Category: Statistics

J Cereb Blood Flow Metab. 2025 Jan 23:271678X251314682. doi: 10.1177/0271678X251314682. Online ahead of print.

ABSTRACT

Zero echo time (zero-TE) pulse sequences provide a quiet and artifact-free alternative to conventional functional magnetic resonance imaging (fMRI) pulse sequences. The fast readouts (<1 ms) utilized in zero-TE fMRI produce an image contrast with negligible contributions from blood oxygenation level-dependent (BOLD) mechanisms, yet the zero-TE contrast is highly sensitive to brain function. However, the precise relationship between the zero-TE contrast and neuronal activity has not been determined. Therefore, we aimed to derive a function to model the temporal dynamics of the zero-TE fMRI signal in response to neuronal activity. Furthermore, we examined the correlation of zero-TE fMRI with neuronal activity across stimulation frequencies. To these ends, we performed simultaneous electrophysiological recordings and zero-TE fMRI in rats subjected to whisker stimulation. The presented impulse response function provides a basis for the statistical modeling of neuronal activity-induced changes in the zero-TE fMRI signal. The temporal characteristics of the zero-TE fMRI response were found to be consistent with the previously postulated non-BOLD hemodynamic origin of the functional contrast. The zero-TE fMRI signal was well predicted by electrophysiological recordings, although systematic stimulation-dependent residuals were also observed, suggesting nonlinearities in neurovascular coupling. We conclude that zero-TE fMRI provides a robust proxy for neuronal activity.

PMID:39846159 | DOI:10.1177/0271678X251314682

Circ Genom Precis Med. 2025 Jan 23:e004629. doi: 10.1161/CIRCGEN.124.004629. Online ahead of print.

ABSTRACT

BACKGROUND: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.

METHODS: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System. We included patients with an Lp(a) measured between 1989 and 2022 and who had at least 1 year of electronic health record data before measurement of an Lp(a) level. The end point of interest was time to first myocardial infarction, stroke/TIA, or coronary revascularization. A random survival forest model was derived and compared with a Cox proportional hazards model derived from traditional cardiovascular risk factors (ie, the variables used to estimate the Pooled Cohort Equations for the primary prevention population and the variables used to estimate the Second Manifestations of Arterial Disease and Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention scores for the secondary prevention population). Model discrimination was evaluated using Harrell C-index.

RESULTS: A total of 4369 patients were included in the study (49.5% were female, mean age was 51 [SD, 18] years, and mean Lp(a) level was 33.6 [38.6] mg/dL, of whom 23.7% had a prior cardiovascular event). The random survival forest model outperformed the traditional risk factor models in the test set (c-index, 0.82 [random forest] versus 0.69 [primary prevention] versus 0.80 [secondary prevention]). These results were similar when restricted to a primary prevention population and under various strategies to handle competing risk. A Cox proportional hazard model based on the top 25 variables from the random forest model had a c-index of 0.80.

CONCLUSIONS: A random survival forest model outperformed a model using traditional risk factors for predicting cardiovascular events in patients with a measured Lp(a) level.

PMID:39846157 | DOI:10.1161/CIRCGEN.124.004629

Pharm Stat. 2025 Jan-Feb;24(1):e2467. doi: 10.1002/pst.2467.

ABSTRACT

With contemporary anesthetic drugs, the efficacy of general anesthesia is assured. Health-economic and clinical objectives are related to reductions in the variability in dosing, variability in recovery, etc. Consequently, meta-analyses for anesthesiology research would benefit from quantification of ratios of standard deviations of log-normally distributed variables (e.g., surgical duration). Generalized confidence intervals can be used, once sample means and standard deviations in the raw, time, scale, for each study and group have been used to estimate the mean and standard deviation of the logarithms of the times (i.e., “log-scale”). We examine the matching of the first two moments versus also using higher-order terms, following Higgins et al. 2008 and Friedrich et al. 2012. Monte Carlo simulations revealed that using the first two moments 95% confidence intervals had coverage 92%-95%, with small bias. Use of higher-order moments worsened confidence interval coverage for the log ratios, especially for coefficients of variation in the time scale of 50% and for larger n = 50 $$ left(n=50right) $$ sample sizes per group, resulting in 88% coverage. We recommend that for calculating confidence intervals for ratios of standard deviations based on generalized pivotal quantities and log-normal distributions, when relying on transformation of sample statistics from time to log scale, use the first two moments, not the higher order terms.

PMID:39846155 | DOI:10.1002/pst.2467

Stat Methods Med Res. 2025 Jan 23:9622802241311458. doi: 10.1177/09622802241311458. Online ahead of print.

ABSTRACT

The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results. Directed acyclic graphs commonly used in epidemiology and in causality, could provide good feedback for learning the possibilities and limits of this extrapolation applied to the binary classification problem. Both the covariate-specific and the covariate-adjusted receiver operating characteristic curves are valuable tools, which can help to a better understanding of the real classification abilities of a marker. Since they are strongly related with the conditional distributions of the marker on the positive (subjects with the studied characteristic) and negative (subjects without the studied characteristic) populations, the use of proportional hazard regression models arises in a very natural way. We explore the use of flexible proportional hazard Cox regression models for estimating the covariate-specific and the covariate-adjusted receiver operating characteristic curves. We study their large- and finite-sample properties and apply the proposed estimators to a real-world problem. The developed code (in R language) is provided on Supplemental Material.

PMID:39846150 | DOI:10.1177/09622802241311458

Stat Methods Med Res. 2025 Jan 23:9622802241310328. doi: 10.1177/09622802241310328. Online ahead of print.

ABSTRACT

One primary goal of precision medicine is to estimate the individualized treatment rules that optimize patients’ health outcomes based on individual characteristics. Health studies with multiple treatments are commonly seen in practice. However, most existing individualized treatment rule estimation methods were developed for the studies with binary treatments. Many require that the outcomes are fully observed. In this article, we propose a matching-based machine learning method to estimate the optimal individualized treatment rules in observational studies with multiple treatments when the outcomes are fully observed or right-censored. We establish theoretical property for the proposed method. It is compared with the existing competitive methods in simulation studies and a hepatocellular carcinoma study.

PMID:39846149 | DOI:10.1177/09622802241310328

Pharm Stat. 2025 Jan-Feb;24(1):e2464. doi: 10.1002/pst.2464.

ABSTRACT

Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity. In addition, they studied the survival outcome only, not competing risk outcomes. Therefore, we propose a clustering-based commensurate prior model with random effects for both survival and competing risk outcomes that effectively borrows information based on the degree of comparability between historical and CT data. Simulation results show that the proposed method controls type I errors better and has a lower bias than some competing methods. We apply our method to a phase III CT which compares the effectiveness of bone marrow donated from family members with only partially matched bone marrow versus two partially matched cord blood units to treat leukemia and lymphoma.

PMID:39846144 | DOI:10.1002/pst.2464

Pharm Stat. 2025 Jan-Feb;24(1):e2465. doi: 10.1002/pst.2465.

ABSTRACT

A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1. In stage 2, participants are re-randomized to either dose level depending on their initial treatment and a dichotomous response. A Bayesian analytic approach borrowing information from both stages was proposed and shown to improve the efficiency of estimation. In this paper, we propose two sample size determination (SSD) methods for the proposed snSMART comparing two dose levels with placebo. Both methods adopt the average coverage criterion (ACC) approach. In the first approach, the sample size is calculated in one step, taking advantage of the explicit posterior variance of the treatment effect. In the other two step approach, we update the sample size needed for a single-stage parallel design with a proposed adjustment factor (AF). Through simulations, we demonstrate that the required sample sizes calculated using the two SSD approaches both provide the desired power. We also provide an applet to allow for convenient and fast sample size calculation in this snSMART setting.

PMID:39846136 | DOI:10.1002/pst.2465

BJU Int. 2025 Jan 23. doi: 10.1111/bju.16662. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess 30- and 90-day postoperative complication rates in patients who underwent robot-assisted radical cystectomy (RARC) after receiving novel immunotherapy-based neoadjuvant treatment.

METHODS: A bi-centre analysis was conducted in patients who underwent RARC with intracorporeal urinary diversion and who received an immunotherapy-based neoadjuvant regimen between 2017 and 2023. Complications were classified using the Clavien-Dindo system.

RESULTS: The cohort included 136 patients, with a median (interquartile range [IQR]) age of 66 (61-73) years, of whom 22 were female (16.2%). The overall 30-day and 31-90-day Clavien-Dindo grade ≥3a complication rates were 15.4%, and 14.7%, respectively. The most common cumulative 90-day complications by category were infectious (59.6%), genitourinary (33.1%), and gastrointestinal (22.7%). The median (IQR) hospital stay was 11 (7-16) days, and 36 patients (26.5%) required readmission. Eighty-four patients received monotherapy with an immune checkpoint inhibitor and 52 received combination immunochemotherapy. A higher rate of 30-day infectious complications was seen in the immuno-monotherapy group (46.4% vs 26.9%; P = 0.03), while pulmonary complications were more commonly reported in the combination immunochemotherapy group (9.6% vs 1.2%; P = 0.03). No statistically significant differences were found in the other complication categories between the groups. Eleven patients (8.1%) experienced 13 (9.6%) immune-related adverse events (irAEs). The most common irAEs were hypothyroidism and dermatitis.

CONCLUSIONS: The cumulative 90-day complication rate after novel immunotherapy-based neoadjuvant treatment appears higher than those previously reported for RARC alone or for chemotherapy-based neoadjuvant regimens. We observed irAEs in 8.1% of patients after RARC, highlighting the need for urologists to recognise such events.

PMID:39846128 | DOI:10.1111/bju.16662

Epidemiol Psychiatr Sci. 2025 Jan 23;34:e6. doi: 10.1017/S2045796024000842.

ABSTRACT

AIMS: Although individuals with lower socio-economic position (SEP) have a higher prevalence of mental health problems than others, there is no conclusive evidence on whether mental healthcare (MHC) is provided equitably. We investigated inequalities in MHC use among adults in Stockholm County (Sweden), and whether inequalities were moderated by self-reported psychological distress.

METHODS: MHC use was examined in 31,433 individuals aged 18-64 years over a 6-month follow-up period, after responding to the General Health Questionnaire-12 (GHQ-12) in 2014 or the Kessler Six (K6) in 2021. Information on their MHC use and SEP indicators, education, and household income, were sourced from administrative registries. Logistic and negative binomial regression analyses were used to estimate inequalities in gained MHC access and frequency of outpatient visits, with psychological distress as a moderating variable.

RESULTS: Individuals with lower education or income levels were more likely to gain access to MHC than those with high SEP, irrespective of distress levels. Education-related differences in gained MHC access diminished with increasing distress, from a 74% higher likelihood when reporting no distress (odds ratio, OR = 1.74 [95% confidence interval, 95% CI: 1.43-2.12]) to 30% when reporting severe distress (OR = 1.30 [0.98-1.72]). Comparable results were found for secondary care but not primary care i.e., lower education predicted reduced access to primary care in moderate-to-severe distress groups (e.g., OR = 0.63 [0.45-0.90]), and for physical but not digital services. Income-related differences in gained MHC access remained stable or increased with distress, especially for secondary care and physical services.

CONCLUSIONS: Overall, individuals with lower education and income used MHC services more than their counterparts with higher socio-economic status; however, low-educated individuals faced inequities in primary care and underutilized non-physician services such as visits to psychologists.

PMID:39846121 | DOI:10.1017/S2045796024000842

Afr J Prim Health Care Fam Med. 2024 Dec 18;16(1):e1-e8. doi: 10.4102/phcfm.v16i1.4654.

ABSTRACT

BACKGROUND: For continuity and quality of care, accurate record-keeping is crucial. Complete care is facilitated by completing a child’s Road to Health Booklet (RTHB) as well as prompt interpretation and appropriate action. This could result in a decrease in child morbidity and mortality.

AIM: The study was aimed at assessing the completeness of the RTHB of children younger than 5 years.

SETTING: Temba Community Health Centre (CHC), Tshwane District, South Africa.

METHODS: A cross-sectional study was conducted using a data collection sheet adopted from previous studies.

RESULTS: Children less than 1-year-old accounted for 70.2% of the 255 RTHBs. The mean ± s.d. age was 11.5 ±10.76 months. The study finding showed no section was 100% fully completed. Of the 255 records studied, 38 (14.9%) human immunodeficiency virus (HIV)-exposed babies were recorded at birth, 39.5% were negative at 6 weeks and 60.5% were not recorded. Ninety-one (35.7%) children were unexposed. The HIV status of 126 (49.4%) children was not recorded. Sixty-six per cent (66%) of recorded maternal syphilis was negative. Immunisations, weight-for-age, neonatal information, and details of the family and child were fully completed in 80% of the booklets. Developmental screening was 17.2% completed, and oral health was 1.6% partially completed. The overall completeness was 40.3%.

CONCLUSION: The completeness of RTHBs was found to be suboptimal.Contribution: The present study’s findings should serve as a reminder that healthcare practitioners must complete RTHBs in their totality in order to improve continuity and care quality, as the results indicated that RTHB completion was below ideal.

PMID:39846111 | DOI:10.4102/phcfm.v16i1.4654