A comparison of chi-square partitioning and two logit analyses of ordinal pain data from a pharmaceutical study

We study three approaches for the analysis of ordinal pain data. The first is essentially non-parametric, based on partitioning the goodness-of-fit statistic for testing the homogeneity model. The other two involve the modelling of quite different logit functions of the multinomial probabilities. We discuss and compare the characteristics of these three approaches using actual data from an analgesic trial.     

Challenges and Applications of the Transtheoretical Model in Patients with Diabetes Mellitus

The transtheoretical model of behavior change has become one of the most influential theoretical models within health psychology. Characterized by treating behavior change as a dynamic process, it has recently been applied to diabetes mellitus. Both interventional and observational studies have applied the transtheoretical model to the care of patients with diabetes in multiple settings; the studies that have been performed have been very heterogeneous in nature. Pooled data from these studies yielded a distribution that describes patients across the stages of change, the main construct of the transtheoretical model. Regarding treatment effectiveness, the empirical data reported in the studies showed that participants did progress through the stages of change when a transtheoretical model-based intervention was applied, and that a higher percentage of participants progressed in the intervention groups than in the control groups. Further research is needed in this field to obtain more information about the constructs of the transtheoretical model and the stages of change. The use of validated questionnaires in the assessment process is also recommended. The studies included in this review show how the transtheoretical model has made a positive contribution to interventions in the field of diabetes.     

Changes in the "stages of change" as outcome measures of a smoking cessation intervention: a randomized controlled trial

OBJECTIVE: Employing changes in the "stages of change" as alternative outcome measures of a smoking cessation intervention. METHOD: RCT carried out between 2002 and 2004 in Germany with 485 daily cigarette smokers recruited from a general population survey. The intervention was conceptually based on the transtheoretical model, involving individualized feedback letters generated by computerized expert-system technology. RESULTS: Latent transition analysis was applied to test models for patterns of change, to evaluate stage of change distributions and transitions over time, and to compare the intervention and control group. The patterns of change across three time points could best be described by a model including stability, one-stage regressions, and one-to-four-stage progressions. There were no significant differences between the intervention and control group in the stage of change distributions at each time point or in the amount of stage transitions over time. CONCLUSION: The efficacy of the intervention could not be demonstrated. Reasons may include oversampling of smokers who are interested in health-related topics but do not intend to quit, and power problems due to substantial attrition.     

A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified

Understanding the dynamic disease process is vital in early detection, diagnosis, and measuring progression. Continuous‐time Markov chain (CTMC) methods have been used to estimate state‐change intensities but challenges arise when stages are potentially misclassified. We present an analytical likelihood approach where the hidden state is modeled as a three‐state CTMC model allowing for some observed states to be possibly misclassified. Covariate effects of the hidden process and misclassification probabilities of the hidden state are estimated without information from a ‘gold standard’ as comparison. Parameter estimates are obtained using a modified expectation‐maximization (EM) algorithm, and identifiability of CTMC estimation is addressed. Simulation studies and an application studying Alzheimer's disease caregiver stress‐levels are presented. The method was highly sensitive to detecting true misclassification and did not falsely identify error in the absence of misclassification. In conclusion, we have developed a robust longitudinal method for analyzing categorical outcome data when classification of disease severity stage is uncertain and the purpose is to study the process' transition behavior without a gold standard. Copyright © 2016 John Wiley & Sons, Ltd.     

Fitting and interpreting continuous‐time latent Markov models for panel data

Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous‐time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi‐Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration‐dependent disease state sojourn distributions. We have developed a robust and efficient expectation–maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data‐generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting. Copyright © 2013 John Wiley & Sons, Ltd.     

基于跨理论模型干预对提升肥胖儿童自尊的作用

目的探索跨理论模型(TTM)在肥胖儿童自尊水平干预中的效果。方法采用类实验性研究,经随机抽样在3～6年级中抽取73名肥胖儿童进行干预,运用变化阶段量表和自尊量表对其进行评估(基线评估),根据评估结果进行基于TTM的体育锻炼及心理干预指导,分别在干预后1个月、6个月对研究对象进行跟进评估,分析干预前后肥胖儿童锻炼变化阶段的分布情况及自尊得分和BMI值的变化。结果干预前后,前意向阶段和保持阶段的肥胖儿童人数分布差异有统计学意义(P<0.001),自尊得分和BMI值差异无统计学意义,自尊得分在五个锻炼变化阶段间差异均有统计学意义(P<0.001),BMI值在五个变化阶段间差异无统计学意义。结论处于锻炼较高阶段的儿童自尊得分高于低阶段的儿童,TTM干预可以促使肥胖儿童从前一阶段向下一阶段转变。     

Change for Life/Cambia tu vida: A health promotion program based on the stages of change model for African descendent and Latino adults in New Hampshire

Studies have shown that diabetes and cardiovascular disease can be controlled and prevented through the modification of behavioral risk factors. The transtheoretical model of behavior change, also known as the stages of change model, offers promise for designing behavior change interventions. However, this model has rarely been applied in group settings with minority communities. To address racial and ethnic disparities related to the risk for diabetes and cardiovascular disease, the New Hampshire REACH 2010 Initiative has designed and implemented Change for Life/Cambia tu vida, a health promotion program based on the stages of change model for African descendent and Latino residents of southern New Hampshire. The program guides participants through the five stages of change and provides resources to support healthy behavior change. We also sponsor periodic class reunions that help program graduates to maintain these healthy habits. This article describes curriculum development, participant feedback, and early pretest and posttest evaluation results from a standardized assessment.     

Patients' readiness for dietary change at the beginning of counselling: a transtheoretical model-based assessment

Background The purpose of this study was to explore patients' readiness for dietary change within a theoretical framework of the transtheoretical model. The patients were recently diagnosed to have type 2 diabetes or impaired glucose tolerance. We discuss the theoretical aspects of appropriate dietary counselling strategies from a standpoint of patient‐specific stages of change. Methods The data included 32 audiotaped diabetes counselling sessions with 16 patients conducted by two nurses. The transcribed data was analysed by using deductive content analysis. Results The patients were at different stages of change of diabetes‐affected dietary behaviour. Their stages of change varied in different dietary areas and within certain dietary habits. These stages of change could involve their overall dietary behaviour or some minor aspects of their diets. Conclusions Understanding patient‐specific stages of change orientates health counsellors to use the most appropriate counselling strategies. The transtheoretical framework helps counsellors to perceive the total range of patients' different stages of change and their effect on the implementation of counselling. However, determining patients' stages of change through examining counselling conversations is occasionally difficult. Further qualitative research is called for.     

Predicting longitudinal trajectories of health probabilities with random‐effects multinomial logit regression

Researchers often encounter longitudinal health data characterized with three or more ordinal or nominal categories. Random‐effects multinomial logit models are generally applied to account for potential lack of independence inherent in such clustered data. When parameter estimates are used to describe longitudinal processes, however, random effects, both between and within individuals, need to be retransformed for correctly predicting outcome probabilities. This study attempts to go beyond existing work by developing a retransformation method that derives longitudinal growth trajectories of unbiased health probabilities. We estimated variances of the predicted probabilities by using the delta method. Additionally, we transformed the covariates’ regression coefficients on the multinomial logit function, not substantively meaningful, to the conditional effects on the predicted probabilities. The empirical illustration uses the longitudinal data from the Asset and Health Dynamics among the Oldest Old. Our analysis compared three sets of the predicted probabilities of three health states at six time points, obtained from, respectively, the retransformation method, the best linear unbiased prediction, and the fixed‐effects approach. The results demonstrate that neglect of retransforming random errors in the random‐effects multinomial logit model results in severely biased longitudinal trajectories of health probabilities as well as overestimated effects of covariates on the probabilities. Copyright © 2012 John Wiley & Sons, Ltd.     

The ‘stages of change’ model in health promotion: Science and Ideology

The transtheoretical or ‘stages of change’ model has greatly influenced health promotion practice in the USA, Australia and the UK since the late 1980s. Application of the model has shaped service planning, provision and implementation. ‘Stages of change’ also has impacted on training agendas at local, regional and national levels. Associated areas of motivational interviewing and brief intervention have led health promotion initiatives in areas such as smoking cessation and alcohol-reduction policies. A number of critiques have recently challenged the ‘stages of change’ model and health promotion orthodoxy. This review examines these critiques, with a focus on the ‘scientific’ status of ‘stages of change’. The review also examines a data-based approach to stages of change, model adequacy, the social and ideological context of change theories in health promotion contexts, levels of explanation and prediction in the ‘stages of change’ framework. Some reasons are offered for the apparent popularity of the model amongst health promotion workers. Recommendations are made about alternative conceptual frameworks.     

Modeling and inference for an ordinal effect size measure

An ordinal measure of effect size is a simple and useful way to describe the difference between two ordered categorical distributions. This measure summarizes the probability that an outcome from one distribution falls above an outcome from the other, adjusted for ties. We develop and compare confidence interval methods for the measure. Simulation studies show that with independent multinomial samples, confidence intervals based on inverting the score test and a pseudo-score-type test perform well. This score method also seems to work well with fully-ranked data, but for dependent samples a simple Wald interval on the logit scale can be better with small samples. We also explore how the ordinal effect size measure relates to an effect measure commonly used for normal distributions, and we consider a logit model for describing how it depends on explanatory variables. The methods are illustrated for a study comparing treatments for shoulder-tip pain.     

Application of the transtheoretical model for HIV prevention in a facility-based and a community-level behavioral intervention research study

This article describes the application of the transtheoretical model of behavior change to prevention programs for women at risk for or infected with HIV. The focus of these multisite demonstration projects was to increase condom and contraceptive use. The model was operationalized for use in the following two different intervention approaches: facility-based interventions (individual counseling for women in clinics, shelters, and drug treatment centers) and community-level interventions (including production of small media materials, street outreach, and community mobilization). The authors found that interventions derived from a complex theory can be disseminated to frontline providers who have little prior HIV education experience or academic training. They suggest that the transtheoretical model has value for the design and implementation of HIV prevention programs.     

Neighbourhood food environments and obesity in southeast Louisiana

Supermarkets might influence food choices, and more distal outcomes like obesity, by increasing the availability of healthy foods. However, recent evidence about their effects is ambiguous, perhaps because supermarkets also increase the availability of unhealthy options. We develop an alternative measure of food environment quality that characterizes urban neighborhoods by the relative amounts of healthy (e.g. fruits and vegetables) to unhealthy foods (e.g. energy-dense snacks). Using data from 307 food stores and 1243 telephone interviews with residents in urban southeastern Louisiana, we estimate a multilevel multinomial logistic model for overweight status. We find that higher quality food environments - but not food store types - decrease the risk of obesity (RR 0.474, 95% CI 0.269-0.835) and overweight (RR 0.532, 95% CI 0.312-0.907). The findings suggest a need to move beyond a sole consideration of food store types to a more nuanced view of the food environment when planning for change.     

Estimating net transition probabilities from cross-sectional data with application to risk factors in chronic disease modeling

A problem occurring in chronic disease modeling is the estimation of transition probabilities of moving from one state of a categorical risk factor to another. Transitions could be obtained from a cohort study, but often such data may not be available. However, under the assumption that transitions remain stable over time, age specific cross-sectional prevalence data could be used instead. Problems that then arise are parameter identifiability and the fact that age dependent cross-sectional data are often noisy or are given in age intervals. In this paper we propose a method to estimate so-called net annual transition probabilities from cross-sectional data, including their uncertainties. Net transitions only describe the net inflow or outflow into a certain risk factor state at a certain age. Our approach consists of two steps: first, smooth the data using multinomial P-splines, second, from these data estimate net transition probabilities. This second step can be formulated as a transportation problem, which is solved using the simplex algorithm from linear programming theory. A sensible specification of the cost matrix is crucial to get meaningful results. Uncertainties are assessed by parametric bootstrapping. We illustrate our method using data on body mass index. We conclude that this method provides a flexible way of estimating net transitions and that the use of net transitions has implications for model dynamics, for example when modeling interventions.     

Multinomial regression models based on continuation ratios

This paper concerns continuation ratio models for multinomial responses. These are conditional probabilities used in logit models to define the dependence of the multinomial proportions on explanatory variables and unknown parameters. A distinctive feature of these models is that if one models the various continuation ratios separately, then resulting estimates and test statistics are asymptotically independent. This allows the partitioning of likelihood ratio statistics and the search for effects in specific categories of an ordinal response variable. Models that use the same parameters for different continuation ratios are suitable for estimating more global differences. The fitting of these models to actual data is illustrated, including an example from a pharmaceutical study. The results show that different models are suitable for modelling complementary sorts of differences between multinomial response distributions.     

Obesity management in primary care: assessment of readiness to change among 284 family practice patients

BACKGROUND: Most adults in primary care are overweight or obese; two thirds of patients with weight problems have other obesity-related conditions. The study objective was to explore the feasibility of a primary care obesity intervention based on the transtheoretical model (TM) of behavior change and principles of chronic disease (CD) care. METHODS: A prospective study of the initial version of the TM-CD intervention with obese family practice patients (n = 284) yielded cross-sectional data on baseline stage of change for six target behaviors: dietary fat, portion control, vegetable intake, fruit intake, usual physical activity, and planned exercise. The sample consisted of obese patients scheduled for an office visit during times when recruitment and informed consent did not conflict with acute care. RESULTS: Obese patients volunteering for a TM-CD program are in different stages of change for six target behaviors. Preparation was the most frequently reported stage for increased exercise (49%) or activity (34%), decreased dietary fat consumption (44%), and increased portion control (51%). Patients in a particular stage for one behavior were distributed across all five stages for another behavior. Stage of change for five target behaviors was associated with body mass index or waist girth (P < .05) in a manner consistent with stage-of-change theory. CONCLUSIONS: Using the transtheoretical model of behavior change will allow physicians to recognize when obese patients are receptive to specific behavioral interventions.     

Testing the predictive power of the transtheoretical model of behavior change applied to dietary fat intake

This study evaluated how well predictions from the transtheoretical model (TTM) generalized from smoking to diet. Longitudinal data were used from a randomized control trial on reducing dietary fat consumption in adults (n =1207) recruited from primary care practices. Predictive power was evaluated by making a priori predictions of the magnitude of change expected in the TTM constructs of temptation, pros and cons, and 10 processes of change when an individual transitions between the stages of change. Generalizability was evaluated by testing predictions based on smoking data. Three sets of predictions were made for each stage: Precontemplation (PC), Contemplation (C) and Preparation (PR) based on stage transition categories of no progress, progress and regression determined by stage at baseline versus stage at the 12-month follow-up. Univariate analysis of variance between stage transition groups was used to calculate the effect size [omega squared (omega(2))]. For diet predictions based on diet data, there was a high degree of confirmation: 92%, 95% and 92% for PC, C and PR, respectively. For diet predictions based on smoking data, 77%, 79% and 85% were confirmed, respectively, suggesting a moderate degree of generalizability. This study revised effect size estimates for future theory testing on the TTM applied to dietary fat.     

The effect of waiting time and distance on hospital choice for English cataract patients

This paper applies latent-class multinomial logit models to the choice of hospital for cataract operations in the UK NHS. We concentrate on the effects of travel time and waiting time and especially on estimating the waiting time elasticity of demand. Models including hospital fixed effects rely on changes over time in waiting time to indentify coefficients. We show how using a latent-class multinomial logit model characterises the unobserved heterogeneity in GP practices' choice behaviour and affects the estimated elasticities of travel time and waiting time. The models estimate waiting time elasticities of demand of approximately -0.1, comparable with previous waiting time-demand models. For the average waiting time elasticity, the simple multinomial logit models are good approximations of the latent-class logit results.     

Modelling decay in effectiveness for evaluation of behaviour change interventions: a tutorial for public health economists

Background and purpose

Recent methodological reviews of evaluations of behaviour change interventions in public health have highlighted that the decay in effectiveness over time has been mostly overlooked, potentially leading to suboptimal decision-making. While, in principle, discrete-time Markov chains—the most commonly used modelling approach—can be adapted to account for decay in effectiveness, this framework inherently lends itself to strong model simplifications. The application of formal and more appropriate modelling approaches has been supported, but limited progress has been made to date. The purpose of this paper is to encourage this shift by offering a practical guide on how to model decay in effectiveness using a continuous-time Markov chain (CTMC)-based approach.

Methods

A CTMC approach is demonstrated, with a contextualized tutorial being presented to facilitate learning and uptake. A worked example based on the stylized case study in physical activity promotion is illustrated with accompanying R code.

Discussion

The proposed framework presents a relatively small incremental change from the current modelling practice. CTMC represents a technical solution which, in absence of relevant data, allows for formally testing the sensitivity of results to assumptions regarding the long-term sustainability of intervention effects and improving model transparency.

Conclusions

The use of CTMC should be considered in evaluations where decay in effectiveness is likely to be a key factor to consider. This would enable more robust model-based evaluations of population-level programmes to promote behaviour change and reduce the uncertainty surrounding the decision to invest in these public health interventions.