Frailty model with change points for survival analysis

We propose a novel frailty model with change points applying random effects to a Cox proportional hazard model to adjust the heterogeneity between clusters. In the specially focused eight Empowered Action Group (EAG) states in India, there are problems with different survival curves for children up to the age of five in different states. Therefore, when analyzing the survival times for the eight EAG states, we need to adjust for the effects among states (clusters). Because the frailty model includes random effects, the parameters are estimated using the expectation-maximization (EM) algorithm. Additionally, our model needs to estimate change points; we thus propose a new algorithm extending the conventional estimation algorithm to the frailty model with change points to solve the problem. We show a practical example to demonstrate how to estimate the change point and the parameters of the distribution of random effect. Our proposed model can be easily analyzed using the existing R package. We conducted simulation studies with three scenarios to confirm the performance of our proposed model. We re-analyzed the survival time data of the eight EAG states in India to show the difference in analysis results with and without random effect. In conclusion, we confirmed that the frailty model with change points has a higher accuracy than the model without a random effect. Our proposed model is useful when heterogeneity needs to be taken into account. Additionally, the absence of heterogeneity did not affect the estimation of the regression parameters.

Will a changing climate affect hatching success in cavity-nesting birds: A case study with Eastern Bluebirds (Sialia sialis)?

A significant body of evidence indicates that climate change is influencing many aspects of avian ecology. Yet, how climate change is affecting, and is expected to influence some aspects of the breeding ecology of cavity-nesting birds remains uncertain. To explore the potential linkage between timing of first clutch, and the influence of ambient temperature on hatching success, we used Eastern Bluebird (Sialia sialis) nest records over a nine-year period from Alabama, USA. We investigated changes to annual clutch initiation dates, as well as variability in hatching success associated with ambient air temperatures during the incubation period. Using a simple linear model, we observed earlier annual egg laying dates over the nine years of this study with a difference of 24 days between earliest egg-laying date of the season. Daily temperature minima increased 2 °C across the nine-year time frame of this study. These data also indicate that Eastern Bluebird hatching success was the highest when mean ambient air temperature during incubation was between 19 °C and 24 °C (78%, as opposed to 69% and 68% above and below this temperature range, respectively). Our findings of increasing maxima, earlier maxima each year, and the lower minima of temperatures within our study area could expand the breadth of temperatures experienced by nesting Eastern Bluebirds possibly exposing them to temperatures outside of what promotes nesting success. These findings with a cavity-nesting bird highlight an optimal range of ambient temperatures associated with highest hatching success, conditions likely to be affected by climate change.

Harnessing cognitive trajectory clusterings to examine subclinical decline risk factors

Cognitive decline in Alzheimer's disease and other dementias typically begins long before clinical impairment. Identifying people experiencing subclinical decline may facilitate earlier intervention. This study developed cognitive trajectory clusters using longitudinally based random slope and change point parameter estimates from a Preclinical Alzheimer's disease Cognitive Composite and examined how baseline and most recently available clinical/health-related characteristics, cognitive statuses and biomarkers for Alzheimer's disease and vascular disease varied across these cognitive clusters. Data were drawn from the Wisconsin Registry for Alzheimer's Prevention, a longitudinal cohort study of adults from late midlife, enriched for a parental history of Alzheimer's disease and without dementia at baseline. Participants who were cognitively unimpaired at the baseline visit with ≥3 cognitive visits were included in trajectory modelling (n = 1068). The following biomarker data were available for subsets: positron emission tomography amyloid (amyloid: n = 367; [11C]Pittsburgh compound B (PiB): global PiB distribution volume ratio); positron emission tomography tau (tau: n = 321; [18F]MK-6240: primary regions of interest meta-temporal composite); MRI neurodegeneration (neurodegeneration: n = 581; hippocampal volume and global brain atrophy); T2 fluid-attenuated inversion recovery MRI white matter ischaemic lesion volumes (vascular: white matter hyperintensities; n = 419); and plasma pTau217 (n = 165). Posterior median estimate person-level change points, slopes' pre- and post-change point and estimated outcome (intercepts) at change point for cognitive composite were extracted from Bayesian Bent-Line Regression modelling and used to characterize cognitive trajectory groups (K-means clustering). A common method was used to identify amyloid/tau/neurodegeneration/vascular biomarker thresholds. We compared demographics, last visit cognitive status, health-related factors and amyloid/tau/neurodegeneration/vascular biomarkers across the cognitive groups using ANOVA, Kruskal-Wallis, χ2, and Fisher's exact tests. Mean (standard deviation) baseline and last cognitive assessment ages were 58.4 (6.4) and 66.6 (6.6) years, respectively. Cluster analysis identified three cognitive trajectory groups representing steep, n = 77 (7.2%); intermediate, n = 446 (41.8%); and minimal, n = 545 (51.0%) cognitive decline. The steep decline group was older, had more females, APOE e4 carriers and mild cognitive impairment/dementia at last visit; it also showed worse self-reported general health-related and vascular risk factors and higher amyloid, tau, neurodegeneration and white matter hyperintensity positive proportions at last visit. Subtle cognitive decline was consistently evident in the steep decline group and was associated with generally worse health. In addition, cognitive trajectory groups differed on aetiology-informative biomarkers and risk factors, suggesting an intimate link between preclinical cognitive patterns and amyloid/tau/neurodegeneration/vascular biomarker differences in late middle-aged adults. The result explains some of the heterogeneity in cognitive performance within cognitively unimpaired late middle-aged adults.

A change point-based analysis procedure for improving the success rate of decision-making in clinical trials with delayed treatment effects

A delayed treatment effect is a commonly observed phenomenon in tumor immunotherapy clinical trials. It can cause a loss of statistical power and complicate the interpretation of the analytical findings. This phenomenon also poses challenges for interim analysis in the context of phase II/III seamless design or group sequential design. It shows potential to lead researchers to make incorrect go/no-go decisions. Despite its significance, rare research has explored the impact of delayed treatment effects on the decision success rate of the interim analysis and the methods to compensate for this loss. In this study, we propose an analysis procedure based on change points for improving the decision success rate at the interim analysis in the presence of delayed treatment effects. This procedure primarily involves three steps: I. detecting and testing the number and locations of change points; II. estimating treatment efficacy; and III. making go/no-go decisions. Simulation results demonstrate that when there is a delayed treatment effect with a single change point, using the proposed analysis procedure significantly improves the decision success rate while controlling the type I error rate. Moreover, the proposed method exhibits very little disparity compared to the unadjusted method when the proportional hazards assumption holds. Therefore, the proposed analysis procedure provides a feasible approach for decision-making at the interim analysis when delayed treatment effects are present.

Detecting Structural Change Point in ARMA Models via Neural Network Regression and LSCUSUM Methods

This study considers the change point testing problem in autoregressive moving average (ARMA) (p,q) models through the location and scale-based cumulative sum (LSCUSUM) method combined with neural network regression (NNR). We estimated the model parameters via the NNR method based on the training sample, where a long AR model was fitted to obtain the residuals. Then, we selected the optimal model orders p and q of the ARMA models using the Akaike information criterion based on a validation set. Finally, we used the forecasting errors obtained from the selected model to construct the LSCUSUM test. Extensive simulations and their application to three real datasets show that the proposed NNR-based LSCUSUM test performs well.

Metabolism is a major driver of hydrogen isotope fractionation recorded in tree-ring glucose of Pinus nigra

Stable isotope abundances convey valuable information about plant physiological processes and underlying environmental controls. Central gaps in our mechanistic understanding of hydrogen isotope abundances impede their widespread application within the plant and biogeosciences. To address these gaps, we analysed intramolecular deuterium abundances in glucose of Pinus nigra extracted from an annually resolved tree-ring series (1961-1995). We found fractionation signals (i.e. temporal variability in deuterium abundance) at glucose H¹ and H² introduced by closely related metabolic processes. Regression analysis indicates that these signals (and thus metabolism) respond to drought and atmospheric CO₂ concentration beyond a response change point. They explain ≈ 60% of the whole-molecule deuterium variability. Altered metabolism is associated with below-average yet not exceptionally low growth. We propose the signals are introduced at the leaf level by changes in sucrose-to-starch carbon partitioning and anaplerotic carbon flux into the Calvin-Benson cycle. In conclusion, metabolism can be the main driver of hydrogen isotope variation in plant glucose.

Ecological thresholds under atmospheric nitrogen deposition for 1200 herbaceous species and 24 communities across the United States

Nitrogen (N) emissions and atmospheric deposition have increased significantly during the last century and become a stressor for many N-sensitive plant species. Understanding individual and community herbaceous plant species thresholds to atmospheric N deposition can inform emissions reduction policy. Here, we present results using Threshold Indicator Taxa Analysis (TITAN) applied to more than 1200 unique plant species and 24 vegetation communities (i.e., alliances) across the United States (US) to assess vulnerability to N deposition. Alliance-level thresholds (change points) for species decreasing in abundance along the gradient ranged from 1.8 to 14.3 kg N ha^─1  year^─1 and tended to be lower in the west than the east, which suggests that eastern communities, where N deposition has been historically higher, may have already lost many sensitive species. For the species that were present in more than one alliance, over half had a variable response to the N deposition gradient, suggesting that local factors affect vulnerability. Significant progress has been made during the past 30 years to reduce N emissions, which has reduced the percentage of plots at risk to N deposition from 72% to 35%. Nevertheless, over a third of plots remain at risk, and an average reduction of N deposition of 20% would protect half of the plots where N deposition exceeds community thresholds. Furthermore, the alliance- and species-level change points determined in this study may be used to inform N critical loads.

: An R Package for performing kernel change point detection on the running statistics of multivariate time series

In many scientific disciplines, researchers are interested in discovering when complex systems such as stock markets, the weather or the human body display abrupt changes. Essentially, this often comes down to detecting whether a multivariate time series contains abrupt changes in one or more statistics, such as means, variances or pairwise correlations. To assist researchers in this endeavor, this paper presents the package for performing kernel change point (KCP) detection on user-selected running statistics of multivariate time series. The running statistics are extracted by sliding a window across the time series and computing the value of the statistic(s) of interest in each window. Next, the similarities of the running values are assessed using a Gaussian kernel, and change points that segment the time series into maximally homogeneous phases are located by minimizing a within-phase variance criterion. To decide on the number of change points, a combination of a permutation-based significance test and a grid search is provided. stands out among the variety of change point detection packages available in because it can be easily adapted to uncover changes in any user-selected statistic without imposing any distribution on the data. To exhibit the usefulness of the package, two empirical examples are provided pertaining to two types of physiological data.

A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO

Ohio is one of the states most impacted by the opioid epidemic and experienced the second highest age-adjusted fatal drug overdose rate in 2017. Initially it was believed prescription opioids were driving the opioid crisis in Ohio. However, as the epidemic evolved, opioid overdose deaths due to fentanyl have drastically increased. In this work we develop a Bayesian multivariate spatiotemporal model for Ohio county overdose death rates from 2007 to 2018 due to different types of opioids. The log-odds are assumed to follow a spatially varying change point regression model. By assuming the regression coefficients are a multivariate conditional autoregressive process, we capture spatial dependence within each drug type and also dependence across drug types. The proposed model allows us to not only study spatiotemporal trends in overdose death rates but also to detect county-level shifts in these trends over time for various types of opioids.

Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States

State-level policy interventions have been critical in managing the spread of the new coronavirus. Here, we study the lag time between policy interventions and change in COVID-19 outcome trajectory in the United States. We develop a stepwise drifts random walk model to account for non-stationarity and strong temporal correlation and subsequently apply a change-point detection algorithm to estimate the number and times of change points in the COVID-19 outcome data. Furthermore, we harmonize data on the estimated change points with non-pharmaceutical interventions adopted by each state of the United States, which provides us insights regarding the lag time between the enactment of a policy and its effect on COVID-19 outcomes. We present the estimated change points for each state and the District of Columbia and find five different emerging trajectory patterns. We also provide insight into the lag time between the enactment of a policy and its effect on COVID-19 outcomes.

Stream sediment bacterial communities exhibit temporally-consistent and distinct thresholds to land use change in a mixed-use watershed

Freshwater ecosystems are susceptible to biodiversity losses due to land conversion. This is particularly true for the conversion of land from forests for agriculture and urban development. Freshwater sediments harbor microorganisms that provide vital ecosystem services. In dynamic habitats like freshwater sediments, microbial communities can be shaped by many processes, although the relative contributions of environmental factors to microbial community dynamics remain unclear. Given the future projected increase in land use change, it is important to ascertain how associated changes in stream physico-chemistry will influence sediment microbiomes. Here, we characterized stream chemistry and sediment bacterial community composition along a mixed land-use gradient in West Virginia, USA across one growing season. Sediment bacterial community richness was unaffected by increasing anthropogenic land use, though microbial communities were compositionally distinct across sites. Community threshold analysis revealed greater community resilience to agricultural land use than urban land use. Further, predicted metagenomes suggest differences in potential microbial function across changes in land use. The results of this study suggest that low levels of urban land use change can alter sediment bacterial community composition and predicted functional capacity in a mixed-use watershed, which could impact stream ecosystem services in the face of global land use change.

Exact change point detection with improved power in small-sample binomial sequences

To detect a change in the probability of a sequence of independent binomial random variables, a variety of asymptotic and exact testing procedures have been proposed. Whenever the sample size or the event rate is small, asymptotic approximations of maximally selected test statistics have been shown to be inaccurate. Although exact methods control the type I error rate, they can be overly conservative due to the discreteness of the test statistics in these situations. We extend approaches by Worsley and Halpern to develop a test that is less discrete to increase the power. Building on ideas from binary segmentation, the proposed test utilizes unused information in the binomial sequences to add a new ordering to test statistics that are of equal value. The exact distributions are derived under side conditions that arise in hypothetical segmentation steps and do not depend on the type of test statistic used (e.g., log likelihood ratio, cumulative sum, or Fisher's exact test). Using the proposed exact segmentation procedure, we construct a change point test and prove that it controls the type-I-error rate at any given nominal level. Furthermore, we prove that the new test is uniformly at least as powerful as Worsley's exact test. In a Monte Carlo simulation study, the gain in power can be remarkable, especially in scenarios with small sample size. Giving a clinical database example about pin site infections and an example assessing publication bias in neuropsychiatric drug research, we demonstrate the wide-ranging applicability of the test.

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.

Modeling repeated labor curves in consecutive pregnancies: Individualized prediction of labor progression from previous pregnancy data

The measurement of cervical dilation of a pregnant woman is used to monitor the progression of labor until 10 cm when pushing begins. There is anecdotal evidence that labor tracks across repeated pregnancies; moreover, no statistical methodology has been developed to address this important issue, which can help obstetricians make more informed clinical decisions about an individual woman's progression. Motivated by the NICHD Consecutive Pregnancies Study (CPS), we propose new methodology for analyzing labor curves across consecutive pregnancies. Our focus is both on studying the correlation between repeated labor curves on the same woman and on using the cervical dilation data from prior pregnancies to predict subsequent labor curves. We propose a hierarchical random effects model with a random change point that characterizes repeated labor curves within and between women to address these issues. We employ Bayesian methodology for parameter estimation and prediction. Model diagnostics to examine the appropriateness of the hierarchical random effects structure for characterizing the dependence structure across consecutive pregnancies are also proposed. The methodology was used in analyzing the CPS data and in developing a predictor for labor progression that can be used in clinical practice.

Estimation and Inference for Upper Hinge Regression Models

We introduce a new type of threshold regression models called upper hinge models. Under this type of threshold models, there only exists an association between the predictor of interest and the outcome when the predictor is less than some threshold value. Just like hinge models, upper hinge models can be seen as a special case of the more general segmented or two-phase regression models. The importance of studying upper hinge models is that even though they only have one fewer degree of freedom than segmented models, they can be estimated with much greater efficiency. We develop a new fast grid search algorithm to estimate upper hinge linear regression models. The new algorithm reduces the computational complexity of the search algorithm dramatically and renders the existing fast grid search algorithm inadmissible. The fast grid search algorithm makes it feasible to construct bootstrap confidence intervals for upper hinge linear regression models; for upper hinge generalized linear models of non-Gaussian family, we derive asymptotic normality to facilitate construction of model-robust confidence intervals. We perform numerical experiments and illustrate the proposed methods with two real data examples from the ecology literature.

mountainClimber Identifies Alternative Transcription Start and Polyadenylation Sites in RNA-Seq

Alternative transcription start (ATS) and alternative polyadenylation (APA) create alternative RNA isoforms and modulate many aspects of RNA expression and protein production. However, ATS and APA remain difficult to detect in RNA sequencing (RNA-seq). Here, we developed mountainClimber, a de novo cumulative-sum-based approach to identify ATS and APA as change points. Unlike many existing methods, mountainClimber runs on a single sample and identifies multiple ATS or APA sites anywhere in the transcript. We analyzed 2,342 GTEx samples (36 tissues, 215 individuals) and found that tissue type is the predominant driver of transcript end variations. 75% and 65% of genes exhibited differential APA and ATS across tissues, respectively. In particular, testis displayed longer 5' untranslated regions (UTRs) and shorter 3' UTRs, often in genes related to testis-specific biology. Overall, we report the largest study of transcript ends across human tissues to our knowledge. mountainClimber is available at github.com/gxiaolab/mountainClimber.

Determination of Cut Point in the Age of Colorectal Cancer Diagnosis Using a Survival Cure Model

Background and Objectives:

Colorectal Cancer (CRC) is the fourth cancer-related cause of death worldwide. CRC is a multi-stage cancer, which is curable during the early stages of the disease. Therefore, determining the time of cut-point existence could improve treatment planning and help directly allocate resources. This study aims to determine the cut point in the age of CRC diagnosis.

Methods:

This study, covering the course 1985 to 2012, consisted of 345 colorectal cancer patients registered in Taleghani Hospital, Tehran, Iran and followed up to 2013. The cut-point in the age of CRC diagnosis was obtained using a mixture cure model. The data were analyzed using SPSS and R, V. 20 and 2.15.0, respectively.

Results:

The results showed that the cut point in the age of CRC diagnosis was 50 years. Based on our estimation, 65% of the patients diagnosed with CRC at or younger than 50 were cured, while 31% of them diagnosed older than 50 were cured, and the younger group had a better survival over the older group.

Conclusion:

Since access to a cut-point and analysis of created prognostic groups are important in screening and treatment planning, our results suggested that it is better to estimate the cut-point in the age of curable cancers in early stages via survival cure models, and the cure rate would increase by CRC timely screening.

Fast Bootstrap Confidence Intervals for Continuous Threshold Linear Regression

Continuous threshold regression is a common type of nonlinear regression that is attractive to many practitioners for its easy interpretability. More widespread adoption of thresh-old regression faces two challenges: (i) the computational complexity of fitting threshold regression models and (ii) obtaining correct coverage of confidence intervals under model misspecification. Both challenges result from the non-smooth and non-convex nature of the threshold regression model likelihood function. In this paper we first show that these two issues together make the ideal approach for making model-robust inference in continuous threshold linear regression an impractical one. The need for a faster way of fitting continuous threshold linear models motivated us to develop a fast grid search method. The new method, based on the simple yet powerful dynamic programming principle, improves the performance by several orders of magnitude.

A model-based multithreshold method for subgroup identification

Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results. After choosing a particular thresholding variable form, we apply a two-stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability. In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.

Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them.

Threshold responses of Amazonian stream fishes to timing and extent of deforestation

Deforestation is a primary driver of biodiversity change through habitat loss and fragmentation. Stream biodiversity may not respond to deforestation in a simple linear relationship. Rather, threshold responses to extent and timing of deforestation may occur. Identification of critical deforestation thresholds is needed for effective conservation and management. We tested for threshold responses of fish species and functional groups to degree of watershed and riparian zone deforestation and time since impact in 75 streams in the western Brazilian Amazon. We used remote sensing to assess deforestation from 1984 to 2011. Fish assemblages were sampled with seines and dip nets in a standardized manner. Fish species (n = 84) were classified into 20 functional groups based on ecomorphological traits associated with habitat use, feeding, and locomotion. Threshold responses were quantified using threshold indicator taxa analysis. Negative threshold responses to deforestation were common and consistently occurred at very low levels of deforestation (<20%) and soon after impact (<10 years). Sensitive species were functionally unique and associated with complex habitats and structures of allochthonous origin found in forested watersheds. Positive threshold responses of species were less common and generally occurred at >70% deforestation and >10 years after impact. Findings were similar at the community level for both taxonomic and functional analyses. Because most negative threshold responses occurred at low levels of deforestation and soon after impact, even minimal change is expected to negatively affect biodiversity. Delayed positive threshold responses to extreme deforestation by a few species do not offset the loss of sensitive taxa and likely contribute to biotic homogenization.

Predicting events in clinical trials using two time-to-event outcomes

In clinical trials with time-to-event outcomes, it is of interest to predict when a prespecified number of events can be reached. Interim analysis is conducted to estimate the underlying survival function. When another correlated time-to-event endpoint is available, both outcome variables can be used to improve estimation efficiency. In this paper, we propose to use the convolution of two time-to-event variables to estimate the survival function of interest. Propositions and examples are provided based on exponential models that accommodate possible change points. We further propose a new estimation equation about the expected time that exploits the relationship of two endpoints. Simulations and the analysis of real data show that the proposed methods with bivariate information yield significant improvement in prediction over that of the univariate method.

A New Approach for Determining Short-Term, Objective Prognostic Predictive Methods for Terminal Cancer Patients Based on the Change Point of Laboratory Test Values

BACKGROUND

In terminal phase cancer, predicting a prognosis precisely plays an important role for patients and their families to live meaningful lives. However, there are no established short-term, objective prognostic predictive methods.

OBJECTIVE

To develop simple, short-term, objective prognostic predictive methods through detecting a change point for laboratory test values.

DESIGN

A retrospective chart review.

SETTING/SUBJECTS

Subjects were cancer patients aged ≥16 years and discharged dead from Osaka University Hospital in 2008.

MEASUREMENTS

Using different laboratory test values, new prognostic predictive methods were determined based on either six laboratory test values (white blood cell [WBC], platelet [PLT], C-reactive protein, blood urea nitrogen [BUN], aspartate aminotransferase [AST], and lactase dehydrogenase [LDH]): the WPCBAL score, or five test values (WBC, PLT, BUN, AST, and LDH): the WPBAL score. Their utility, including sensitivity and specificity, was compared with that of Glasgow prognostic scores (GPSs).

RESULTS

In total, 121 cancer patients were enrolled. WPCBAL and WPBAL scores showed higher sensitivity (0.88 and 0.91 vs. 0.68), specificity (0.79 and 0.70 vs. 0.53), negative predictive value (0.98 and 0.97 vs. 0.76), and a much larger relative risk (16.5 and 14.2 vs. 1.78) as prognostic predictors within two weeks of death than GPS as a prognostic predictor within three weeks of death.

CONCLUSION

This is the first study that suggests that the objective prognostic predictive methods, through detecting the change point of laboratory test values, are useful for predicting short-term prognosis. The WPCBAL score and WPBAL score could objectively predict the remaining lifetime within two weeks of mortality.

Cognitive Trajectory Changes Over 20 Years Before Dementia Diagnosis: A Large Cohort Study

BACKGROUND/OBJECTIVES

Longitudinal studies have shown an increase in cognitive decline many years before clinical diagnosis of dementia. We sought to estimate changes, relative to "normal" aging, in the trajectory of scores on a global cognitive function test-the Cognitive Abilities Screening Instrument (CASI).

DESIGN

A prospective cohort study.

SETTING

Community-dwelling members of a U.S. health maintenance organization.

PARTICIPANTS

Individuals aged 65 and older who had no dementia diagnosis at baseline and had at least two visits with valid CASI test score (N = 4,315).

MEASUREMENTS

Average longitudinal trajectories, including changes in trajectory before clinical diagnosis in those who would be diagnosed with dementia, were estimated for CASI item response theory (IRT) scores. The impact of sex, education level, and APOE genotype on cognitive trajectories was assessed.

RESULTS

Increased cognitive decline relative to "normal" aging was evident in CASI IRT at least 10 years before clinical diagnosis. Male gender, lower education, and presence of ≥1 APOE ε4 alleles were associated with lower average IRT scores. In those who would be diagnosed with dementia, a trajectory change point was estimated at an average of 3.1 years (95% confidence interval 3.0-3.2) before clinical diagnosis, after which cognitive decline appeared to accelerate. The change point did not differ by sex, education level, or APOE ε4 genotype. There were subtle differences in trajectory slopes by sex and APOE ε4 genotype, but not by education.

CONCLUSION

Decline in average global cognitive function was evident at least 10 years before clinical diagnosis of dementia. The decline accelerated about 3 years before clinical diagnosis.

A Bayesian semiparametric approach with change points for spatial ordinal data

The change-point model has drawn much attention over the past few decades. It can accommodate the jump process, which allows for changes of the effects before and after the change point. Intellectual disability is a long-term disability that impacts performance in cognitive aspects of life and usually has its onset prior to birth. Among many potential causes, soil chemical exposures are associated with the risk of intellectual disability in children. Motivated by a study for soil metal effects on intellectual disability, we propose a Bayesian hierarchical spatial model with change points for spatial ordinal data to detect the unknown threshold effects. The spatial continuous latent variable underlying the spatial ordinal outcome is modeled by the multivariate Gaussian process, which captures spatial variation and is centered at the nonlinear mean. The mean function is modeled by using the penalized smoothing splines for some covariates with unknown change points and the linear regression for the others. Some identifiability constraints are used to define the latent variable. A simulation example is presented to evaluate the performance of the proposed approach with the competing models. A retrospective cohort study for intellectual disability in South Carolina is used as an illustration.

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.

Rapid declines of large mammal populations after the collapse of the Soviet Union

Anecdotal evidence suggests that socioeconomic shocks strongly affect wildlife populations, but quantitative evidence is sparse. The collapse of socialism in Russia in 1991 caused a major socioeconomic shock, including a sharp increase in poverty. We analyzed population trends of 8 large mammals in Russia from 1981 to 2010 (i.e., before and after the collapse). We hypothesized that the collapse would first cause population declines, primarily due to overexploitation, and then population increases due to adaptation of wildlife to new environments following the collapse. The long-term Database of the Russian Federal Agency of Game Mammal Monitoring, consisting of up to 50,000 transects that are monitored annually, provided an exceptional data set for investigating these population trends. Three species showed strong declines in population growth rates in the decade following the collapse, while grey wolf (Canis lupus) increased by more than 150%. After 2000 some trends reversed. For example, roe deer (Capreolus spp.) abundance in 2010 was the highest of any period in our study. Likely reasons for the population declines in the 1990s include poaching and the erosion of wildlife protection enforcement. The rapid increase of the grey wolf populations is likely due to the cessation of governmental population control. In general, the widespread declines in wildlife populations after the collapse of the Soviet Union highlight the magnitude of the effects that socioeconomic shocks can have on wildlife populations and the possible need for special conservation efforts during such times.

An Efficient Operator for the Change Point Estimation in Partial Spline Model

In bio-informatics application, the estimation of the starting and ending points of drop-down in the longitudinal data is important. One possible approach to estimate such change times is to use the partial spline model with change points. In order to use estimate change time, the minimum operator in terms of a smoothing parameter has been widely used, but we showed that the minimum operator causes large MSE of change point estimates. In this paper, we proposed the summation operator in terms of a smoothing parameter, and our simulation study showed that the summation operator gives smaller MSE for estimated change points than the minimum one. We also applied the proposed approach to the experiment data, blood flow during photodynamic cancer therapy.

Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers

Prostate-specific antigen (PSA) is a widely used marker in clinical trials for patients with prostate cancer. We develop a mixture model to estimate longitudinal PSA trajectory in response to treatment. The model accommodates subjects responding and not responding to therapy through a mixture of two functions. A responder is described by a piecewise linear function, represented by an intercept, a PSA decline rate, a period of PSA decline, and a PSA rising rate; a nonresponder is described by an increasing linear function with an intercept and a PSA rising rate. Each trajectory is classified as a linear or a piecewise linear function with a certain probability, and the weighted average of these two functions sufficiently characterizes a variety of patterns of PSA trajectories. Furthermore, this mixture structure enables us to derive clinically useful endpoints such as a response rate and time-to-progression, as well as biologically meaningful endpoints such as a cancer cell killing fraction and tumor growth delay. We compare our model with the most commonly used dynamic model in the literature and show its advantages. Finally, we illustrate our approach using data from two multicenter prostate cancer trials. The R code used to produce the analyses reported in this paper is available on request.

G-scores: a method for identifying disease-causing pathogens with application to lower respiratory tract infections

Lower respiratory tract infections (LRTIs) are well known for the lack of a good diagnostic method. The main difficulty lies in the fact that there are a variety of pathogens causing LRTIs, and their management and treatment are quite different. The development of quantitative real-time loop-mediated isothermal amplification (qrt-LAMP) made it possible to rapidly amplify and quantify multiple pathogens simultaneously. The question that remains to be answered is how accurate and reliable is this method? More importantly, how are qrt-LAMP measurements utilized to inform/suggest medical decisions? When does a pathogen start to grow out of control and cause infection? Answers to these questions are crucial to advise treatment guidance for LRTIs and also helpful to design phase I/II trials or adaptive treatment strategies. In this article, our main contributions include the following two aspects. First, we utilize zero-inflated mixture models to provide statistical evidence for the validity of qrt-LAMP being used in detecting pathogens for LRTIs without the presence of a gold standard test. Our results on qrt-LAMP suggest that it provides reliable measurements on pathogens of interest. Second, we propose a novel statistical approach to identify disease-causing pathogens, that is, distinguish the pathogens that colonize without causing problems from those that rapidly grow and cause infection. We achieve this by combining information from absolute quantities of pathogens and their symbiosis information to form G-scores. Change-point detection methods are utilized on these G-scores to detect the three phases of bacterial growth-lag phase, log phase, and stationary phase.

Identifying multiple change points in a linear mixed effects model

Although change-point analysis methods for longitudinal data have been developed, it is often of interest to detect multiple change points in longitudinal data. In this paper, we propose a linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data. Specifically, we develop a novel statistical and computational framework that integrates the expectation-maximization and the dynamic programming algorithms. We conduct a comprehensive simulation study to demonstrate the performance of our method. We illustrate our method with an analysis of data from a trial evaluating a behavioral intervention for the control of type I diabetes in adolescents with HbA1c as the longitudinal response variable.

Change-point models to estimate the limit of detection

In many biological and environmental studies, measured data is subject to a limit of detection. The limit of detection is generally defined as the lowest concentration of analyte that can be differentiated from a blank sample with some certainty. Data falling below the limit of detection is left censored, falling below a level that is easily quantified by a measuring device. A great deal of interest lies in estimating the limit of detection for a particular measurement device. In this paper, we propose a change-point model to estimate the limit of detection by using data from an experiment with known analyte concentrations. Estimation of the limit of detection proceeds by a two-stage maximum likelihood method. Extensions are considered that allow for censored measurements and data from multiple experiments. A simulation study is conducted demonstrating that in some settings the change-point model provides less biased estimates of the limit of detection than conventional methods. The proposed method is then applied to data from an HIV pilot study.

A Bayesian analysis of the 2009 decline in tuberculosis morbidity in the United States

Although annual data are commonly used to model linear trends and changes in trends of disease incidence, monthly data could provide additional resolution for statistical inferences. Because monthly data may exhibit seasonal patterns, we need to consider seasonally adjusted models, which can be theoretically complex and computationally intensive. We propose a combination of methods to reduce the complexity of modeling seasonal data and to provide estimates for a change in trend when the timing and magnitude of the change are unknown. To assess potential changes in trend, we first used autoregressive integrated moving average (ARIMA) models to analyze the residuals and forecast errors, followed by multiple ARIMA intervention models to estimate the timing and magnitude of the change. Because the variable corresponding to time of change is not a statistical parameter, its confidence bounds cannot be estimated by intervention models. To model timing of change and its credible interval, we developed a Bayesian technique. We avoided the need for computationally intensive simulations by deriving a closed form for the posterior distribution of the time of change. Using a combination of ARIMA and Bayesian methods, we estimated the timing and magnitude of change in trend for tuberculosis cases in the United States. Published 2012. This article is a US Government work and is in the public domain in the USA.

An examination of Bayesian statistical approaches to modeling change in cognitive decline in an Alzheimer's disease population

The mini mental state examination (MMSE) is a common tool for measuring cognitive decline in Alzhiemer's Disease (AD) subjects. Subjects are usually observed for a specified period of time or until death to determine the trajectory of the decline which for the most part appears to be linear. However, it may be noted that the decline may not be modeled by a single linear model over a specified period of time. There may be a point called a change point where the rate or gradient of the decline may change depending on the length of time of observation. A Bayesian approach is used to model the trajectory and determine an appropriate posterior estimate of the change point as well as the predicted model of decline before and after the change point. Estimates of the appropriate parameters as well as their posterior credible regions or regions of interest are established. Coherent prior to posterior analysis using mainly non informative priors for the parameters of interest is provided. This approach is applied to an existing AD database.