Tag: statistics

J Clin Exp Neuropsychol. 2022 May 22:1-11. doi: 10.1080/13803395.2022.2073334. Online ahead of print.

ABSTRACT

OBJECTIVE: The Delis-Kaplan Executive Function System (D-KEFS) Trail Making Test (TMT) is a commonly used measure of processing speed and executive functioning that may also be useful as an embedded performance validity test (PVT). We evaluated the utility of several multi-condition indices on the D-KEFS TMT in three independent samples to determine an optimal multi-condition index and cutoff on the D-KEFS TMT.

METHOD: Classification accuracy statistics for multiple multi-condition indices on the D-KEFS TMT were evaluated in three independent samples, including a sample with history of mild traumatic brain injury (TBI; n = 267) classified into psychometrically defined performance-valid and performance-invalid subgroups, the D-KEFS national normative sample (n = 1713), and a sample of middle- and older-aged adults diagnosed with mild cognitive impairment (MCI; n = 70).

RESULTS: The D-KEFS TMT Conditions 1-5 summation index maximized sensitivity at .31 while maintaining adequate specificity at ≥.9. This index also had acceptable classification accuracy in both the D-KEFS national normative and MCI cross-validation samples, with the exception of the oldest subgroup of the national norming sample (i.e., individuals’ ages 80-89), in which the observed failure rates for all multi-condition indices tested were greater than 10%.

CONCLUSION: Our study provides support for the use the D-KEFS TMT Conditions 1-5 summation index as an embedded PVT among individuals younger than 80 years-old and from a range of conditions spanning from cognitively normal to mildly impaired; however, further validation is necessary.

PMID:35603485 | DOI:10.1080/13803395.2022.2073334

Nucl Med Commun. 2022 May 20. doi: 10.1097/MNM.0000000000001581. Online ahead of print.

ABSTRACT

PURPOSE: Some studies have suggested that cardiac [123I]metaiodobenzylguanidine images obtained 15-20 min after tracer administration are as accurate for dementia with Lewy bodies (DLB) diagnosis as standard images acquired after a delay of 3-4 h; some suggest delayed imaging is preferable. We compare early and delayed heart-to-mediastinum ratios (HMR) in a well-characterised research dataset and make recommendations for clinical practice.

METHODS: Images were acquired using a Siemens gamma camera with medium energy collimators. Early images were obtained at 20 min and delayed at 4 h (± 30) min. In total 167 pairs of images were reviewed: 30 controls, 39 people with dementia and 98 with mild cognitive impairment. HMR normal cutoff values derived from control data were ≥2.10 for early imaging and ≥1.85 for delayed.

RESULTS: HMR tended to drop between early and delayed for abnormal images, but increase for normal images. Histograms of early and delayed HMR showed a slightly better separation of results into two groups for delayed imaging. Accuracy results were slightly higher for delayed imaging than early imaging (73 vs. 77%), sensitivity 63 vs. 65% and specificity 82 vs. 88%. However, this was not statistically significant – in total only 8/167 (5%) of scans changed designation between early and delayed imaging.

CONCLUSION: We suggest that a delayed image could be acquired only if the early result is borderline. This removes the need for delayed imaging in about 70% of patients. Adopting this protocol in clinical practice would reduce the time most patients have to wait and could free up scanner time.

PMID:35603421 | DOI:10.1097/MNM.0000000000001581

Math Biosci Eng. 2022 Apr 18;19(6):6252-6275. doi: 10.3934/mbe.2022292.

ABSTRACT

In real-life experiments, collecting complete data is time-, finance-, and resources-consuming as stated by statisticians and analysts. Their goal was to compromise between the total time of testing, the number of units under scrutiny, and the expenditures paid through a censoring scheme. Comparing failure-censored schemes (Type-Ⅱ and Progressive Type-Ⅱ) to Time-censored schemes (Type-Ⅰ), it’s worth noting that the former is time-consuming and is no more suitable to be applied in real-life situations. This is the reason why the Type-Ⅰ adaptive progressive hybrid censoring scheme has exceeded other failure-censored types; Time-censored types enable analysts to accomplish their trials and experiments in a shorter time and with higher efficiency. In this paper, the parameters of the inverse Weibull distribution are estimated under the Type-Ⅰ adaptive progressive hybrid censoring scheme (Type-Ⅰ APHCS) based on competing risks data. The model parameters are estimated using maximum likelihood estimation and Bayesian estimation methods. Further, we examine the asymptotic confidence intervals and bootstrap confidence intervals for the unknown model parameters. Monte Carlo simulations are carried out to compare the performance of the suggested estimation methods under Type-Ⅰ APHCS. Moreover, Markov Chain Monte Carlo by applying Metropolis-Hasting algorithm under the square error of loss function is used to compute Bayes estimates and related to the highest posterior density. Finally, two data sets are studied to illustrate the introduced methods of inference. Based on our results, we can conclude that the Bayesian estimation outperforms the maximum likelihood estimation for estimating the inverse Weibull parameters under Type-Ⅰ APHCS.

PMID:35603400 | DOI:10.3934/mbe.2022292

Math Biosci Eng. 2022 Apr 6;19(6):5813-5831. doi: 10.3934/mbe.2022272.

ABSTRACT

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

PMID:35603380 | DOI:10.3934/mbe.2022272

Math Biosci Eng. 2022 Apr 2;19(6):5738-5753. doi: 10.3934/mbe.2022268.

ABSTRACT

With the development of science and technology, digital economy has penetrated into traditional manufacturing industry extensively and deeply, which has motivated the progress of the digital transformation for traditional manufacturing industry. Through comparative analysis of theoretical mechanism of synergetic development, this paper researches the order degree model and the synergy degree model based on the social shaping of technology theory to evaluate the synergetic degree of the digital transformation for China’s traditional manufacturing industry. An empirical study is presented based on the proposed models and the spatial analysis method by using the statistical data of Yangtze River Delta in China from 2014 to 2019. The results were consistent with the actual situation and indicated that the model fully reflect the dynamic interaction between systems, which is a sound basis for scientific judgment and effective decision-making when seeking to the digital transformation of traditional manufacturing industry.

PMID:35603376 | DOI:10.3934/mbe.2022268

Math Biosci Eng. 2022 Mar 28;19(6):5446-5481. doi: 10.3934/mbe.2022256.

ABSTRACT

We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.

PMID:35603364 | DOI:10.3934/mbe.2022256

Educ Inf Technol (Dordr). 2022 May 17:1-26. doi: 10.1007/s10639-022-11106-4. Online ahead of print.

ABSTRACT

Educational institutions abruptly implemented online higher education to cope with sanitary distance restrictions in 2020, causing an increment in student failure. This negative impact attracts the analyses of online higher education as a critical issue for educational systems. The early identification of students at risk is a strategy to cope with this issue by predicting their performance. Computational techniques are projected helpful in performing this task. However, the accurateness of predictions and the best model selection are goals in progress. This work objective is to describe two experiments using student grades of an online higher education program to build and apply three classifiers to predict student performance. In the literature, the three classifiers, a Probabilistic Neural Network, a Support Vector Machine, and a Discriminant Analysis, have proved efficient. I applied the leave-one-out cross-validation method, tested their performances by five criteria, and compared their results through statistical analysis. The analyses of the five performance criteria support the decision on which model applies given particular prediction goals. The results allow timely identification of students at risk of failure for early intervention and predict which students will succeed.

PMID:35603317 | PMC:PMC9110636 | DOI:10.1007/s10639-022-11106-4

Commun Med (Lond). 2022 Mar 2;2:21. doi: 10.1038/s43856-022-00081-z. eCollection 2022.

ABSTRACT

BACKGROUND: Survivors of childhood cancer can develop adverse health events later in life. Infrequent occurrences and scarcity of structured information result in analytical and statistical challenges. Alternative statistical approaches are required to investigate the basis of late effects in smaller data sets.

METHODS: Here we describe sex-specific health care use, mortality and causal associations between primary diagnosis, treatment and outcomes in a small cohort (n = 2315) of 5-year survivors of childhood cancer (n = 2129) in southern Sweden and a control group (n = 11,882; age-, sex- and region-matched from the general population). We developed a constraint-based method for causal inference based on Bayesian estimation of distributions, and used it to investigate health care use and causal associations between diagnoses, treatments and outcomes. Mortality was analyzed by the Kaplan-Meier method.

RESULTS: Our results confirm a significantly higher health care usage and premature mortality among childhood cancer survivors as compared to controls. The developed method for causal inference identifies 98 significant associations (p < 0.0001) where most are well known (n = 73; 74.5%). Hitherto undescribed associations are identified (n = 5; 5.1%). These were between use of alkylating agents and eye conditions, topoisomerase inhibitors and viral infections; pituitary surgery and intestinal infections; and cervical cancer and endometritis. We discuss study-related biases (n = 20; 20.4%) and limitations.

CONCLUSIONS: The findings contribute to a broader understanding of the consequences of cancer treatment. The study shows relevance for small data sets and causal inference, and presents the method as a complement to traditional statistical approaches.

PMID:35603279 | PMC:PMC9053221 | DOI:10.1038/s43856-022-00081-z

Commun Med (Lond). 2022 Mar 1;2:19. doi: 10.1038/s43856-022-00080-0. eCollection 2022.

ABSTRACT

BACKGROUND: The aim of the nationwide prospective seroconversion (PROSECO) study was to investigate the dynamics of anti-SARS-CoV-2 IgG antibodies in the Czech population. Here we report on baseline prevalence from that study.

METHODS: The study included the first 30,054 persons who provided a blood sample between October 2020 and March 2021. Seroprevalence was compared between calendar periods, previous RT-PCR results and other factors.

RESULTS: The data show a large increase in seropositivity over time, from 28% in October/November 2020 to 43% in December 2020/January 2021 to 51% in February/March 2021. These trends were consistent with government data on cumulative viral antigenic prevalence in the population captured by PCR testing – although the seroprevalence rates established in this study were considerably higher. There were only minor differences in seropositivity between sexes, age groups and BMI categories, and results were similar between test providing laboratories. Seropositivity was substantially higher among persons with history of symptoms (76% vs. 34%). At least one third of all seropositive participants had no history of symptoms, and 28% of participants with antibodies against SARS-CoV-2 never underwent PCR testing.

CONCLUSIONS: Our data confirm the rapidly increasing prevalence in the Czech population during the rising pandemic wave prior to the beginning of vaccination. The difference between our results on seroprevalence and PCR testing suggests that antibody response provides a better marker of past infection than the routine testing program.

PMID:35603283 | PMC:PMC9053194 | DOI:10.1038/s43856-022-00080-0

Commun Med (Lond). 2022 May 19;2:54. doi: 10.1038/s43856-022-00106-7. eCollection 2022.

ABSTRACT

BACKGROUND: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion.

METHODS: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries.

RESULTS: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%.

CONCLUSION: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.

PMID:35603270 | PMC:PMC9120146 | DOI:10.1038/s43856-022-00106-7