Average Sample Size Research Articles

Non-invasive markers of inflammation in alcohol-associated liver disease: A scoping review.

Clinical manifestations of liver inflammation in alcohol-associated liver disease (ALD) can range from asymptomatic to severe alcoholic hepatitis. While biopsy is the gold standard for identifying liver inflammation, it is an invasive procedure with risks of bleeding, visceral damage, and infection. We aim to establish the state of the current literature on non-invasive markers of inflammation in ALD. We searched Ovid MEDLINE, Embase, and the Cochrane Library for original studies on the association between one or more non-invasive biomarker(s) and histological inflammation or hepatitis in ALD patients. Exclusion criteria were lack of histological data, abstract only, non-English-language articles, and animal studies. Two independent reviewers screened abstracts, reviewed full texts, and extracted data from included papers. Our search identified 8051 unique studies. Title and abstract screening resulted in 563 studies, and full-text screening resulted in 31 studies for final inclusion. The majority were single-center observational cohorts with an average sample size of 124. Review of these studies identified 44 unique biomarkers and 8 calculated scores associated with histological inflammation and/or hepatitis, in addition to a metabolomic panel of 468 metabolites. Six studies examined diagnostic accuracy for histological inflammation and/or hepatitis. The highest area under the receiver operating characteristic curve was 0.932 using a model based on four metabolites. This review highlights the available literature on non-invasive markers of inflammation in ALD. There is a dearth of studies that evaluate the diagnostic accuracy of these biomarkers, and larger studies are needed to confirm findings identified in small cohorts.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called , in the presence of redundant random effects. We show that the derived formulae, and , adhere to empirical values via numerical demonstration and that ( indicating either or ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of is illustrated with a textbook example dataset.

Leveraging Technology to Diagnose Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Background: About 50 million people worldwide suffered from dementia in 2018—two-thirds of those with Alzheimer’s disease (AD). By 2050, this number is expected to rise to 152 million—which is slightly larger than the country of Russia. About 90% of these people are over the age of 65, but early-onset dementia can occur at younger ages. Early detection is imperative to expedient treatment, which can improve outcomes over the span of diagnosis. Objectives: To conduct a meta-analysis of similar studies along with a systematic literature review to hasten the development of clinical practice guidelines to assist clinicians in the diagnosis of AD. We analyzed data points in each article published over the last 10 years to meet this objective: cost, efficiency, accuracy, acceptability (by physician and patient), patient satisfaction, and barriers to adoption. Methods: Four research databases were queried (PubMed, CINAHL Ultimate, Web of Science, and ScienceDirect). The review was conducted in accordance with a published protocol, the Kruse Protocol, and reported in accordance with PRISMA (2020). Results: Ten interventions were identified to help diagnose AD among older patients, and some involved a combination of methods (such as MRI and PET). The average sample size was 320.32 (SD = 437.51). These 10 interventions were identified as accurate, non-invasive, non-stressful, inexpensive, convenient, and rapid. Only one intervention was identified as statistically ineffective, and this same intervention was used effectively in other studies. The barriers identified were cost, training, expense of travel, and required physical presence of patient. The weighted average sensitivity was 85.16%, specificity was 88.53, and the weighted average effect size was 0.7339 (medium). Conclusion: Innovation can accurately diagnose AD, but not all methods are successful. Providers must ensure they have the proper training and familiarity with these interventions to ensure accuracy in diagnosis. While the physical presence of the patient is often required, many interventions are non-invasive, non-stressful, and relatively inexpensive.

Second term improvement to generalised linear mixed model asymptotics

Summary A recent article on generalised linear mixed model asymptotics, Jiang et al. (2022), derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If m denotes the number of groups and n is the average within-group sample size then the asymptotic variances have orders m − 1 and (mn)−1, depending on the parameter. We extend this theory to provide explicit forms of the (mn)−1 second terms of the asymptotically harder-to-estimate parameters. Improved accuracy of statistical inference and planning are consequences of our theory.

Design of a clinical trial using generalized pairwise comparisons to test a less intensive treatment regimen.

Showing "similar efficacy" of a less intensive treatment typically requires a non-inferiority trial. Yet such trials may be challenging to design and conduct. In acute promyelocytic leukemia, great progress has been achieved with the introduction of targeted therapies, but toxicity remains a major clinical issue. There is a pressing need to show the favorable benefit/risk of less intensive treatment regimens. We designed a clinical trial that uses generalized pairwise comparisons of five prioritized outcomes (alive and event-free at 2 years, grade 3/4 documented infections, differentiation syndrome, hepatotoxicity, and neuropathy) to confirm a favorable benefit/risk of a less intensive treatment regimen. We conducted simulations based on historical data and assumptions about the differences expected between the standard of care and the less intensive treatment regimen to calculate the sample size required to have high power to show a positive Net Treatment Benefit in favor of the less intensive treatment regimen. Across 10,000 simulations, average sample sizes of 260 to 300 patients are required for a trial using generalized pairwise comparisons to detect typical Net Treatment Benefits of 0.19 (interquartile range 0.14-0.23 for a sample size of 280). The Net Treatment Benefit is interpreted as a difference between the probability of doing better on the less intensive treatment regimen than on the standard of care, minus the probability of the opposite situation. A Net Treatment Benefit of 0.19 translates to a number needed to treat of about 5.3 patients (1/0.19 ≃ 5.3). Generalized pairwise comparisons allow for simultaneous assessment of efficacy and safety, with priority given to the former. The sample size required would be of the order of 300 patients, as compared with more than 700 patients for a non-inferiority trial using a margin of 4% against the less intensive treatment regimen for the absolute difference in event-free survival at 2 years, as considered here.

Design of generalized fuzzy multiple deferred state sampling plan for attributes

In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan’s characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings.

The Relationship Between the Menstrual Cycle, Oral Contraceptives, and Executive Function – Inhibition, Updating, and Shifting

Abstract: Previous research suggests a link between oral contraceptives and cognitive functioning in women, yet the results are contradictory and limited by methodological inconsistencies. This is the first meta-analysis of studies comparing naturally cycling women with women taking oral contraceptives on measures testing three executive functions: inhibition, updating, and shifting. We conducted a systematic literature search. Sixteen articles were included which were either cross-sectional or experimental and compared executive functions between women taking oral contraceptives ( n = 588) or cycling naturally ( n = 594). The average sample size was n = 32.33 for oral contraceptives users and n = 31.34 for naturally cycling women with ranges going from 8 to 144 participants per study. The age range of participants in all the studies taken together was between 18 for the youngest participant and 50 years old for the oldest participant with a mean age of M = 21.97, SD = 2.28. The studies presented a mixture of androgenic and anti-androgenic oral contraceptives which were rarely analyzed as separate groups. We ran a multivariate meta-analysis model to estimate the effect size of 66 comparisons in executive functioning between the groups taking oral contraceptives and the groups of naturally cycling women. Overall, the effect size of differences in executive functioning between groups was not significant: d = 0.044, SE = 0.0713, 95% CI [−0.0959, 0.1839], z = 0.62; p = 0.54. The analysis of the cycle phases and types of executive functions as moderators was not significant, however, the studies assessed as having a lower quality increased the overall effect. Our analysis indicates no difference between oral contraceptive users and naturally cycling women on core executive functions but the high amount of heterogeneity might reflect a high level of methodological diversity. Implications for research design and methodology are discussed.

Learning Style Will Affect Learning Effectiveness

In this study, the author compared two types of learning styles based on the same course: online synchronous learning and face-to-face synchronous learning. Researchers are monitoring a semester of education. The overall course GPA, test, activity, and final examination scores are measured and compared. This experiment contained a relatively average sample size of 400 students from four different cultures. This study aims to understand how students from different cultures learn and how the learning style (online and face-to-face) will affect how students perform in their academics. In the final conclusion, it can be proved that different learning styles do affect the effectiveness of students' learning.

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

Hierarchical forecasting models of stink bug population dynamics for pest management

In recent decades, the intensification of agricultural production, accompanied by an increasing pressure from pests in various crops, has resulted in a substantial increase in the use of synthetic pesticides. Integrated pest management (IPM) provides a framework for the development and use of sustainable control strategies, which include the monitoring of the crop pest complex and the use of decision tools such as predictive models. In this study, an empirical modeling approach based on a hierarchical Bayesian model with a state-space structure was developed to perform stink bug (Pentatomidae) density forecasts to assist in deciding when to carry out pest control interventions, thus increasing the efficacy and efficiency of IPM. Using stink bug abundance and crop phenology data, along with meteorological data from eight different sites in Argentina, we made 1-week forecasts of population density, evaluated the predictive capacity of different models using Leave-One-Out-Cross-Validation, and analyzed how the uncertainty in the predictions vary as a function of the number of vertical beat sheet samples. The forecasts made with our best model showed a reasonable degree of accuracy. We found that i) the observation error of the vertical beat sheet method was much larger than expected, and ii) the uncertainty analysis suggested a sample size of 40 to obtain a good balance between precision and sampling effort, which is in stark contrast to the average sample size usually taken by advisors. Our approach provides advisors with a tool to make better-informed decisions about when and if to carry out pest control interventions.

Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect. We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice. We demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis. Optimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.

First Asia-Pacific experience of trans-bronchial core biopsy with a Franseen needle.

Endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) is the standard for evaluating mediastinal and hilar lesions. EBUS-TBNA is limited by small volume of material obtained for immunohistochemistry (IHC) and ancillary studies important for oncological therapies. The Franseen AcquireTM needle is designed for EBUS-transbronchial needle core biopsy (TBNB) allowing larger core sizes with evidence in gastroenterology literature but little in pulmonology. This study reports the first Asia-Pacific experience of EBUS-TBNB and adequacy of samples for diagnosis and ancillary studies. A retrospective cohort study of EBUS-TBNB at the Royal Adelaide Hospital was conducted between December 2019 and May 2021. Diagnostic rate, adequacy for ancillary studies and complications were evaluated. Samples were flushed into formalin for histological processing with no rapid on-site cytological evaluation (ROSE). For suspected lymphoma, samples were flushed into HANKS for flow cytometry. Cases performed with the Olympus VizishotTM during the same 18-month were similarly analysed. One hundred and eighty-nine patients were sampled with the AcquireTM needle. Diagnostic rate was 174/189 (92.1%). Where reported [146/189 (77.2%)], average core aggregate sample size was 13.4 mm × 10.7 mm × 1.7 mm. For non-small cell lung cancer (NSCLC) cases, 45/49 (91.8%) had adequate tissue for programmed cell death-ligand 1 (PD-L1). 32/35 (91.4%) adenocarcinoma cases had sufficient tissue for ancillary studies. There was one false negative malignant lymph node at the first AcquireTM procedure. There were no major complications. One hundred and one patients were sampled with the VizishotTM needle. Diagnostic rate was 86/101 (85.1%) with only 25/101 (24.8%) having reported tissue cores (P<0.0001 of VizishotTM) with the remaining samples processed via cell block. AcquireTM EBUS-TBNB diagnostic rate is comparable to historical data with >90% of cases having sufficient core material for ancillary studies. There appears to be a role for the AcquireTM alongside the standard of care for the work up of lymphadenopathy and particularly for lung cancer.

Age estimation by evaluation of obliteration of the palatine sutures: a scoping review.

The age estimation (AE) of human remains is a challenging task since it is dependent on the state in which these remains are found. Since the macroscopic evaluation of palatal sutures has been proposed as a method for AE, the aim of this study was to review the literature on this method, considering that the cases of edentulous elderly are among the greatest challenges in anthropological and forensic contexts. A scoping review was performed using a specific search strategy in PubMed, Web of Science, SciELO, LILACS, and Google Scholar. The search identified 13 articles, among which the USA yielded the most information with 3 articles. Only 1 study was identified in Latin America (Peru). There was great diversity regarding the origin of samples, and the studies were carried out on both historical and modern populations. Only 6 articles exceeded the average sample size (168.08) and 4 articles studied samples of fewer than 100 individuals. Although 6 different methods were identified, Mann et al.'s revised method was the most used. The selection of appropriate methods for AE depends on what skeletal elements are present and the general age of the specimens. Although evaluation of the obliteration of the palatal sutures has been found to be simple and promising for AE in individuals over 60years of age, this method has been reported to have less precision than other more complex methods, which makes the use of a combination of methods necessary to increase the level of confidence and the percentage of success. Further research could resolve this weakness, and methodological refinement (perhaps the digitization and automation of processes, or the application of Bayesian methodology) could provide the necessary solidity to comply with international standards in the forensic scenario.

Electrochemical sensor for determination of butylated hydroxyanisole in real samples using glassy carbon electrode modified by [Co(HL)2Cl2] nano-complex.

A new mononuclear Co(II) complex with the formula [Co(HL)2Cl2] (1) (HL= N-(2-hydroxy-1-naphthylidene)-2-methyl aniline) has been synthesized and characterized by Fourier transform infrared spectroscopy, UV-Vis, elemental analysis and single crystal X-ray structure analysis. Single crystals of the complex [Co(HL)2Cl2] (1) were obtained through slow evaporation of an acetonitrile solution at room temperature. The crystal structure analysis revealed that the two Schiff base ligands create a tetrahedral geometry via oxygen atoms and two chloride atoms. The nano-size of [Co(HL)2Cl2] (2) have been synthesized by the sonochemical process. Characterization of nanoparticles (2) was carried out via X-ray powder diffraction (XRD), scanning electron microscopy (SEM), UV-Vis, and FT-IR spectroscopy. The average sample size synthesized via the sonochemical method was approximately 56 nm. In this work, a simple sensor based on a glassy carbon electrode modified with [Co(HL)2Cl2] nano-complex was developed ([Co(HL)2Cl2] nano-complex/GCE) for convenient and fast electrochemical detection of butylated hydroxyanisole (BHA). The modified electrode offers considerably improved voltammetric sensitivity toward BHA compared to the bare electrode. Applying linear differential pulse voltammetry, a good linear relationship of the oxidation peak current with respect to concentrations of BHA across the range of 0.5-150 μM and a detection limit of 0.12 μM was achieved. The [Co(HL)2Cl2] nano-complex/GCE sensor was applied to the determination of BHA in real samples successfully.

Weak range-wide population structure in the blackfin tuna (Thunnus atlanticus) revealed by analysis of genome-wide SNPs

Abstract Blackfin tuna (Thunnus atlanticus) is a small tuna distributed in the western Atlantic Ocean where it is exploited by growing recreational and commercial regional fisheries. In this work, genome-wide genetic variation was analysed to investigate the occurrence of stock subdivision. A de novo assembly of the blackfin tuna genome was generated using Illumina paired-end sequencing data and applied as a reference for population genomic analysis of specimens from nine localities (average sample size per locality n = 72) spanning most of the blackfin tuna distribution range. A total of 2139 single-nucleotide polymorphisms were discovered and genotyped using the double-digest restriction associated DNA sequencing. Pairwise exact homogeneity tests were significant in 24 out of 36 population pairs and significant spatial autocorrelation of genotypes was observed for specimens collected within 2250 km of each other. However, divergence among locality samples was very low (pairwise FST range 0.0002–0.0025) and significant temporal variations were detected in localities sampled multiple times. Approaches to detect cryptic groups de novo were unsuccessful. Additional sampling is warranted to determine if multiple stocks need to be defined for management and assess temporal and spatial patterns of gene flow connecting them.

Bayesian single-to-double arm transition design using both short-term and long-term endpoints.

The choice between single-arm designs versus randomized double-arm designs has been contentiously debated in the literature of phase II oncology trials. Recently, as a compromise, the single-to-double arm transition design was proposed, combining the two designs into one trial over two stages. Successful implementation of the two-stage transition design requires a suspension period at the end of the first stage to collect the response data of the already enrolled patients. When the evaluation of the primary efficacy endpoint is overly long, the between-stage suspension period may unfavorably prolong the trial duration and cause a delay in treating future eligible patients. To accelerate the trial, we propose a Bayesian single-to-double arm design with short-term endpoints (BSDS), where an intermediate short-term endpoint is used for making early termination decisions at the end of the single-arm stage, followed by an evaluation of the long-term endpoint at the end of the subsequent double-arm stage. Bayesian posterior probabilities are used as the primary decision-making tool at the end of the trial. Design calibration steps are proposed for this Bayesian monitoring process to control the frequentist operating characteristics and minimize the expected sample size. Extensive simulation studies have demonstrated that our design has comparable power and average sample size but a much shorter trial duration than conventional single-to-double arm design. Applications of the design are illustrated using two phase II oncology trials with binary endpoints.

Estimation of population parameters using sample extremes from nonconstant sample sizes.

We examine the accuracy and precision of parameter estimates for both the exponential and normal distributions when using only a collection of sample extremes. That is, we consider a collection of random variables, where each of the random variables is either the minimum or maximum of a sample of nj independent, identically distributed random variables drawn from a normal or exponential distribution with unknown parameters. Previous work derived estimators for the population parameters assuming the nj sample sizes are constant. Since sample sizes are often not constant in applications, we derive new unbiased estimators that take into account the varying sample sizes. We also perform simulations to assess how the previously derived estimators perform when the constant sample size is simply replaced with the average sample size. We explore how varying the mean, standard deviation, and probability distribution of the sample sizes affects the estimation error. Overall, our results demonstrate that using the average sample size in place of the constant sample size still results in reliable estimates for the population parameters, especially when the average sample size is large. Our estimation framework is applied to a biological example involving plant pollination.

Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: A Monte Carlo simulation study

BackgroundAdvantages of meta-analysis depend on the assumptions underlying the statistical procedures used being met. One of the main assumptions that is usually taken for granted is the normality underlying the population of true effects in a random-effects model, even though the available evidence suggests that this assumption is often not met. This paper examines how 21 frequentist and 24 Bayesian methods, including several novel procedures, for computing a point estimate of the heterogeneity parameter ({tau }^{2}) perform when the distribution of random effects departs from normality compared to normal scenarios in meta-analysis of standardized mean differences.MethodsA Monte Carlo simulation was carried out using the R software, generating data for meta-analyses using the standardized mean difference. The simulation factors were the number and average sample size of primary studies, the amount of heterogeneity, as well as the shape of the random-effects distribution. The point estimators were compared in terms of absolute bias and variance, although results regarding mean squared error were also discussed.ResultsAlthough not all the estimators were affected to the same extent, there was a general tendency to obtain lower and more variable {tau }^{2} estimates as the random-effects distribution departed from normality. However, the estimators ranking in terms of their absolute bias and variance did not change: Those estimators that obtained lower bias also showed greater variance. Finally, a large number and sample size of primary studies acted as a bias-protective factor against a lack of normality for several procedures, whereas only a high number of studies was a variance-protective factor for most of the estimators analyzed.ConclusionsAlthough the estimation and inference of the combined effect have proven to be sufficiently robust, our work highlights the role that the deviation from normality may be playing in the meta-analytic conclusions from the simulation results and the numerical examples included in this work. With the aim to exercise caution in the interpretation of the results obtained from random-effects models, the tau2() R function is made available for obtaining the range of {tau }^{2} values computed from the 45 estimators analyzed in this work, as well as to assess how the pooled effect, its confidence and prediction intervals vary according to the estimator chosen.

Application of Wearable Technologies in Fall Risk Assessment and Improvement in Patients with Peripheral Neuropathy: A Systematic Review

Background. Peripheral neuropathy is regarded as one of the leading causes of fatal and nonfatal falls. Wearable sensors, due to their increasing availability and flexibility of setting and space, are used widely to obtain wearer’s kinematic data to analyze one’s balance capacities for the evaluation of risk of fall. There is yet to have a review study focusing on the application of wearable sensors in the scope of fall risk for patients with peripheral neuropathy. Objective. To investigate the methods by which researchers adopt to assess risk of fall in peripheral neuropathy patients and potentially shed light on future researches. Methods. A systematic review design was used to identify articles on fall risk assessment and balance training using wearable sensors in patients with peripheral neuropathy. The study is aimed at extracting the following information: the type of sensors, the type of signal and data processing employed, the scales and tests used in the study, and the type of application. Results. We identified 351 studies, from which 8 were included. An average sample size of 35.6 patients enrolled the studies. The accelerometer was the most common wearable sensor used. 10‐meter walk test was the preferable procedure for assessing risk of fall. Conclusion. This review examined several key components in studies on assessing and improving the risk of fall using wearable sensors. We identified the preferred functional test (10‐meter walk test), sensor technology (accelerometer), locations (torso and lower legs), and fall risk improvement methods (prostheses). However, due to the limited number of articles specializing in this field of research, a consensus on patient sample size and procedures is not reached. We would recommend future researches to examine more parameters and adopt a fusion sensor setup.

The use of natural language processing in palliative care research: A scoping review

Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.