biomarkers have only been developed for a few dietary components. We propose new methods to use controlled feeding studies to develop valid biomarkers for many more dietary components and to estimate the diet disease associations. Asymptotic distribution theory for the proposed estimators is derived. Extensive simulation is performed to study the finite sample performance of the proposed estimators. We applied our method to examine the associations between the sodium/potassium intake ratio and cardiovascular disease incidence using the Women's Health Initiative cohort data. We discovered positive associations between sodium/potassium ratio and the risks of coronary heart disease, nonfatal myocardial infarction, coronary death, ischemic stroke, and total cardiovascular disease.

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.