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1. Teaching little kids big sentences: A randomized controlled trial showing that children with DLD respond to complex syntax intervention embedded within the context of preschool/kindergarten science instruction

   https://doi.org/10.1111/1460-6984.12882  

   Owen Van Horne, Amanda J. ; Curran, Maura ; Cook, Susan Wagner ; Cole, Renée ; McGregor, Karla K. ( May 2023 , International Journal of Language & Communication Disorders)

   The language of the science curriculum is complex, even in the early grades. To communicate their scientific observations, children must produce complex syntax, particularly complement clauses (e.g.,I think it will float;We noticed that it vibrates). Complex syntax is often challenging for children with developmental language disorder (DLD), and thus their learning and communication of science may be compromised.

   We asked whether recast therapy delivered in the context of a science curriculum led to gains in complement clause use and scientific content knowledge. To understand the efficacy of recast therapy, we compared changes in science and language knowledge in children who received treatment for complement clauses embedded in a first‐grade science curriculum to two active control conditions (vocabulary + science, phonological awareness + science).

   This 2‐year single‐site three‐arm parallel randomized controlled trial was conducted in Delaware, USA. Children with DLD, not yet in first grade and with low accuracy on complement clauses, were eligible. Thirty‐three 4–7‐year‐old children participated in the summers of 2018 and 2019 (2020 was cancelled due to COVID‐19). We assigned participants to arms using 1:1:1 pseudo‐random allocation (avoiding placing siblings together). The intervention consisted of 39 small‐group sessions of recast therapy, robust vocabulary instruction or phonological awareness intervention during eight science units over 4 weeks, followed by two science units (1 week) taught without language intervention. Pre‐/post‐measures were collected 3 weeks before and after camp by unmasked assessors.

   Primary outcome measures were accuracy on a 20‐item probe of complement clause production and performance on ten 10‐item unit tests (eight science + language, two science only). Complete data were available for 31 children (10 grammar, 21 active control); two others were lost to follow‐up. Both groups made similar gains on science unit tests for science + language content (pre versus post,d= 2.9,p< 0.0001; group,p= 0.24). The grammar group performed significantly better at post‐test than the active control group (d= 2.5,p= 0.049) on complement clause probes and marginally better on science‐only unit tests (d= 2.5,p= 0.051).

   Children with DLD can benefit from language intervention embedded in curricular content and learn both language and science targets taught simultaneously. Tentative findings suggest that treatment for grammar targets may improve academic outcomes.

   We know that recast therapy focused on morphology is effective but very time consuming. Treatment for complex syntax in young children has preliminary efficacy data available. Prior research provides mixed evidence as to children’s ability to learn language targets in conjunction with other information.

   This study provides additional data supporting the efficacy of intensive complex syntax recast therapy for children ages 4–7 with Developmental Language Disorder. It also provides data that children can learn language targets and science curricular content simultaneously.

   As SLPs, we have to talk about something to deliver language therapy; we should consider talking about curricular content. Recast therapy focused on syntactic frames is effective with young children.

    

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2. Sample size re‐estimation in Phase 2 dose‐finding: Conditional power versus Bayesian predictive power

   https://doi.org/10.1002/pst.2275  

   Liu, Qingyang ; Hu, Guanyu ; Ye, Binqi ; Wang, Susan ; Wu, Yaoshi ( November 2022 , Pharmaceutical Statistics)

   Unblinded sample size re‐estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof‐of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two‐stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi‐arm trials.

    

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3. Using Post-Outcome Measurement Information in Censoring-by-Death Problems

   https://doi.org/10.1111/rssb.12113  

   Yang, Fan ; Small, Dylan S. ( April 2015 , Journal of the Royal Statistical Society Series B: Statistical Methodology)

   Many clinical studies on non-mortality outcomes such as quality of life suffer from the problem that the non-mortality outcome can be censored by death, i.e. the non-mortality outcome cannot be measured if the subject dies before the time of measurement. To address the problem that this censoring by death is informative, it is of interest to consider the average effect of the treatment on the non-mortality outcome among subjects whose measurement would not be censored under either treatment or control, which is called the survivor average causal effect (SACE). The SACE is not point identified under usual assumptions but bounds can be constructed. The previous literature on bounding the SACE uses only the survival information before the measurement of the non-mortality outcome. However, survival information after the measurement of the non-mortality outcome could also be informative. For randomized trials, we propose a set of ranked average score assumptions that make use of survival information before and after the measurement of the non-mortality outcome which are plausibly satisfied in many studies and we develop a two-step linear programming approach to obtain the closed form for bounds on the SACE under our assumptions. We also extend our method to randomized trials with non-compliance or observational studies with a valid instrumental variable to obtain bounds on the complier SACE which is presented in on-line supplementary material. We apply our method to a randomized trial of the effect of mechanical ventilation with lower tidal volume versus traditional tidal volume for acute lung injury patients. Our bounds on the SACE are much shorter than the bounds that are obtained by using only the survival information before the measurement of the non-mortality outcome.

    

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4. Causal inference over stochastic networks

   https://doi.org/10.1093/jrsssa/qnae001  

   Clark, Duncan A. ; Handcock, Mark S. ( January 2024 , Journal of the Royal Statistical Society Series A: Statistics in Society)

   Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks.

    

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5. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference

   https://doi.org/10.1111/biom.12102  

   Li, Shi ; Mukherjee, Bhramar ; Batterman, Stuart ; Ghosh, Malay ( November 2013 , Biometrics)

   Case-crossover designs are widely used to study short-term exposure effects on the risk of acute adverse health events. While the frequentist literature on this topic is vast, there is no Bayesian work in this general area. The contribution of this paper is twofold. First, the paper establishes Bayesian equivalence results that require characterization of the set of priors under which the posterior distributions of the risk ratio parameters based on a case-crossover and time-series analysis are identical. Second, the paper studies inferential issues under case-crossover designs in a Bayesian framework. Traditionally, a conditional logistic regression is used for inference on risk-ratio parameters in case-crossover studies. We consider instead a more general full likelihood-based approach which makes less restrictive assumptions on the risk functions. Formulation of a full likelihood leads to growth in the number of parameters proportional to the sample size. We propose a semi-parametric Bayesian approach using a Dirichlet process prior to handle the random nuisance parameters that appear in a full likelihood formulation. We carry out a simulation study to compare the Bayesian methods based on full and conditional likelihood with the standard frequentist approaches for case-crossover and time-series analysis. The proposed methods are illustrated through the Detroit Asthma Morbidity, Air Quality and Traffic study, which examines the association between acute asthma risk and ambient air pollutant concentrations.

    

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