Covariate Model Research Articles

Species Archetype Models of Kelp Forest Communities Reveal Diverse Responses to Environmental Gradients

Assessing ecosystem integrity by monitoring populations and communities is an important management tool, but is often limited by the immense variety of species and the rarity of many of them. Grouping species by their responses to variation in the environment is one approach to choosing species to serve as effective indicators of community change. Moreover, identifying species that are characterized by similar archetypical responses to the environment increases the power to predict their occurrence and simplifies management of diverse species assemblages by focusing on a much smaller number of archetypes. To this end, we used the species archetype model (SAM) to fit generalized linear models of environmental covariates to species distribution data in order to identify environmentally correlated groups of kelp forest species in the Santa Barbara Channel region. Eighty-two species of macroalgae, invertebrates, and fish monitored in kelp forests across the channel were grouped into one of 10 archetypes based on their similar responses to environmental parameters, with water temperature emerging as one of the strongest drivers of archetype differences. Predictive maps of the distribution of species archetypes identified sites where multiple archetypes are common, indicating high diversity, as well as sites where rare species are more likely to occur. Potential indicator species were identified for each archetype. New monitoring efforts across the growing Marine Biodiversity Observation Network could use modeling approaches like SAM to guide their designs, optimizing the cost-to-benefit ratio of monitoring whole communities.

BODY COMPOSITION AMONG UNIVERSITY FEMALE ATHLETES OF TEAM SPORTS

ABSTRACT Introduction: The assessment of body composition in female athletes of different sports is important for health monitoring. Objective: To compare body composition in university athletes of different team sports (indoor soccer, flag football and volleyball). Methods: A cross-sectional study carried out with 45 female athletes, aged 18 to 35 years (22.8 ± 3.55). The dependent variables were body fat mass (FM) and fat-free mass (FFM) measured by air displacement plethysmography. Bone mineral content, adjusted for height (BMC/height) and bone mineral density (BMD), were measured by dual energy X-ray absorptiometry. The independent variable was sport [flag football (n = 12); indoor soccer (n = 20); volleyball (n = 13)] and the covariates were age (complete years), training volume (minutes per week) and length of time playing the sport (complete years). Analysis of covariance was used. Results: Adjusting the model for covariates, volleyball athletes (19.27 kg ± 2.20) presented higher FM values compared to the flag football (16.00 kg ± 1.70) and indoor soccer players (12.20 kg ± 1.30). There was no significant difference in FFM, BMC/height and total BMD between sports, even after adjusting for covariates. Conclusion: Volleyball athletes presented higher FM compared to flag football and indoor soccer athletes. There were no differences in FFM, BMC/height and BMD among the players of the different team sports. This study can help coaches and other sports professionals to prevent injuries to athletes in sports such as higher FM (volleyball), or to prevent diseases such as menstrual irregularities, which are common in athletes who may have low levels of body fat (indoor soccer players), this being one of the risk factors for the female athlete triad (eating disorders, menstrual irregularities and low BMD). Level of evidence III; Retrospective comparative study .

Bayesian EWMA control chart with measurement error under different loss functions

AbstractThis paper presents the Bayesian EWMA control chart under two different loss functions (i) squared error loss function (SELF) and (ii) linex loss function (LLF) in the presence of measurement error (ME). We take posterior and posterior predictive distribution under the conjugate prior. We used a linear covariate model in the existence of ME to evaluate the control chart. We also studied the effects of multiple measurements and linear increasing variance methods in the existence of the ME. The average run length and standard deviation of run length are used to measure the performance of the Bayesian EWMA control chart with ME. We conducted the Monte Carlo simulation study to evaluate the performance of Bayesian EWMA control chart with ME. A real‐life data example is also presented to demonstrate the application of the control chart.

Fast screening of covariates in population models empowered by machine learning

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts.

Maximum hybrid exponentially weighted moving average control chart in the presence of measurement error by using auxiliary information

AbstractHybrid control charts have become part of statistical process control (SPC) but still, need more emphasis. Researchers are developing charts for joint monitoring of process mean and variance shifts just like Max‐EWMA and their hybrid version using auxiliary information but are ignoring the effect of measurement error on the efficiency of charts. We propose maximum hybrid exponentially weighted moving average with measurement error using auxiliary information and name it Max‐HEWMAMEAI control chart. The efficiency of this chart is proved through calculations of average run lengths () and standard deviations of run lengths (SDRLs) using the Monte Carlo simulations method whereas, and are shown in tabular form. The effect of measurement error on the efficiency of the chart has been analyzed and the impact of multiple measurements to reduce the error effect has been studied using the covariate model. Real‐life application is also part of this article to support the simulation results.

Environmentally triggered shifts in steelhead migration behavior and consequences for survival in the mid-Columbia River.

The majority of Columbia River summer-run steelhead encounter high river temperatures (near or > 20°C) during their spawning migration. While some steelhead pass through the mid-Columbia River in a matter of days, others use tributary habitats as temperature refuges for periods that can last months. Using PIT tag detection data from adult return years 2004-2016, we fit 3-component mixture models to differentiate between "fast", "slow", and "overwintering" migration behaviors in five aggregated population groups. Fast fish migrated straight through the reach on average in ~7-9 days while slow fish delayed their migration for weeks to months, and overwintering fish generally took ~150-250 days. We then fit covariate models to examine what factors contributed to the probability of migration delay during summer months (slow or overwintering behaviors), and to explore how migration delay related to mortality. Finally, to account for the impact of extended residence times in the reach for fish that delayed, we compared patterns in estimated average daily rates of mortality between migration behaviors and across population groups. Results suggest that migration delay was primarily triggered by high river temperatures but temperature thresholds for delay were lowest just before the seasonal peak in river temperatures. While all populations groups demonstrated these general patterns, we documented substantial variability in temperature thresholds and length of average delays across population groups. Although migration delay was related to higher reach mortality, it was also related to lower average daily mortality rates due to the proportional increase in reach passage duration being larger than the associated increase in mortality. Lower daily mortality rates suggest that migration delay could help mitigate the impacts of harsh migration conditions, presumably through the use of thermal refuges, despite prolonged exposure to local fisheries. Future studies tracking individual populations from their migration through reproduction could help illuminate the full extent of the tradeoffs between different migration behaviors.

Systematic Review and Patient-Level Meta-Analysis of SARS-CoV-2 Viral Dynamics to Model Response to Antiviral Therapies.

Severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral loads change rapidly following symptom onset, so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient‐level meta‐analysis of SARS‐CoV‐2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to date. This systematic review identified case reports, case series, and clinical trial data from publications between January 1, 2020, and May 31, 2020, following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. A multivariable Cox proportional hazards (Cox‐PH) regression model of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed‐effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modeling of respiratory viral dynamics was performed to quantify time‐dependent drug effects. Patient‐level data from 645 individuals (age 1 month to 100 years) with 6,316 viral loads were extracted. Model‐based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions, and breast milk were generated. Cox‐PH modeling showed longer time to viral clearance in older patients, men, and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, P < 0.001), as well as interferon, particularly when combined with ribavirin (AHR = 2.2, P = 0.015; AHR = 6.04, P = 0.006). Combination therapy should be further investigated. A viral dynamic dataset and NLME model for designing and analyzing antiviral trials has been established.

Population pharmacokinetics of meropenem among post-operative patients in Pakistan.

Meropenem, a potent carbapenem is considered the first choice for the empirical treatment of severe infections. Being a hydrophilic drug, more than 83% of the administered dose is eliminated through the renal route, and therefore, the kidney status of the patient may have a significant effect on meropenem clearance (CL). The data of 205 samples obtained from 59 patients treated with meropenem at the General Hospital Lahore, Pakistan, was used for the development of a population pharmacokinetic (-popPK) model by using nonlinear mixed-effects modeling software. The effect of age, body weight, creatinine clearance (CRCL), and gender was observed on meropenem CL through a stepwise covariate modeling approach. Simulations of 1,000mg q8h and 1,500mg q12h over 3-hour infusion were performed based on the renal status of the patients. A two-compartment model was used for popPK analysis, and the values of the pharmacokinetic parameters for CL, V1, V2, and Q were 12.2L/h, 21.7L, 7.74L, and 3.28 L/h, respectively. Meropenem CL was significantly influenced by CRCL, while no significant effect of body weight, age, and sex was observed. Both simulated dosage regimens were equally effective if CRCL of the patient was ≤100 mL/min, while 1,000mg q8h produced better results if CRCL was >100 mL/min. The CL of meropenem depends on the renal status of the patients. The model can be used for dosing simulations based on the CRCL of the patients in order to tailor the dose of meropenem in Pakistani patients.

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

ObjectivesOur aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.MethodsThe right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).ResultsOf 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87).ConclusionOur results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously.Key Points• Subchondral bone plays a role in the osteoarthritis disease processes.• MRI radiomics is a potential method for quantifying changes in subchondral bone.• Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

Projecting results of zoned multi-environment trials to new locations using environmental covariates with random coefficient models: accuracy and precision

Key messageWe propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations.Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30–38% and 12–40%, respectively.

Provider variability in the intraoperative use of neuromuscular blocking agents: a retrospective multicentre cohort study

ObjectiveTo assess variability in the intraoperative use of non-depolarising neuromuscular blocking agents (NMBAs) across individual anaesthesia providers, surgeons and hospitals.DesignRetrospective observational cohort study.SettingTwo major tertiary referral centres, Boston, Massachusetts, USA.Participants265...

Serum Creatinine and Serum Cystatin C are Both Relevant Renal Markers to Estimate Vancomycin Clearance in Critically Ill Neonates

Purpose: Serum creatinine (SCr) is used as a marker of kidney function to guide dosing of renally eliminated drugs. Serum Cystatin C (S-CysC) has been suggested as a more reliable kidney marker than SCr in adults and children. Purpose of this study was to investigate S-CysC as alternative renal marker to SCr for estimating vancomycin clearance in neonates undergoing intensive care. Methods: Vancomycin pharmacokinetics (PK), SCr and S-CysC data were collected in patients undergoing vancomycin treatment in the neonatal intensive care unit of Robert Debré Hospital - Paris. A population PK analysis was performed utilizing routine therapeutic drug monitoring samples. S-CysC and SCr were compared as covariates on vancomycin clearance using stepwise covariate modeling (forward inclusion [p < 0.05] and backward elimination [p < 0.01]). Model performance was evaluated by graphical and statistical criteria. Results: A total of 108 vancomycin concentrations from 66 patients (postmenstrual age [PMA] of 26–46 weeks) were modeled with an allometric one-compartment model. The median (range) values for SCr and S-CysC were 41 (12–153) µmol/l and 1.43 (0.95–2.83) mg/l, respectively. Following stepwise covariate model building, SCr was retained as single marker of kidney function (after accounting for weight and PMA) in the final model. Compared to the final model based on SCr, the alternative model based on S-CysC showed very similar performance (e.g. BIC of 578.3 vs. 576.4) but included one additional covariate: impact of mechanical ventilation on vancomycin clearance, in addition to the effects of size and maturation. Conclusion: ill neonates. However, if using S-CysC for this purpose mechanical ventilation needs to be taken into account.

Exposure to PM2.5 is a risk factor for acute exacerbation of surgically diagnosed idiopathic pulmonary fibrosis: a case\u2013control study

BackgroundShort-term exposure to ozone and nitrogen dioxide is a risk factor for acute exacerbation (AE) of idiopathic pulmonary fibrosis (AE-IPF). The comprehensive roles of exposure to fine particulate matter in AE-IPF remain unclear. We aim to investigate the association of short-term exposure to fine particulate matter with the incidence of AE-IPF and to determine the exposure-risk time window during 3 months before the diagnosis of AE-IPF.MethodsIPF patients were retrospectively identified from the nationwide registry in Japan. We conducted a case–control study to assess the correlation between AE-IPF incidence and short-term exposure to eight air pollutants, including particulate matter < 2.5 µm (PM2.5). In the time-series data, we compared monthly mean exposure concentrations between months with AE (case months) and those without AE (control months). We used multilevel mixed-effects logistic regression models to consider individual and institutional-level variables, and also adjusted these models for several covariates, including temperature and humidity. An additional analysis with different monthly lag periods was conducted to determine the risk-exposure time window for 3 months before the diagnosis of AE-IPF.ResultsOverall, 152 patients with surgically diagnosed IPF were analyzed. AE-IPF was significantly associated with an increased mean exposure level of nitric oxide (NO) and PM2.5 30 days prior to AE diagnosis. Adjusted odds ratio (OR) with a 10 unit increase in NO was 1.46 [95% confidence interval (CI) 1.11–1.93], and PM2.5 was 2.56 (95% CI 1.27–5.15). Additional analysis revealed that AE-IPF was associated with exposure to NO during the lag periods lag 1, lag 2, lag 1–2, and lag 1–3, and PM2.5 during the lag periods lag 1 and lag 1–2.ConclusionsOur results show that PM2.5 is a risk factor for AE-IPF, and the risk-exposure time window related to AE-IPF may lie within 1–2 months before the AE diagnosis. Further investigation is needed on the novel findings regarding the exposure to NO and AE-IPF.

Performance of adaptive exponentially weighted moving average control chart in the presence of measurement error

A control chart is one of the important industrial tools used for monitoring the stability of manufacturing processes. The performance of the control charts can be affected in the presence of measurement error, which also leads to erroneous conclusions. In this article, we examined the effect of measurement error on an adaptive exponentially weighted moving average (AEWMA) control chart by using simple random sampling (SRS) and ranked set sampling (RSS) schemes. The linear covariate model is used to evaluate the control chart in the presence of measurement error. The case of multiple measurements and linear increasing variance is also investigated in the presence of measurement error. The average run length (ARL) and the standard deviation of run length (SDRL) are used as the performance measurement tool. Our results from the simulations and real data application demonstrated that measurement error affects the performance of control charts and multiple measurements are recommended to be used to avoid the effects of measurement error.

Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants.

This article describes an agricultural application of remote sensing methods. The idea is to aid in eradicating an invasive plant called Sosnowskyi borscht (H. sosnowskyi). These plants contain strong allergens and can induce burning skin pain, and may displace native plant species by overshadowing them, meaning that even solitary individuals must be controlled or destroyed in order to prevent damage to unused rural land and other neighbouring land of various types (mostly violated forest or housing areas). We describe several methods for detecting H. sosnowskyi plants from Sentinel-2A images, and verify our results. The workflow is based on recently improved technologies, which are used to pinpoint exact locations (small areas) of plants, allowing them to be found more efficiently than by visual inspection on foot or by car. The results are in the form of images that can be classified by several methods, and estimates of the cross-covariance or single-vector auto-covariance functions of the contaminant parameters are calculated from random functions composed of plant pixel vector data arrays. The correlation of the pixel vectors for H. sosnowskyi images depends on the density of the chlorophyll content in the plants. Estimates of the covariance functions were computed by varying the quantisation interval on a certain time scale and using a computer programme based on MATLAB. The correlation between the pixels of the H. sosnowskyi plants and other plants was found, possibly because their structures have sufficiently unique spectral signatures (pixel values) in raster images. H. sosnowskyi can be identified and confirmed using a combination of two classification methods (using supervised and unsupervised approaches). The reliability of this combined method was verified by applying the theory of covariance function, and the results showed that H. sosnowskyi plants had a higher correlation coefficient. This can be used to improve the results in order to get rid of plants in particular areas. Further experiments will be carried out to confirm these results based on in situ fieldwork, and to calculate the efficiency of our method.

Population pharmacokinetic modelling and simulation of tafamidis in healthy subjects and patients with transthyretin amyloidosis

Since the first approval for transthyretin amyloid polyneuropathy patients, new formulations and different strength of tafamidis have been developed and tested in a different population (transthyretin amyloid cardiomyopathy). The objective of this analysis was to develop a unified population pharmacokinetic (PK) model of tafamidis, which can describe the PK of various different formulations in healthy subjects as well as patients with TTR amyloidosis, and to understand effects of intrinsic and extrinsic factors on the PK variability. Pooled data from 23 clinical studies (17 Phase 1 and 6 Phase 2/3 studies) were used for the analysis. The plasma concentration-time data were analysed using a nonlinear mixed effects modelling methodology. Covariate analysis was performed using a stepwise covariate model building procedure. The final model was a 2-compartment model with first-order absorption and elimination coupled with an absorption lag time for nonsolution formations. Body weight, food and tafamidis formulations were incorporated as structural covariates on PK parameters. Covariate analysis further identified age ≥65years (14.5% decrease) and moderate hepatic impairment effects (57.6% increase) on apparent clearance and transthyretin amyloid polyneuropathy effect (17.3% decrease) on F. However, model-based clinical trial simulation results indicated that tafamidis steady-state exposure changes were not clinically meaningful under the tested conditions. The unified population PK model of tafamidis was developed based on 23 studies. Subsequent clinical trial simulations indicated that no significant changes in tafamidis exposure necessitating a dose modification are expected due to either extrinsic or intrinsic factors. The model was used to support labelling statements for dose recommendations in special populations.

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study

BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Occupancy rate and observations of Baird’s tapir (Tapirella bairdii) near waterholes in the Maya forest corridor, Belize

It is estimated that 25 % of the mammal species in the world are at risk of extinction due to habitat loss, fragmentation, farming, and overexploitation. Baird’s tapir (Tapirella bairdii), an ungulate with part of its distribution within the Maya Forest, is at risk of extinction. Waterholes, a primary source for water and an essential element for the survival of tapirs, were the target of this study design. We used a camera-trap survey to determine tapir occupancy and detection rate probability models at seven survey stations at Runaway Creek Nature Reserve which is located within the Maya Forest Corridor in central Belize. The survey was carried out from March to September 2015 and from January 2017 to October 2019 with a total sampling effort of 8,932 camera-trap nights. Our results indicated a cumulative naïve occupancy estimate of 85.7 %, a rate probability of occupancy of 0.97 +/- 0.15 (SE), and a probability of detection of 0.14 +/- 0.01 for all sites and years. The results of occupancy models with human infrastructure covariate showed that the distance to roads model had the highest influence on tapir occurrence (β = -0.95 +/- 0.87), followed by the village covariate model (β = 0.77 +/- 0.99). The topographic covariate of river model (β = 0.47 +/- 0.85) had minimal support. This study highlights the critical importance of waterholes for survival of tapirs, and the influence of roads on tapir occurrence at Runaway Creek Nature Reserve.