Assessing the treatment effects in apraxia of speech: introduction and evaluation of the Modified Diadochokinesis Test

Background: The number of reliable and valid instruments to measure the effects of therapy in apraxia of speech (AoS) is limited. Aims: To evaluate the newly developed Modified Diadochokinesis Test (MDT), which is a task to assess the effects of rate and rhythm therapies for AoS in a multiple baseline across behaviours design. Methods: The consistency, accuracy and fluency of speech of 24 adults with AoS and 12 unaffected speakers matched for age, gender and educational level were assessed using the MDT. The reliability and validity of the instrument were considered and outcomes compared with those obtained with existing tests. Results: The results revealed that MDT had a strong internal consistency. Scores were influenced by syllable structure complexity, while distinctive features of articulation had no measurable effect. The testretest and intra- and inter-rater reliabilities were shown to be adequate, and the discriminant validity was good. For convergent validity different outcomes were found: apart from one correlation, the scores on tests assessing functional communication and AoS correlated significantly with the MDT outcome measures. The spontaneous speech phonology measure of the Aachen Aphasia Test (AAT) correlated significantly with the MDT outcome measures, but no correlations were found for the repetition subtest and the spontaneous speech articulation/prosody measure of the AAT. Conclusions & Implications: The study shows that the MDT has adequate psychometric properties, implying that it can be used to measure changes in speech motor control during treatment for apraxia of speech. The results demonstrate the validity and utility of the instrument as a supplement to speech tasks in assessing speech improvement aimed at the level of planning and programming of speech

Chemical Enhancement of Bloody Footwear Impressions from Buried Substrates

Footwear impressions are regarded as one of the most common forensic evidence types left at crime scenes. A review of research to date describes previous tests on the survival of footwear impressions in a range of contaminants on a myriad of surfaces. None, however, examined the effects of the burial environment on such impressions. Using human blood as a contaminant, footwear impressions were made on samples of white cotton, newspaper, and black plastic trash bags and were buried for specific time frames, from one to four weeks. The study examines the subsequent development of the surviving impressions postexcavation, using chemical enhancement techniques of ninhydrin, acid black 1, leucocrystal violet (LCV), and Bluestar. The majority of impressions recovered were from the substrates that were in the soil for the shortest period. Poor recovery rates and loss of impressions were observed on substrates buried for more than two weeks. LCV and Bluestar proved most effective for enhancing and retrieving impressions. Impressions were able to be examined by a trained forensic footwear investigator to identify class, individual, and wear characteristics of the impression itself. Potential survival of such identifying features is of paramount importance to an investigation

The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy

Background: There are numerous non-biologic and biologic disease-modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. Methods: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every 6\ua0months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. Results: 7300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. Eighty-two percent of TCZm use began within 3\ua0years of starting any bDMARD. Ninety-three percent of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with the use of TCZm included prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. Conclusions: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data

Patrones de interacción verbal en el contexto clínico: un modelo de cómo la gente cambia en terapia

Antecedentes: el artículo publicado en esta revista “¿Por qué la gente cambia en terapia? Un estudio preliminar” (2006) supuso el inicio de una línea de investigación basada en metodología observacional, dirigida a clarifi car el proceso terapéutico. A lo largo de estos años han sido grandes los avances en la explicación del cambio clínico. En este artículo se presenta una síntesis de esta línea de investigación, aportando una serie de conclusiones que, en cierta medida, dan respuesta a muchos de los interrogantes que presentábamos en ese primer trabajo al que hacíamos referencia. Método: se registró la conducta verbal de terapeutas y clientes en 92 sesiones clínicas, mediante el sistema de categorización de la interacción de la conducta verbal en terapia (SISC- INTER- CVT). A continuación, se realizó un análisis descriptivo y secuencial de las observaciones. Resultados: los datos mostraron la existencia de ciertos patrones de interacción verbal, relacionados con las actividades clínicamente relevantes desempeñadas por el terapeuta, a partir de los cuales se desarrolló un modelo de interacción verbal en el contexto clínico. Conclusiones: el análisis funcional de la interacción verbal terapeuta-cliente resulta imprescindible para comprender los procesos que explican el cambio clínico y aumentar la calidad de la terapia psicológicaBackground: The paper “Why do people change in therapy? A preliminary study” (2006), published in this journal, led to the beginning of a line of research based on observational methodology and aimed at the clarifi cation of the therapeutic process. Throughout these years, signifi cant progress has been made towards an explanation of clinical change. In this paper, a synthesis of this line of research is presented, along with a series of conclusions that can, to some extent, provide an answer to the questions we posed in the aforementioned fi rst paper. Method: Verbal behavior both of therapist and client was coded for 92 clinical sessions using the Verbal Behavior Interaction Category System (SISC-INTER-CVT). Descriptive and sequential analyses of the observations were then performed. Results: The data show the existence of certain patterns of verbal interaction that are related to the clinically relevant activities undertaken by the therapist, from which a model for verbal interaction in the clinical context was developed. Conclusions: The functional analysis of the therapist-client verbal interaction is essential for the comprehension of the processes that explain clinical change as well as for the improvement of the quality of psychological therapyFinancial support was received from the Spanish Government (Science and Innovation Ministry, I +D+ I Research Grant, SEJ2007-66537-PSIC, PSI2010-15908

A Non-Experimental Evaluation of Curriculum Effectiveness in Math

We use non-experimental data from a large panel of schools and districts in Indiana to evaluate the impacts of math curricula on student achievement. Using matching methods, we obtain causal estimates of curriculum effects at just a fraction of what it would cost to produce experimental estimates. Furthermore, external validity concerns that are particularly cogent in experimental curricular evaluations suggest that our non-experimental estimates may be preferred. In the short term, we find large differences in effectiveness across some math curricula. However, as with many other educational inputs, the effects of math curricula do not persist over time. Across curriculum adoption cycles, publishers that produce less effective curricula in one cycle do not lose market share in the next cycle. One explanation for this result is the dearth of information available to administrators about curricular effectiveness

Detecting risk for treatment nonresponse among families of young children with behavior problems: Candidate tailoring variables and early decision points for adaptive interventions

Heterogeneity in mental health treatment outcomes and high rates of treatment nonresponse highlight the need for adaptive interventions that align with precision mental health care approaches to tailor treatments according to individual differences in progress over time. Modern clinical trial methodologies and analytic strategies can inform dynamic mental health treatment decisions, but the potential to improve patient outcomes is only as strong as the extent to which selected tailoring variables (i.e., interim response factors that dictate whether treatment should shift course) accurately detect risk for treatment nonresponse. Identifying empirically informed tailoring variables and the most appropriate timepoint(s) to assess them (i.e., critical decision points) is essential in order to design adaptive interventions. This dissertation is comprised of three manuscripts focused on the use of early interim progress data to detect risk for mental health treatment nonresponse. First, I detail a strategy that leverages secondary data analysis to examine candidate tailoring variables at candidate critical decision points, and their relationships with treatment nonresponse. Then, I directly apply this strategy to a pooled sample of families who presented for treatment of early childhood behavior problems (N=153). This study showed that using dichotomous classifications of early interim treatment progress yielded limited utility in differentially predicting post-treatment response when examined in isolation from one another. Thus, I subsequently adopt a continuous approach to measuring early interim treatment progress and examine whether interactions between early indicators of treatment response predict symptom trajectories in a sample of families who participated in a behavioral parenting intervention (BPI) for early childhood developmental delay and behavior problems (N=70). Findings from the third paper suggest symptom response trajectories can be predicted by examining the interaction between caregiver skills and child behavior problems displayed within the first six sessions of a BPI. Collectively, this collection of work encourages the use of routine outcome monitoring to assess multiple domains of early interim treatment progress. To improve the efficiency and effectiveness of mental health care, future work should continue to use analytic approaches that capture the dynamic interplay among multiple early interim response factors that can optimally inform clinical decision-making practices throughout treatment

Leveraging real-world data to assess treatment sequences in health economic evaluations: a study protocol for emulating target trials using the English Cancer Registry and US Electronic Health Records-Derived Database

Background Considering the sequence of treatments is vital for optimising healthcare resource allocation, especially in cancer care, where sequence changes can affect patients’ overall survival and associated costs. A key challenge in evaluating treatment sequences in health technology assessments (HTA) is the scarce evidence on effectiveness, leading to uncertainties in decision making. While randomised controlled trials (RCTs) and meta-analyses are viewed as the gold standards for evidence, applying them to determine the effectiveness of treatment sequences in economic models often necessitates making arbitrary assumptions due to insufficient information on patients' treatment histories and subsequent therapies. In contrast, real-world data (RWD) presents a promising alternative source of evidence, often encompassing details across treatment lines. However, due to its non-randomised nature, estimates of the treatment effectiveness based on RWD analyses can be susceptible to biases if not properly adjusted for confounding factors. To date, several international initiatives have been investigating methods to derive reliable treatment effects from RWD — by emulating Target Trials that replicate existing RCTs (i.e. benchmarks) and comparing the emulated results against the benchmarks. These studies primarily seek to determine the viability of obtaining trial-equivalent results through deploying specific analytical methodologies and study designs within the Target Trial emulation framework, using a given database. Adopting the Target Trial emulation framework facilitates the analyses to be operated under causal inference principles. Upon validation in a particular database, these techniques can be applied to address similar questions (e.g., same disease area, same outcome type), but in populations lacking clinical trial evidence, leveraging the same RWD source. Studies to date, however, have predominantly focused on the comparison of individual treatments rather than treatment sequences. Moreover, the majority of these investigations have been undertaken in non-English contexts. Consequently, the use of RWD in evaluating treatment sequences for HTA, especially in an English setting, remains largely unexplored. Objectives The goal of this project is to investigate the feasibility of leveraging RWD to produce reliable, trial-like effectiveness estimates for treatment sequences. We aim to assess the capability of two oncology databases: the US-based Flatiron electronic health record and the National Cancer Registration and Analysis Service (NCRAS) database of England. To achieve this, we plan to harness the Target Trial Emulation (TTE) framework for replicating two existing oncology RCTs that compared treatment sequences, with the intent of benchmarking our results against the original studies. Further, we aim to detail the practicalities involved with implementing TTE in diverse databases and outline the challenges encountered. Methods 1. We aim to emulate existing RCTs that compare the effect of different treatment sequences by constructing the study design and analysis plan following the TTE framework. Specifically, the following case studies are planned: (1) Prostate cancer case study 1 (PC1) - US direct proof-of-concept study (method direct validation): replicating the GUTG-001 trial using Flatiron data (2) Prostate cancer case study 2 (PC2) - US-England bridging study (method extension): emulating Target Trials that compare treatment sequences that have been common in England using Flatiron data (3) Prostate cancer case study 3 (PC3) - English indirect proof-of-concept study (method indirect validation): emulating the same Target Trial in PC2 using English NCRAS data (4) Renal cell carcinoma case study (RCC) - method direct validation in a single-arm setting: emulating the sunitinib followed by everolimus arm in the RECORD-3 trial using English NCRAS data 2. We will compare results of the emulated Target Trials with those from the benchmark trials. 3. We plan to compare different advanced causal inference methods (e.g. marginal structural models using IPW and other g-methods) in estimating the effect of treatment sequences in RWD. Expected results This study will provide evidence on whether it is feasible to obtain reliable estimates of the (comparative) effectiveness of treatment sequences using Flatiron data and English NCRAS data. If applicable, we intend to develop a framework that provides a systematic way of obtaining the (comparative) effectiveness of treatment sequences using RWD. It is possible that the data quality is insufficient to emulate the planned Target Trials. In this case, we will report reasons for the implausibility of data analysis. If applicable, we will make suggestions to whether the national health data collection may be enhanced to make the analyses possible. The results of this study will be submitted to peer-reviewed journals and international conferences