Moderate Sample Sizes Research Articles

Near Infrared Spectroscopy (NIRS) Model Based on Transfer Learning

This study compares three transfer learning strategies for adapting convolutional neural network (CNN) models in near-infrared spectroscopy to new instruments. Strategy 1 freezes pre-trained weights except for the output layer; Strategy 2 fine-tunes all weights with new data; Strategy 3 adds new fully connected layers to the pre-trained model. Using tablet spectra to predict active pharmaceutical ingredient content, results show that fine-tuning all network weights (Strategy 2) yields the best performance. The study also finds that moderate training sample sizes balance model accuracy and resource expenditure. Transfer learning is effective for near-infrared model transfer.

A seamless Phase I/II platform design with a time-to-event efficacy endpoint for potential COVID-19 therapies.

In the search for effective treatments for COVID-19, the initial emphasis has been on re-purposed treatments. To maximize the chances of finding successful treatments, novel treatments that have been developed for this disease in particular, are needed. In this article, we describe and evaluate the statistical design of the AGILE platform, an adaptive randomized seamless Phase I/II trial platform that seeks to quickly establish a safe range of doses and investigates treatments for potential efficacy. The bespoke Bayesian design (i) utilizes randomization during dose-finding, (ii) shares control arm information across the platform, and (iii) uses a time-to-event endpoint with a formal testing structure and error control for evaluation of potential efficacy. Both single-agent and combination treatments are considered. We find that the design can identify potential treatments that are safe and efficacious reliably with small to moderate sample sizes.

Likelihood adaptively incorporated external aggregate information with uncertainty for survival data.

Population-based cancer registry databases are critical resources to bridge the information gap that results from a lack of sufficient statistical power from primary cohort data with small to moderate sample size. Although comprehensive data associated with tumor biomarkers often remain either unavailable or inconsistently measured in these registry databases, aggregate survival information sourced from these repositories has been well documented and publicly accessible. An appealing option is to integrate the aggregate survival information from the registry data with the primary cohort to enhance the evaluation of treatment impacts or prediction of survival outcomes across distinct tumor subtypes. Nevertheless, for rare types of cancer, even the sample sizes of cancer registries remain modest. The variability linked to the aggregated statistics could be non-negligible compared with the sample variation of the primary cohort. In response, we propose an externally informed likelihood approach, which facilitates the linkage between the primary cohort and external aggregate data, with consideration of the variation from aggregate information. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. Through the application of our proposed method, we integrate data from the cohort of inflammatory breast cancer (IBC) patients at the University of Texas MD Anderson Cancer Center with aggregate survival data from the National Cancer Data Base, enabling us to appraise the effect of tri-modality treatment on survival across various tumor subtypes of IBC.

Firth-Type Penalized Methods of the Modified Poisson and Least-Squares Regression Analyses for Binary Outcomes.

The modified Poisson and least-squares regression analyses for binary outcomes have been widely used as effective multivariable analysis methods to provide risk ratio and risk difference estimates in clinical and epidemiological studies. However, there is no certain evidence that assessed their operating characteristics under small and sparse data settings and no effective methods have been proposed for these regression analyses to address this issue. In this article, we show that the modified Poisson regression provides seriously biased estimates under small and sparse data settings. In addition, the modified least-squares regression provides unbiased estimates under these settings. We further show that the ordinary robust variance estimators for both of the methods have certain biases under situations that involve small or moderate sample sizes. To address these issues, we propose the Firth-type penalized methods for the modified Poisson and least-squares regressions. The adjustment methods lead to a more accurate and stable risk ratio estimator under small and sparse data settings, although the risk difference estimator is not invariant. In addition, to improve the inferences of the effect measures, we provide an improved robust variance estimator for these regression analyses. We conducted extensive simulation studies to assess the performances of the proposed methods under real-world conditions and found that the accuracies of the point and interval estimations were markedly improved by the proposed methods. We illustrate the effectiveness of these methods by applying them to a clinical study of epilepsy.

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.

The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

Identification and estimation of entry games under symmetry of unobservables

Abstract This paper provides a new point identification and estimation method for two-player entry games with complete information, based on symmetry of unobservables. Neither equilibrium selection nor parametric distributional assumptions are required. In addition, a weaker support condition is used relative to the existing literature. Unlike other semiparametric estimators, the estimator proposed here is $\sqrt{n}$-consistent. A test of the required symmetry condition is provided. Monte Carlo evidence shows that the estimator performs well with moderate sample sizes, and is robust to unimodal and multimodal error distributions. The estimator is applied to an entry game of discount retailers in Jia (2008). Our results suggest that researchers should apply the normality assumption with caution.

Local Bootstrap for Network Data

SUMMARY In network analysis, we frequently need to conduct inference for network parameters based on one observed network. Since the sampling distribution of the statistic is often unknown, we need to rely on the bootstrap. However, due to the complex dependence structure among vertices, existing bootstrap methods often yield unsatisfactory performance, especially under small or moderate sample sizes. To this end, we propose a new network bootstrap procedure, termed local bootstrap, to estimate the standard errors of network statistics. We propose to resample the observed vertices along with their neighbor sets, and reconstruct the edges between the resampled vertices by drawing from the set of edges connecting their neighbor sets. We justify the proposed method theoretically with desirable asymptotic properties for statistics such as motif density, and demonstrate its excellent numerical performance in small and moderate sample sizes. Our method includes several existing methods, such as the empirical graphon bootstrap, as special cases. We investigate the advantages of the proposed methods over the existing methods through the lens of edge randomness, vertex heterogeneity, neighbor set size, which shed some light on the complex issue of network bootstrapping.

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.

Concurrent Functional Linear Regression Via Plug-in Empirical Likelihood

AbstractFunctional data with non-smooth features (e.g., discontinuities in the functional mean and/or covariance) and monotonicity arise frequently in practice. This paper develops simultaneous inference for concurrent functional linear regression in this setting. We construct a simultaneous confidence band for a functional covariate effect of interest. Along with a Wald-type formulation, our approach is based on a powerful nonparametric likelihood ratio method. Our procedures are flexible enough to allow discontinuities in the coefficient functions and the covariance structure, while accounting for discretization of the observed trajectories under a fixed dense design. A simulation study shows that the proposed likelihood ratio-based procedure outperforms the Wald-type procedure in moderate sample sizes. We apply the proposed methods to studying the effect of age on the occupation time curve derived from wearable device data obtained in an NHANES study.

TROPHY-U-01 Cohort 2: A Phase II Study of Sacituzumab Govitecan in Cisplatin-Ineligible Patients With Metastatic Urothelial Cancer Progressing After Previous Checkpoint Inhibitor Therapy.

Sacituzumab govitecan (SG) is a Trop-2-directed antibody-drug conjugate with an SN-38 payload, approved for patients with locally advanced (LA) or metastatic urothelial cancer (mUC) who progressed after platinum (PT)-based chemotherapy and a checkpoint inhibitor (CPI). Here, we report results from Cohort 2 of TROPHY-U-01 trial, evaluating the efficacy and safety of SG in patients with mUC. TROPHY-U-01 (ClinicalTrials.gov identifier: NCT03547973) is a multicohort, open-label phase II study. Cohort 2 includes patients with LA or mUC who have had progression or recurrence after a CPI and were cisplatin-ineligible at study initiation. Patients received SG 10 mg/kg on days 1 and 8 of 21-day cycles. The primary end point was objective response rate (ORR) per central review; secondary end points were clinical benefit rate (CBR), duration of response (DOR), and progression-free survival (PFS) per central review and safety. Cohort 2 included 38 patients (61% male; median age 72.5 years; 66% visceral metastases [29% liver]; 50% received previous PT-based chemotherapy as previous [neo]adjuvant therapy]). At a median follow-up of 9.3 months, ORR was 32% (95% CI, 17.5 to 48.7), CBR 42% (95% CI, 26.3 to 59.2), median DOR 5.6 months (95% CI, 2.8 to 13.3), median PFS 5.6 months (95% CI, 4.1 to 8.3), and median overall survival 13.5 months (95% CI, 7.6 to 15.6). Grade ≥3 treatment-emergent adverse events occurred in 87% of patients, most commonly neutropenia (34%), anemia (24%), leukopenia (19%), fatigue (18%), and diarrhea (16%). SG monotherapy demonstrated a relatively high ORR with rapid responses; this was feasible with a manageable toxicity profile in cisplatin-ineligible patients who had progression after CPI therapy. Limitations include a moderate sample size and lack of random assignment. These results warrant further evaluation of SG alone and in combinations in patients with LA/mUC.

Peripartum lithium management: Early maternal and neonatal outcomes

BackgroundIt has been suggested that a 30–50 % lithium dose reduction or lithium discontinuation 24-48 h before delivery could minimize neonatal complications. We investigated the maternal lithemia changes around delivery after a brief discontinuation, the placental transfer of lithium at delivery, and the association between neonatal lithemia at delivery and acute neonatal outcomes. MethodsA retrospective observational cohort study was conducted in a teaching hospital (November/2006-December/2018). Data was extracted from the medical records. We included psychopathologically stable women, with a singleton pregnancy, treated with lithium in late pregnancy, with at least one maternal and neonatal lithemia at delivery. Lithium was discontinued 12 h before a scheduled caesarean section or induction, or at admission day to hospital birth; and restarted 6-12 h post. ResultsSixty-six mother-infant pairs were included, and 226 maternal and 66 neonatal lithemias were obtained. We found slight maternal lithemia fluctuations close to 0.20 mEq/L, and early postpartum relapse of 6 %. The mean (SD) umbilical cord/mother intrapartum lithemia ratio was 1.10 (0.17). Fifty-six percent of neonates presented transient acute complications. Neonatal hypotonia was the most frequent outcome (N = 15). Mean lithemia were 0.178 mEq/L higher in those with hypotonia than in those without (p = 0.028). LimitationsIt is a retrospective cohort of a moderate sample size of healthy uncomplicated pregnancies and results cannot be generalized to all pregnant treated with lithium. ConclusionsLithium transfers completely across the placenta. A brief predelivery lithium discontinuation was associated with slight maternal lithemia fluctuations. Neonates exposed intrautero to lithium present frequent but transient acute effects.

STATISTICAL INFERENCE ON SINE-EXPONENTIAL DISTRIBUTION PARAMETER

The Sine-Exponential (Sine-E) distribution is a probability distribution that combines the periodic behavior of the sine function with the decay characteristic of the exponential function. This study addresses the problem of identifying the most accurate and reliable estimation method for the parameter of the Sine-E distribution. The objective is to evaluate various parameter estimation techniques, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), Weighted Least Squares Estimation (WLSE), Maximum Product of Spacing Estimation (MPSE), Cramer-von-Mises Estimation (CVME), and Anderson-Darling Estimation (ADE), using Mean Square Error (MSE) as the criterion for determining the technique with the minimum error. The study’s findings reveal that as sample size increases, the parameter estimates for all techniques converge to the true parameter value, with decreases in bias, MSE, and mean relative estimates. Among the techniques evaluated, the MPSE method consistently provides estimates closest to the true parameter value and exhibits the least bias and lowest MSE across small, moderate, and large sample sizes, making it the best estimator for the Sine-E distribution.

Liver Transplantation for Cholangiocarcinoma

AbstractCholangiocarcinomas (CCAs) are highly aggressive, primary liver cancers with rising incidence and mortality rates. The current 5-year overall survival is less than 20%. There are no standardized screening protocols, and current diagnostic methods include serum biomarkers and imaging techniques with suboptimal sensitivities and specificities. The most commonly used treatment options, including combination systemic therapies, locoregional therapies, and surgical resection, offer improving but nonetheless limited progression-free and overall survival. Liver transplantation has shown promising results as a potentially curative treatment for two types of CCA, namely, perihilar and intrahepatic. However, the evidence is largely from retrospective series of small to moderate sample sizes. There is a need to define optimal types and sequencing of neoadjuvant and adjuvant peritransplant therapies, as well as criteria for CCA patient transplant eligibility. Here, we conduct a granular review of the evidence available on every step of the transplant care pathway for perihilar and intrahepatic CCA patients. We aim to inform best practices to inform future avenues of research and maximize the number of patients eligible for this potentially life-prolonging therapy.

P-705 Telomere length of white blood cells is a non-invasive marker of reproductive aging in women

Abstract Study question Can leukocyte telomere length be a marker of ovarian decline? Summary answer The significant correlation found between leukocyte and cumulus cell telomere lengths supports the use of leukocyte telomere length (TL) as a marker of ovarian aging. What is known already The telomere-based theory of reproductive aging proposes that age-related ovarian dysfunction is a consequence of progressive telomere shortening from fetal oogenesis to the adult ovary, compromising genome stability and proper chromosome segregation. Telomere length in cumulus cells (CCTL) is known to be a biomarker of oocyte and embryo quality, but its assessment is not practical. Hence, we previously proposed leukocyte telomere length (LTL) as an alternative to directly measuring telomere length in follicular cells, but so far it has only been assessed in samples of moderate size and homogeneous populations without consistent results. Study design, size, duration This prospective, non-interventional cohort study involved 300 women aged 18 to 46 years undergoing controlled ovarian stimulation over a 24-month period. The cohort included 169 egg donors and 131 IVF patients. Oocyte retrieval was performed 36 hours after maturation induction, with cumulus cells collected during oocyte stripping and blood samples taken via peripheral venous access. Participants/materials, setting, methods Genomic DNA was isolated from leukocytes and cumulus cells of all subjects for TL measurements. Relative TL was determined using a SYBR-Green quantitative real-time PCR protocol. A Taqman assay for the multicopy gene Alu was used to normalize the measurements. A Pearson’s correlation test was used to analyze the correlation between CCTL and LTL, as well as between TL and age, antral follicle count, Anti-Müllerian hormone, and number of mature oocytes retrieved. Main results and the role of chance Mean age of the subjects was 30.70±7.61 years. Anti-Müllerian hormone (AMH) mean level was 2.60±1.87 ng/ml, antral follicle count (AFC) was 17.8±7.4 and the number of mature oocytes was 14.3±9.3. A significant correlation was observed between CCTL and LTL (R = 0.6, p-value=2.2E-16). Notably, the relationship between leukocyte and cumulus cell telomere lengths was stronger in the 97 participants over 35 years (R = 0.67, p-value=1.55E-13), especially those between 39 to 46 years (R = 0.70, p-value=1.19E-12). Telomere length was inversely correlated with age in both leukocytes and cumulus cells (R=-0.23, p-value=6.98E-05; R=-0.4, p-value=2.38E-05), meaning the older the subject, the shorter the telomeres in both cells. Furthermore, longer telomeres in leukocytes and cumulus cells were significantly correlated with higher AFC (R = 0.20, p-value=0.0006; R = 0.25, p-value=1.83E-05), and more mature oocytes retrieved (R = 0.16, p-value=0.0043; R = 0.27, p-value=2.83E-06). However, we did not find any association between AMH levels and LTL or CCTL (R = 0.07, p-value=0.2666; R = 0.07, p-value=0.2747). Limitations, reasons for caution The method used to measure the average telomere length from pooled leukocyte samples may not reflect individual cell variability. Additionally, it allows for relative but not absolute telomere length comparisons. Wider implications of the findings This study is the first to demonstrate in a large and heterogeneous population that LTL could be a viable marker for ovarian aging by reflecting follicular cell telomere length. This finding may provide a valuable tool for assessing and monitoring the reproductive health of IVF patients. Trial registration number Not applicable

Comparing of the Reliability for two-parameters Lomax distribution using Lindley method Estimation

Bayesian analysis of two-parameters Lomax distribution's reliability has been examined in this search using three prior functions [ Gamma, assumption &Inverted Levy] , the estimation was produced under squared errors loss function used idea of Lindley. The simulation study was carried out to assess the performance of the three alternative estimators of reliability function by 6 assumed experiments for different values of distributions parameters with small, moderate, and large sample sizes using mean squared error criteria. The best performance, according to the reported results showed that the best performance was for Inverted Levy prior with squared error loss function.

Inference for the Parameters of a Zero-Inflated Poisson Predictive Model

In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.

Holo-transcobalamin is not associated with the risk of all-cause mortality in the general population

Background & AimsHolo-Transcobalamin (holo-TC) is the biologically active form of vitamin B12, a vitamin essential in human metabolism. The association between vitamin B12 (total cobalamin) and mortality risk has been controversially reported, whereas the relation between holo-TC and survival is unknown. In a population-based sample (n=862, female share 42.8 %, median age 62.3 years), we related serum holo-TC to the risk of all-cause mortality. MethodsWe measured serum holo-TC by electro-chemiluminescence. Multivariable-adjusted Cox proportional hazards regression models were used to quantify the association between serum holo-TC and all-cause mortality. ResultsOver a median follow-up time of 10.9 years, n=99 individuals died. We did not find significant associations between serum holo-TC and the risk of all-cause mortality (HR: 1.00 [95% CI 0.97–1.03] per 5-point increment in holo-TC), neither in the overall sample, nor in subgroups stratified by sex, diabetes, or hypertension. ConclusionThe biologically active form of vitamin B12, holo-TC, is not related to the risk of all-cause mortality in a moderate-sized sample from the general population.

Nonparametric control limits incorporating exceedance probability criterion for statistical process monitoring with commonly employed small to moderate sample sizes

This article aims to enhance the effectiveness of Phase I in statistical process monitoring by integrating the assessment of estimation uncertainty into control charts using the exceedance probability criterion. This criterion guarantees the desired in-control performance that a practitioner will achieve with a predefined high nominal coverage probability, which can help prevent high false alarm rates from occurring. In pursuit of this objective, we introduce two nonparametric approaches: one based on an analytical method and the other on a bootstrapping technique. Both approaches exhibit superior performance compared to the existing nonparametric method, particularly for Phase I, where small to moderate sample sizes are common. These proposed methodologies are especially advantageous for practitioners in real-world production environments.

Strategic interventions for bridging the design-reality gap in property conferment rights: a case study of India’s ambitious initiative

PurposeThis research aims to assess how well the e-governance initiative aligns with its design expectations and on-ground realities for property management to redevelop Delhi, the mega-city of India. Additionally, the study proposes strategic interventions to address any gaps identified, aiming to improve the project’s effectiveness and success.Design/methodology/approachThe research employs a singular exploratory case study methodology to scrutinise the e-governance initiative undertaken by the Government of India to confer property rights. Specifically, the study utilises a qualitative research approach known as design-reality gap (DRG) analysis. The study draws from primary and secondary sources using a mixed-methods data collection strategy. Primary data are gathered through a purposeful and snowball sampling method involving direct engagement with respondents, whilst secondary data are sourced from the project portal.FindingsThe investigation reveals a substantial disparity between the planning and execution phases of e-governance projects. This incongruity predominantly manifests in the domains of process, staffing and skills, as well as objectives and values. The study further finds that strategic collaboration amongst stakeholders and the sustainability of decisions are the most critical factors in ensuring the success of e-governance initiatives and bridging the DRG of e-governance projects.Research limitations/implicationsThis research highlights the complex challenges faced by e-governance projects in technical, human and organisational aspects. The successful implementation and sustainability of these projects require effective strategies to overcome challenges, which management should proactively identify and act on. To improve services, beneficiaries should be encouraged to provide feedback and suggestions, as they play a crucial role in service enhancement. A dynamic feedback loop would be established by creating a two-way interaction between beneficiaries and service providers, leading to iterative service improvement. It is important to note that the study’s findings are more illustrative than conclusive due to the moderate sample size, reflecting its limitations.Originality/valueThe research contributes to the scholarly discourse on e-governance and policy implementation by comprehensively examining the alignment between conceptual design and real-world execution. It also introduces a normalised scale for the DRG framework, mapping its dimensions to deduce specific outcomes. This innovative approach enhances the research’s originality and value, offering insights applicable in both academic and practical domains.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.