Improving early phase oncology clinical trial design: The case for finding the optimal biological dose

Historically early phase oncology drug development programmes have been based on the belief that “more is better”. Furthermore, rule-based study designs such as the “3 + 3” design are still often used to identify the MTD. Phillips and Clark argue that newer Bayesian model-assisted designs such as the BOIN design should become the go to designs for statisticians for MTD finding. This short communication goes one stage further and argues that Bayesian model-assisted designs such as the BOIN12 which balances risk-benefit should be included as one of the go to designs for early phase oncology trials, depending on the study objectives. Identifying the optimal biological dose for future research for many modern targeted drugs, immunotherapies, cell therapies and vaccine therapies can save significant time and resources.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Empirical Limits for Time Series Econometric Models

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.     

Adolescents at risk of mental health problems in the COVID-19 pandemic: A prospective population-based study of the effects of government mandates and school closures

There is increasing evidence that the COVID-19 pandemic has had substantial mental health impacts for adolescents. Yet, few definitive studies have investigated which adolescents were at higher risk of poor mental health and well-being during the pandemic. Data were drawn from the Childhood to Adolescence Transition Study, a prospective cohort study of students in Australia (N = 1211). Prevalence of mental health outcomes (depressive symptoms, anxiety symptoms, self-harm and good subjective well-being) was estimated in school Years 5–12, where Years 11 (2020) and 12 (2021) coincided with the pandemic. The age- and sex-adjusted relative risk of each mental health outcome for each priority group during the pandemic were estimated. During the pandemic, over 50% of study participants reported depressive symptoms, and one quarter reported anxiety symptoms. There was a decrease in good subjective well-being compared with pre-pandemic years, while self-harm prevalence remained similar. History of mental health problems, school disengagement and frequent peer victimisation increased the risk of experiencing mental health problems during the pandemic. Schools play a central role in maintaining the mental health and good subjective well-being of students, and this is particularly important during periods of social disruption, such as the COVID-19 pandemic.     

Experiences of child welfare social workers in addressing substance use among maltreated young mothers to prevent child maltreatment

Substance use during the perinatal period and while parenting can pose a significant risk to children's safety and well-being. Mothers who have experienced child maltreatment are more likely to use substances than mothers without a history of maltreatment. This study explores how child welfare social workers experience supporting young, maltreated mothers struggling with substance use to prevent the intergenerational transmission of child maltreatment. Semi-structured in-depth interviews were conducted with four social workers working with young mothers with a history of maltreatment and substance use. Interpretative Phenomenological Analysis revealed two themes: (1) grappling with system challenges and (2) supporting strategies for disrupting intergenerational transmission of child maltreatment. The results highlight the need for systemic changes around support for social workers who work with young mothers who use substances and have a history of maltreatment, and substance use treatment and mental health programs themselves. Mothers need access to prenatal programs that are trauma-informed, non-judgemental and that support participants' basic needs and parenting skills.     

Factor structure of the Mini-Maternal Behavior Q-Sort and associations with infant attachment: Informing precision in research and intervention

We examined the factor structure of parental sensitivity to infants as assessed by the Mini-Maternal Behavior Q-Sort (Mini-MBQS), a 25-item short-form of the original 90-item MBQS. We aimed to: (1) identify latent factors of the Mini-MBQS; and (2) validate each factor by testing associations with infant attachment classifications. Data on parent-infant dyads (n = 313; 222 mothers with 281 children, 29 fathers with 32 children) were drawn from a three-generation Australian cohort study. Exploratory Factor Analysis and Exploratory Structural Equation Modelling examined the structure of the Mini-MBQS. Two latent Mini-MBQS factors were identified, requiring 8 of 25 original items: (1) Attention and Responsiveness and (2) Contingency in Interactions. Infants with insecure attachment classifications had parents with lower sensitivity across both factors relative to infants classified secure. In particular, infants with resistant attachment classifications had parents with notably low Contingency in Interactions scores. Infants with disorganised attachment classifications had parents with the lowest relative sensitivity across both factors, and in these dyads Attention and Responsiveness scores were especially low. Results provide an empirically derived factor structure for the Mini-MBQS. Two subscales, each with significant infant attachment associations, may improve precision in clinical intervention and research translation.     

Nonstationary Binary Choice

This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood (ML) estimator is consistent but a new phenomenon arises in its limit distribution theory. The estimator consists of a mixture of two components, one of which is parallel to and the other orthogonal to the direction of the true parameter vector, with the latter being the principal component. The ML estimator is shown to converge at a rate of n^3/4 along its principal component but has the slower rate of n^1/4 convergence in all other directions. This is the first instance known to the authors of multiple convergence rates in models where the regressors have the same (full rank) stochastic order and where the parameters appear in linear forms of these regressors. It is a consequence of the fact that the estimating equations involve nonlinear integrable transformations of linear forms of integrated processes as well as polynomials in these processes, and the asymptotic behavior of these elements is quite different. The limit distribution of the ML estimator is derived and is shown to be a mixture of two mixed normal distributions with mixing variates that are dependent upon Brownian local time as well as Brownian motion. It is further shown that the sample proportion of binary choices follows an arc sine law and therefore spends most of its time in the neighborhood of zero or unity. The result has implications for policy decision making that involves binary choices and where the decisions depend on economic fundamentals that involve stochastic trends. Our limit theory shows that, in such conditions, policy is likely to manifest streams of little intervention or intensive intervention.