John Joseph Dziak

• Ph.D.
• Research Associate at Pennsylvania State University

Background Approximately ten percent of US military veterans suffer from posttraumatic stress disorder (PTSD). Cognitive processing therapy (CPT) is a highly effective, evidence-based, first-line treatment for PTSD that has been widely adopted by the Department of Veterans Affairs (VA). CPT consists of discrete therapeutic components delivered acro...

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to sequence and match interventions to the unique, changing needs of individuals. A variety of sample size planning res...

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is...

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person’s daily emo...

Objective Insufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whet...

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emo...

The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the...

Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of int...

Objective: While self-monitoring can help mitigate alcohol misuse in young adults, engagement with digital self-monitoring is suboptimal. The present study investigates the utility of two types of digital prompts (reminders) to encourage young adults to self-monitor their alcohol use. These prompts leverage information that is self-relevant (i.e.,...

Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been deve...

The increase in the use of mobile and wearable devices now allow dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the m...

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how best to sequence and match interventions to the unique and changing needs of individuals. A variety of sample size calc...

In many health domains such as substance-use, outcomes are often counts with an excessive number of zeros (EZ) - count data having zero counts at a rate significantly higher than that expected of a standard count distribution (e.g., Poisson). However, an important gap exists in sample size estimation methodology for planning sequential multiple ass...

Post-hoc power estimates (power calculated for hypothesis tests after performing them) are sometimes requested by reviewers in an attempt to promote more rigorous designs. However, they should never be requested or reported because they have been shown to be logically invalid and practically misleading. We review the problems associated with post-h...

Information criteria (ICs) based on penalized likelihood, such as Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions abou...

Introduction: Although much of the work on risky alcohol use behaviors, such as heavy drinking, focuses on adolescence and young adulthood, these behaviors are associated with negative health consequences across all ages. Existing studies on age trends have focused on a single alcohol use behavior across many ages, using methods such as time-varyi...

In recent years, there has been increased interest in the development of adaptive interventions across various domains of health and psychological research. An adaptive intervention is a protocolized sequence of individualized treatments that seeks to address the unique and changing needs of individuals as they progress through an intervention prog...

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outc...

Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression functi...

Aims: Alcohol use disorders (AUDs) are linked with numerous severe detrimental outcomes. Evidence suggests that there is a typology of individuals with an AUD based on the symptoms they report. Scant research has identified how these groups may vary in prevalence by age, which could highlight aspects of problematic drinking behavior that are parti...

Information criteria (ICs) based on penalized likelihood, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions abo...

Cost-effectiveness—increasing the benefit obtained for a given expenditure of time or money—is an important idea in many applied research fields. It is one important quality that a researcher interested in the multiphase optimization strategy (MOST) may wish to optimize. However, further research is needed about how to best incorporate cost informa...

Factorial designs are one of the many useful experimental tools that can be used to inform the construction of multicomponent behavioral, biobehavioral, and biomedical interventions. Clustering presents various challenges to investigators aiming to implement such designs. Clustering means that some or all individuals are nested in higher-level soci...

This chapter is intended to describe the differences between effect coding and dummy coding when the multiple regression approach is used to perform analysis of variance (ANOVA) with balanced (i.e., an equal number of subjects in each experimental condition) factorial designs. Using a hypothetical example of a 2³ factorial experiment, we present th...

The collection of articles in this special issue focus on latent variable mixture models including latent class analysis (LCA), latent profile analysis (LPA), and latent transition analysis (LTA). These are all methods for summarizing observed variables by postulating an underlying categorical latent variable representing a type or status; in the c...

Latent class analysis (LCA) has proven to be a useful tool for identifying qualitatively different population subgroups who may be at varying levels of risk for negative outcomes. Recent methodological work has improved techniques for linking latent class membership to distal outcomes; however, these techniques do not adjust for potential confoundi...

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailo...

Researchers often build regression models to relate a response to a set of predictor variables. In some cases, there are predictors that apply to some participants, or to some measurement occasions, but not others. For example, a romantic partner's substance use may be a key predictor of one's own substance use. However, not all participants have a...

Aims: To identify subgroups of adult drinkers characterized by typical drinking patterns. Methods: We used data from the National Epidemiologic Survey on Alcohol and Related Conditions-III to classify drinkers based on several indicators of drinking. Past-year drinkers aged 18-64 were included (n = 22,776). Results: Latent class analysis revea...

Both daily stress and the tendency to react to stress with heightened levels of negative affect (i.e., stress sensitivity) are important vulnerability factors for adverse mental health outcomes. Mindfulness-based stress reduction (MBSR) may help to reduce perceived daily stress and stress sensitivity. The purpose of this study was to examine how ch...

Factorial experimental designs have many applications in the behavioral sciences. In the context of intervention development, factorial experiments play a critical role in building and optimizing high-quality, multicomponent behavioral interventions. One challenge in implementing factorial experiments in the behavioral sciences is that individuals...

Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004...

Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories o...

The purpose of this study was to describe historical trends in rates of recent substance use and associations between marijuana and other substances, among U.S. high school seniors by race and gender. Data from Monitoring the Future (1976-2013; N = 599,109) were used to estimate historical trends in alcohol use, heavy episodic drinking (HED), cigar...

\item Dziak, J. J., Coffman, D. L., Lanza, S. T., and Li, R. (2012). Sensitivity and specificity of information criteria (Methodology Center Technical Report 12-119). University Park, PA: The Methodology Center, The Pennsylvania State University. Available at http://methodology.psu.edu/media/techreports/12-119.pdf and https://peerj.com/preprints/11...

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailo...

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can t the observed data very well, but be too closely tailore...

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailo...

Ordinal responses are very common in longitudinal data collected from substance abuse research or other behavioral research. This study develops a new statistical model with free SAS macros that can be applied to characterize time-varying effects on ordinal responses. Our simulation study shows that the ordinal-scale time-varying effects model has...

Selecting the number of different classes which will be assumed to exist in the population is an important step in latent class analysis (LCA). The bootstrap likelihood ratio test (BLRT) provides a data-driven way to evaluate the relative adequacy of a (K -1)-class model compared to a K-class model. However, very little is known about how to predic...

Background: An understanding of the individual and combined effects of a set of intervention components is important for moving the science of preventive medicine interventions forward. This understanding can often be achieved in an efficient and economical way via a factorial experiment, in which two or more independent variables are manipulated....

An important goal of prevention research is to predict a range of outcomes (e.g., smoking, drinking, injury etc.). Statistically, prediction can be expressed as a regression model, with an outcome of interest regressed on predictors. The model set-up is such that both an outcome and predictors are measured on a similar frequency scale. If frequenci...

This technology demonstration presents recent advances in SAS, R, STATA, and web-based software developed at The Methodology Center at Penn State. New and updated SAS procedures, R packages, STATA plug-ins, and free-standing web applets for latent class and latent transition analysis (LCA/LTA), causal inference, adaptive interventions, intensive lo...

This technology demonstration presents recent advances in SAS, R, and Stata software developed at The Methodology Center at Penn State. New and updated SAS procedures, R packages, Stata plug-ins, and free-standing applets for latent class analysis (LCA), missing data, adaptive interventions, and ecological momentary assessments (EMA, also called in...

Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention...

An investigator who plans to conduct an experiment with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Consider...

Correlated data, mainly including longitudinal data, panel data, functional data and repeated measured data, are common in the fields of biomedical research, environmental studies, econometrics and the social sciences. Various statistical procedures have been proposed for analysis of correlated data in the literature. This chapter intends to provid...

During the past two decades, there have been many new developments in longitudinal data analysis. Authors have made many efforts on developing diverse models, along with inference procedures, for longitudinal data. More recently, researchers in longitudinal modeling have begun addressing the vital issue of variable selection. Model selection criter...