Latent transition analysis with covariates: pubertal timing and substance use behaviours in adolescent females

We investigate the impact of pubertal development, age, and its interaction on female substance use behaviour. An extended latent transition model with two latent variables is used to reflect the dependency of adolescent substance use on pubertal development and age. A sample of females in grades 7-12 is analysed using maximum-likelihood estimation. Analyses indicate that experiencing puberty is related to increased substance use for all age groups. Among females aged 12-15, those who have experienced puberty are more likely to advance in substance use compared to their late-maturing counterparts. Particularly, among 12-year old non-substance users, those who have experienced puberty are approximately three times more likely to advance towards substance use than those who have not experienced puberty. In addition, among older females, those whose puberty is in progress are more prone to advance in substance use compared to those whose puberty has not occurred.     

Latent variable models with nonparametric interaction effects of latent variables

Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P‐splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.     

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category ‘missing by design’, rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models. Copyright © 2009 John Wiley & Sons, Ltd.     

Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables

Joint modelling of longitudinal and survival data has received much attention in recent years. Most have concentrated on a single longitudinal variable. This paper considers joint modelling in the presence of multiple longitudinal variables. We explore direct association of time-to-event and multiple longitudinal processes through a frailty model and use a mixed effects model for each of the longitudinal variables. Correlations among the longitudinal variables are induced through correlated random effects. We allow effects of categorical and continuous covariates on both longitudinal and time-to-event responses and explore interactions between the longitudinal variables and other covariates on time-to-event. Estimates of the parameters are obtained by maximizing the joint likelihood for the longitudinal variable processes and the event process. We use a one-step-late EM algorithm to handle the direct dependence of the event process on the modelled longitudinal variables along with the presence of other fixed covariates in both processes. We argue that such a joint analysis with multiple longitudinal variables is advantageous to one with only a single longitudinal variable in revealing interplay among multiple longitudinal variables and the time-to-event.     

Stroke Rehabilitation Use and Caregiver Psychosocial Health Profiles in Singapore: A Latent Profile Transition Analysis

ObjectivesTo identify and describe caregiver profiles based on their psychosocial health characteristics over a 12-month period and transitions among these profiles, to determine if stroke rehabilitation use at 12 months post-stroke differed by caregiver profile transition patterns, and to investigate if caregiver profiles at 3 months post-stroke moderate the association of stroke rehabilitation use at 3 months and 12 months post-stroke after accounting for covariates.DesignLatent profile transition analysis of caregiver psychosocial health with stroke rehabilitation use at 12 month post-stroke as outcome.Setting and ParticipantsA total of 149 stroke patient-caregiver dyads from the Singapore Stroke Study.MethodsCross-sectional latent profile analyses were conducted on caregiver psychosocial health indicators of burden, depression, health status, quality of relationship with patient, and social support. Changes in latent profile classification over 3 time points (baseline, 3 months, and 12 months post-stroke) were analyzed using latent transition analysis. A transition model with stroke rehabilitation use at 12 months post-stroke as the outcome was tested after accounting for covariates.ResultsTwo distinct caregiver psychosocial health latent profiles were found across time: nondistressed and distressed. Most caregivers were classified as nondistressed and remained nondistressed over time. Distressed caregivers at baseline were 76% likely to become nondistressed at 12 month post-stroke. Regardless of profile transition patterns, nondistressed caregivers at 12 months post-stroke tended to have cared for stroke rehabilitation nonusers at 12 months post-stroke. Patient depression explained profile classification at 3 months and 12 months post-stroke. After accounting for covariates, rehabilitation users at 3 months post-stroke tended to continue using rehabilitation at 12 months post-stroke only when they had nondistressed caregivers at 3 months post-stroke.Conclusions and ImplicationsWhether caregiver adaptation explains the associations between the latent profile transition patterns and rehabilitation use at 12 months post-stroke should be examined. Early psychosocial health assessment and sustained support should be made available to stroke caregivers to enhance their well-being and subsequent patient rehabilitation participation.     

Latent class joint model of ovarian function suppression and DFS for premenopausal breast cancer patients

Breast cancer is the leading cancer in women of reproductive age; more than a quarter of women diagnosed with breast cancer in the US are premenopausal. A common adjuvant treatment for this patient population is chemotherapy, which has been shown to cause premature menopause and infertility with serious consequences to quality of life. Luteinizing‐hormone‐releasing hormone (LHRH) agonists, which induce temporary ovarian function suppression (OFS), has been shown to be a useful alternative to chemotherapy in the adjuvant setting for estrogen‐receptor‐positive breast cancer patients. LHRH agonists have the potential to preserve fertility after treatment, thus, reducing the negative effects on a patient's reproductive health. However, little is known about the association between a patient's underlying degree of OFS and disease‐free survival (DFS) after receiving LHRH agonists. Specifically, we are interested in whether patients with lower underlying degrees of OFS (i.e. higher estrogen production) after taking LHRH agonists are at a higher risk for late breast cancer events. In this paper, we propose a latent class joint model (LCJM) to analyze a data set from International Breast Cancer Study Group (IBCSG) Trial VIII to investigate the association between OFS and DFS. Analysis of this data set is challenging due to the fact that the main outcome of interest, OFS, is unobservable and the available surrogates for this latent variable involve masked event and cured proportions. We employ a likelihood approach and the EM algorithm to obtain parameter estimates and present results from the IBCSG data analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Incorporating covariates in mapping heterogeneous traits: a hierarchical model using empirical Bayes estimation

Complex genetic traits are inherently heterogeneous, i.e., they may be caused by different genes, or non-genetic factors, in different individuals. So, for mapping genes responsible for these diseases using linkage analysis, heterogeneity must be accounted for in the model. Heterogeneity across different families can be modeled using a mixture distribution by letting each family have its own heterogeneity parameter denoting the probability that its disease-causing gene is linked to the marker map under consideration. A substantial gain in power is expected if covariates that can discriminate between the families of linked and unlinked types are incorporated in this modeling framework. To this end, we propose a hierarchical Bayesian model, in which the families are grouped according to various (categorized) levels of covariate(s). The heterogeneity parameters of families within each group are assigned a common prior, whose parameters are further assigned hyper-priors. The hyper-parameters are obtained by utilizing the empirical Bayes estimates. We also address related issues such as evaluating whether the covariate(s) under consideration are informative and grouping of families. We compare the proposed approach with one that does not utilize covariates and show that our approach leads to considerable gains in power to detect linkage and in precision of interval estimates through various simulation scenarios. An application to the asthma datasets of Genetic Analysis Workshop 12 also illustrates this gain in a real data analysis. Additionally, we compare the performances of microsatellite markers and single nucleotide polymorphisms for our approach and find that the latter clearly outperforms the former.     

Estimation of the transition matrix of a discrete-time Markov chain.

Discrete-time Markov chains have been successfully used to investigate treatment programs and health care protocols for chronic diseases. In these situations, the transition matrix, which describes the natural progression of the disease, is often estimated from a cohort observed at common intervals. Estimation of the matrix, however, is often complicated by the complex relationship among transition probabilities. This paper summarizes methods to obtain the maximum likelihood estimate of the transition matrix when the cycle length of the model coincides with the observation interval, the cycle length does not coincide with the observation interval, and when the observation intervals are unequal in length. In addition, the bootstrap is discussed as a method to assess the uncertainty of the maximum likelihood estimate and to construct confidence intervals for functions of the transition matrix such as expected survival.     

Statistical properties and inference of the antimicrobial MIC test

A common method for measuring the drug-specific minimum inhibitory concentration (MIC) of an antibacterial agent is via a two-fold broth dilution test known as the MIC test. Because this procedure implicitly rounds data upward, inference based on unadjusted measurements is biased and overestimates bacterial resistance to a drug. We detail this test procedure and its associated bias, which, in many cases, has an expected value of approximately 0.5 on the log(2) scale. In addition, new bias-corrected estimates of resistance are proposed. A numeric example is used to illustrate the extent to which the traditional resistance estimate can overestimate the true proportion of resistant strains, a phenomenon which is remedied by using the proposed estimates.     

Cluster analysis using multivariate mixed effects models

A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. Recently, methods have been proposed to deal with cases where individuals are classified into clusters through a linear model of mixed univariate effects deriving from a longitudinally measured variable. The method proposed in the current work deals with the case of clustering and then classification based on two or more variables measured longitudinally, through the fitting of non‐linear multivariate mixed effect models, and with consideration given to parameter estimation for balanced and unbalanced data using an EM algorithm. The application of the method is illustrated with an example in which the clusters are identified and the classification into clusters is compared with the true membership of individuals in one of two groups, which is known at the end of the follow‐up period. Copyright © 2009 John Wiley & Sons, Ltd.     

Statistical inference for missing data mechanisms

In the literature of statistical analysis with missing data there is a significant gap in statistical inference for missing data mechanisms especially for nonmonotone missing data, which has essentially restricted the use of the estimation methods which require estimating the missing data mechanisms. For example, the inverse probability weighting methods (Horvitz & Thompson, 1952; Little & Rubin, 2002), including the popular augmented inverse probability weighting (Robins et al, 1994), depend on sufficient models for the missing data mechanisms to reduce estimation bias while improving estimation efficiency. This research proposes a semiparametric likelihood method for estimating missing data mechanisms where an EM algorithm with closed form expressions for both E-step and M-step is used in evaluating the estimate (Zhao et al, 2009; Zhao, 2020). The asymptotic variance of the proposed estimator is estimated from the profile score function. The methods are general and robust. Simulation studies in various missing data settings are performed to examine the finite sample performance of the proposed method. Finally, we analysis the missing data mechanism of Duke cardiac catheterization coronary artery disease diagnostic data to illustrate the method.     

Latent time‐varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates

Longitudinal data are often segmented by unobserved time‐varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject‐specific random effects and Markovian sequences of time‐varying effects in the linear predictor. We propose an expectation?‐maximization algorithm for maximum likelihood estimation, based on data augmentation. It reduces to the iterative maximization of the expected value of a complete likelihood function, derived from an augmented dataset with case weights, alternated with weights updating. In a case study of the Survey on Stress Aging and Health in Russia, the model is exploited to estimate the influence of the observed covariates under unobserved time‐varying factors, which affect the cardiovascular activity of each subject during the observation period. Copyright © 2014 John Wiley & Sons, Ltd.     

Using Latent Mixed Markov Models for the choice of the best pharmacological treatment

The choice of the best pharmacological treatment for an individual patient is crucial to optimize convalescence. Due to their effects on pharmacokinetics variables like gender and age are important factors when the pharmacological regimen is planned. By means of an example from anaesthesiology the usefulness of Latent Mixed Markov Models for choosing the optimal anaesthetic considering patient characteristics is demonstrated. Latent Mixed Markov models allow to predict and compare the quality of recovery from anaesthesia for different patient groups (defined by age and gender and treated with different anaesthetic regimens) in a multivariate non-parametric approach. On the basis of observed symptoms immediately after surgery and a few days later the probabilities for the respective dynamic latent status (like health or illness) and the probabilities for transition from one status to another are estimated depending on latent class membership (patient group).     

Imputation methods to improve inference in SNP association studies

Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we develop two haplotype-based imputation approaches and one tree-based imputation approach for association studies. The emphasis is to evaluate the impact of imputation on parameter estimation, compared to the standard practice of ignoring missing data. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves efficiency over the standard practice of ignoring missing data. The tree-based approach performs comparably well as haplotype-based approaches, but the former has a computational advantage. The WEM approach yields the smallest bias at a price of increased variance.     

Semiparametric estimation of the cure fraction in population-based cancer survival analysis

With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female breast cancer study, background mortality is incorporated into the mixture cure proportional hazards (MCPH) model to improve the cure fraction estimation in population-based cancer studies. Here, that patients are “cured” is defined as when the mortality rate of the individuals in diseased group returns to the same level as that expected in the general population, where the population level mortality is presented by the mortality table of the United States. The semiparametric estimation method based on the EM algorithm for the MCPH model with background mortality (MCPH+BM) is further developed and validated via comprehensive simulation studies. Real data analysis shows that the proposed semiparametric MCPH+BM model may provide more accurate estimation in population-level cancer study.     

援鄂医护人员急性应激反应的潜在剖面分析

目的对援鄂医护人员急性应激反应进行潜在剖面分析并探讨不同类别间人口学特征的差异。方法采用随机抽样方法选取山东、北京、福建、湖南、重庆及河南等6地区参与援鄂的医护人员627名。采用一般资料调查表、斯坦福急性应激反应量表对相关医护人员应激反应进行横断面调查,对结果进行潜在剖面分析,并探讨不同类别间人口学特征的差异。结果 627名援鄂医护人员分可为高急性应激反应(8.13%),中急性应激反应(30.62%)及低急性应激反应(61.24%)3个潜在类别。危险因素包括女性(OR=1.742)、援鄂一线医护人员(OR=3.228)、近期到过疫区(OR=2.206)、自我感觉疫情持续半年以上(OR=5.786)、年龄>60岁(OR=8.837)、援鄂一线医护人员(OR=24.315)、担心自己周围人感染的可能性(OR=13.843),均P<0.05;保护性因素为家中有医护人员(OR=0.579)、自己感染可能性比较大(OR=0.181)及感染可能性有一些(OR=0.266),均P<0.05。结论援鄂医护人员急性应激反应可分为3类,护理管理者及专业心理人员可根据其不同人口学特征实施个体化心理干预以激减轻其急性应激反应,防止过渡为急性应激障碍或创伤后应激障碍。     

Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies

In AIDS studies such as HIV viral dynamics, statistical inference is often complicated because the viral load measurements may be subject to left censoring due to a detection limit and time-varying covariates such as CD4 counts may be measured with substantial errors. Mixed-effects models are often used to model the response and the covariate processes in these studies. We propose a unified approach which addresses the censoring and measurement errors simultaneously. We estimate the model parameters by a Monte-Carlo EM algorithm via the Gibbs sampler. A simulation study is conducted to compare the proposed method with the usual two-step method and a naive method. We find that the proposed method produces approximately unbiased estimates with more reliable standard errors. A real data set from an AIDS study is analysed using the proposed method.     

A joint latent class analysis for adjusting survival bias with application to a trauma transfusion study

There is no clear classification rule to rapidly identify trauma patients who are severely hemorrhaging and may need substantial blood transfusions. Massive transfusion (MT), defined as the transfusion of at least 10 units of red blood cells within 24 h of hospital admission, has served as a conventional surrogate that has been used to develop early predictive algorithms and establish criteria for ordering an MT protocol from the blood bank. However, the conventional MT rule is a poor proxy, because it is likely to misclassify many severely hemorrhaging trauma patients as they could die before receiving the 10th red blood cells transfusion. In this article, we propose to use a latent class model to obtain a more accurate and complete metric in the presence of early death. Our new approach incorporates baseline patient information from the time of hospital admission, by combining respective models for survival time and usage of blood products transfused within the framework of latent class analysis. To account for statistical challenges, caused by induced dependent censoring inherent in 24‐h sums of transfusions, we propose to estimate an improved standard via a pseudo‐likelihood function using an expectation‐maximization algorithm with the inverse weighting principle. We evaluated the performance of our new standard in simulation studies and compared with the conventional MT definition using actual patient data from the Prospective Observational Multicenter Major Trauma Transfusion study. Copyright © 2015 John Wiley & Sons, Ltd.     

Concerns related to eating, weight, and shape: typologies and transitions in men during the college years

Objective:

How to best classify concerns related to eating, weight, and shape (CREWS) in men remains an open question. Research on men considering CREWS during different developmental periods could be particularly informative.

Method:

Focusing on one potentially dynamic developmental period, this study charts the course of CREWS in men over the college years. Latent class/latent transition analysis identified typologies of weight‐ and shape‐influenced self judgment, limiting attempts, fasting, overeating, binge eating, self‐induced vomiting, and laxative or diuretic abuse for 1,025 men over the four traditional college years.

Results:

Three classes emerged: (1) no obvious pathological eating‐related concerns (61–65%); (2) a high likelihood of limiting attempts and a moderately high likelihood of overeating (31–34%); (3) pervasive bulimic‐like concerns (4–6%). Class membership was highly stable across assessment occasions.

Discussion:

The results contribute to the growing literature on empirically derived classifications of CREWS and indicate that for many men CREWS are a chronic presence during the college years. © 2011 by Wiley Periodicals, Inc. (Int J Eat Disord 2012; 45:768–775)