scholarly journals Combination of longitudinal biomarkers in predicting binary events

Biostatistics ◽  
2014 ◽  
Vol 15 (4) ◽  
pp. 706-718 ◽  
Author(s):  
Danping Liu ◽  
Paul S. Albert
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Disease Risk ◽  
Likelihood Ratio Statistic ◽  
Disease Status ◽  
Analytical Form ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Shared Random Effects

Abstract In disease screening, the combination of multiple biomarkers often substantially improves the diagnostic accuracy over a single marker. This is particularly true for longitudinal biomarkers where individual trajectory may improve the diagnosis. We propose a pattern mixture model (PMM) framework to predict a binary disease status from a longitudinal sequence of biomarkers. The marker distribution given the disease status is estimated from a linear mixed effects model. A likelihood ratio statistic is computed as the combination rule, which is optimal in the sense of the maximum receiver operating characteristic (ROC) curve under the correctly specified mixed effects model. The individual disease risk score is then estimated by Bayes’ theorem, and we derive the analytical form of the 95% confidence interval. We show that this PMM is an approximation to the shared random effects (SRE) model proposed by Albert (2012. A linear mixed model for predicting a binary event from longitudinal data under random effects mis-specification. Statistics in Medicine31(2), 143–154). Further, with extensive simulation studies, we found that the PMM is more robust than the SRE model under wide classes of models. This new PPM approach for combining biomarkers is motivated by and applied to a fetal growth study, where the interest is in predicting macrosomia using longitudinal ultrasound measurements.

scholarly journals Poisson–Tweedie mixed-effects model: A flexible approach for the analysis of longitudinal RNA-seq data

2020 ◽  
pp. 1471082X2093601
Author(s):  
Mirko Signorelli ◽  
Pietro Spitali ◽  
Roula Tsonaka
Keyword(s):  
Rna Sequencing ◽  
Heavy Tails ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Mixed Effects Model ◽  
Rna Seq

We present a new modelling approach for longitudinal overdispersed counts that is motivated by the increasing availability of longitudinal RNA-sequencing experiments. The distribution of RNA-seq counts typically exhibits overdispersion, zero-inflation and heavy tails; moreover, in longitudinal designs repeated measurements from the same subject are typically (positively) correlated. We propose a generalized linear mixed model based on the Poisson–Tweedie distribution that can flexibly handle each of the aforementioned features of longitudinal overdispersed counts. We develop a computational approach to accurately evaluate the likelihood of the proposed model and to perform maximum likelihood estimation. Our approach is implemented in the R package ptmixed, which can be freely downloaded from CRAN. We assess the performance of ptmixed on simulated data, and we present an application to a dataset with longitudinal RNA-sequencing measurements from healthy and dystrophic mice. The applicability of the Poisson–Tweedie mixed-effects model is not restricted to longitudinal RNA-seq data, but it extends to any scenario where non-independent measurements of a discrete overdispersed response variable are available.

scholarly journals Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

2016 ◽  
Vol 27 (7) ◽  
pp. 2060-2080 ◽  
Author(s):  
David M Hughes ◽  
Arnošt Komárek ◽  
Gabriela Czanner ◽  
Marta Garcia-Fiñana
Keyword(s):  
Discriminant Analysis ◽  
Random Effects ◽  
Clinical Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Clinical Information ◽  
Disease Status ◽  
Time Dependent ◽  
Different Types ◽  
Multiple Biomarkers

There is an emerging need in clinical research to accurately predict patients’ disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a five-year follow-up period.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals Probability of pregnancy before ninety days postpartum in water buffaloes

2021 ◽  
Vol 30 (1) ◽  
pp. 29-34
Author(s):  
Hector Nava-Trujillo ◽  
Robert Valeris-Chacin ◽  
Adriana Morgado-Osorio ◽  
Javier Hernández ◽  
Janeth Caamaño ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Water Buffalo ◽  
Mixed Effects ◽  
Short Photoperiod ◽  
Water Buffaloes ◽  
Negative Effect ◽  
Log Odds ◽  
First 90 Days ◽  
Long Photoperiod

This study aimed to determine the effect of parity and season of calving on the probability of water buffalo cows becoming pregnant before 90 days postpartum. A retrospective analysis of reproductive records of 1,465 water buffaloes with 3,181 pregnancies was carried out. Buffaloes were grouped according to parity in one, two, or three and more calvings. Season of calving was created with the following values: long photoperiod (March-August) and short photoperiod (September-February) and predicted probabilities from the mixed-effects logistic regression model were calculated, and a generalized linear mixed model was fitted with random intercepts to calculate the log odds of becoming pregnant ≤90 days postpartum. The probability of pregnancy ≤90 days postpartum was 0.3645, and this was lower in primiparous (0.2717) in comparison with two-calved (0.3863) and three or more calving buffaloes (0.5166). Probability of pregnancy ≤90 days postpartum increased 1.77 odds by each increase in parity. The probability of becoming pregnant ≤90 days postpartum was higher in water buffaloes calving during the short photoperiod season (0.4239 vs. 0.2474, P>0.000), and water buffaloes calving during the long photoperiod season only had 0.2645 odds to become pregnant than those calving during the short photoperiod season. The negative effect of long photoperiod was observed indifferently of parity. In conclusion, primiparity and the long photoperiod affect water buffalo cow's reproductive performance, decreasing pregnancy probability during the first 90 days postpartum.

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

scholarly journals A graphical model for skewed matrix-variate non-randomly missing data

Biostatistics ◽  
2018 ◽  
Author(s):  
Lin Zhang ◽  
Dipankar Bandyopadhyay
Keyword(s):  
Mixed Model ◽  
Graphical Model ◽  
Linear Mixed Model ◽  
Disease Status ◽  
Epidemiological Studies ◽  
Model Parameters ◽  
Probit Regression ◽  
Pocket Depth ◽  
Cross Sectional ◽  
Missing Responses

SummaryEpidemiological studies on periodontal disease (PD) collect relevant bio-markers, such as the clinical attachment level (CAL) and the probed pocket depth (PPD), at pre-specified tooth sites clustered within a subject’s mouth, along with various other demographic and biological risk factors. Routine cross-sectional evaluation are conducted under a linear mixed model (LMM) framework with underlying normality assumptions on the random terms. However, a careful investigation reveals considerable non-normality manifested in those random terms, in the form of skewness and tail behavior. In addition, PD progression is hypothesized to be spatially-referenced, i.e. disease status at proximal tooth-sites may be different from distally located sites, and tooth missingness is non-random (or informative), given that the number and location of missing teeth informs about the periodontal health in that region. To mitigate these complexities, we consider a matrix-variate skew-$t$ formulation of the LMM with a Markov graphical embedding to handle the site-level spatial associations of the bivariate (PPD and CAL) responses. Within the same framework, the non-randomly missing responses are imputed via a latent probit regression of the missingness indicator over the responses. Our hierarchical Bayesian framework powered by relevant Markov chain Monte Carlo steps addresses the aforementioned complexities within an unified paradigm, and estimates model parameters with seamless sharing of information across various stages of the hierarchy. Using both synthetic and real clinical data assessing PD status, we demonstrate a significantly improved fit of our proposition over various other alternative models.

Assessing the Effect of Mansionization on Suburban Single-Family House Sales Prices

2019 ◽  
pp. 0739456X1983315
Author(s):  
Suzanne Lanyi Charles
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Single Family ◽  
Socioeconomic Characteristics ◽  
House Values ◽  
Crossed Random Effects ◽  
Family House

Using observed single-family house sales in the inner-ring suburbs of Chicago from 2010 through 2017, this paper uses a multilevel mixed-effects model with crossed random effects to estimate the effect that millennium mansions—new, large single-family houses—have on the sales prices of nearby single-family houses. Controlling for property, sale timing, and surrounding neighborhood socioeconomic characteristics, the study finds that mansionization is associated with an increase in the sales prices of neighboring houses. Long-term residents of a neighborhood undergoing mansionization should not fear a decrease in their house values; however, decreases in neighborhood affordability may result in exclusionary displacement.

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