Outcomes of Warfarin Home INR Monitoring vs Office-Based Monitoring: a Retrospective Claims-Based Analysis.

BACKGROUND: Home INR testing (patient self-testing) is feasible and effective for warfarin patients but little is known about real-world differences in outcomes for patients using PST versus laboratory-based INR monitoring. OBJECTIVE: To compare the safety/efficacy of patient self-testing of real-world warfarin therapy versus office/lab-based monitoring of therapy. DESIGN/SETTING/PARTICIPANTS/EXPOSURE: A retrospective claims-based analysis of warfarin patients enrolled in the MarketScan® Commercial Claims and Encounters and Medicare databases between January 1, 2013, and March 30, 2020. Stratification was based on INR testing method: patient self-testing versus testing at physicians' offices/local laboratory. The probability of adverse events in each cohort was determined after adjusting for demographic and baseline clinical characteristics using a repeated measures analysis. MAIN MEASURES: Rates of all adverse events: deep venous thrombosis, pulmonary embolism, bleeding, and stroke. A secondary outcome of interest was emergency department visits. KEY RESULTS: A total of 37,837 patients were included in the analysis: 1592 patients in the patient self-testing group and 36,245 in the office-based therapy group. After adjusting for demographic and baseline clinical characteristics, patients in the office-based group had statistically significantly higher rates of all adverse events (incidence rate ratio [IRR]=2.07, 95% CI [1.82, 2.36]), and specific adverse events including thromboembolism (IRR=4.38, 95% CI [3.29, 5.84]), major bleed (IRR=1.45, 95% CI [1.28, 1.64]), and stroke (IRR=1.30, 95% CI [1.05, 1.61]) than patients in the patient self-testing group. Office-based patients also had a statistically significant higher rate of emergency department visits than patient self-testing patients (IRR = 1.65, 95% CI [1.47, 1.84]). CONCLUSIONS/RELEVANCE: This analysis of real-world claims data shows lower rates of stroke, thromboembolism, and major bleeding, as well as fewer emergency department visits, with patient self-testing compared to office-based/lab INR monitoring. Our finding that PST is safe and effective among current users suggests that more patients may benefit from its use.

The effect of eye movement desensitization and reprocessing on fibromyalgia: A multiple-baseline experimental case study across ten participants.

Fibromyalgia (FM) is a chronic pain disorder characterized by widespread musculoskeletal pain, fatigue, and stiffness in muscles and joints. Traumatic life experiences and post-traumatic stress symptoms play a role in its development and persistence. Although previous research suggests that pain and FM symptoms decrease after eye movement desensitization and reprocessing (EMDR) therapy, its effectiveness has not been investigated in a controlled manner. The present study investigated the effectiveness of a six-session, 90-minute EMDR therapy using a multiple baseline single-case experimental design (SCED) across ten adult females with FM. The SCED involved a baseline, intervention, one- and three-month follow-up phase. The primary outcome was pain. Secondary outcomes included post-traumatic stress symptoms, other FM symptoms (fatigue, stiffness in muscles and joints), and the impact of FM on daily activities and sleep. Data were statistically analyzed by primarily testing means across phases on an individual and group level. Post-traumatic stress symptoms improved significantly in seven participants. Pain severity decreased significantly in six participants, with three of them maintaining significant improvement three months later. One participant showed clinically relevant change one month later. Furthermore, improvements were observed in secondary outcome measures. The findings support the efficacy of EMDR in reducing FM symptoms.

Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods.

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect sizes and their sampling variances are subsequently combined to estimate a meta-analytic multilevel model. The multilevel model in the two-stage approach has fewer parameters to estimate, in exchange for the reduction of information of the raw data to effect sizes. In this paper we explore how the one-stage and two-stage IPD approaches can be applied in the context of meta-analysis of single-case designs. Both approaches are compared for several single-case designs of increasing complexity. Through a simulation study we show that the two-stage approach obtains better convergence rates for more complex models, but that model estimation does not necessarily converge at a faster speed. The point estimates of the fixed effects are unbiased for both approaches across all models, as such confirming results from methodological research on IPD meta-analysis of group-comparison designs. In light of these results, we discuss the implementation of both methods in R.

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling.

The MultiSCED web application has been developed to assist applied researchers in behavioral sciences to apply multilevel modeling to quantitatively summarize single-case experimental design (SCED) studies through a user-friendly point-and-click interface embedded within R. In this paper, we offer a brief introduction to the application, explaining how to define and estimate the relevant multilevel models and how to interpret the results numerically and graphically. The use of the application is illustrated through a re-analysis of an existing meta-analytic dataset. By guiding applied researchers through MultiSCED, we aim to make use of the multilevel modeling technique for combining SCED data across cases and across studies more comprehensible and accessible.

Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies.

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5).

Analysis of single-case experimental count data using the linear mixed effects model: A simulation study.

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data.

Illustrating novel techniques for analyzing single-case experiments: Effects of pre-session mindfulness practice.

In this article we describe and illustrate various visual and nonparametric techniques that can be used to evaluate the effectiveness of interventions in single-case experiments. Using an alternating treatments design across 2 cases, we tested whether practicing mindfulness would help a novice therapist stay focused and respond to a client more empathically and genuinely. Specifically, after taking a brief workshop on mindfulness, one male and one female doctoral trainee were asked, immediately before beginning each of 10 sessions, either to engage in a guided mindfulness practice for three minutes or a control activity of their choice. Five experimental and 5 control sessions were randomly scheduled for each therapist. The clients were unaware of the purpose of the study and the nature of the experimental manipulation. After each session, we administered measures of client-rated empathy, client- and therapist-rated real relationship, and therapist-rated level of hindering self-awareness. The graphed data were examined separately for each measure. Results showed that the client in Dyad 1 perceived the therapist as more empathic following mindfulness sessions, and the therapist reported less hindering self-awareness and rated the real relationship more favorably, with moderate to strong experimental effects. By contrast, Dyad 2 showed moderate to strong experimental effects for mindfulness practice on ratings of the real relationship, but not on empathy or hindering self-awareness. A cross-case comparison illustrates how replicated single-case experimental research with self-report data can advance our understanding of individual differences in response to psychotherapeutic interventions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The role of language proficiency and linguistic distance in cross-linguistic treatment effects in aphasia.

Current findings from intervention in bilingual aphasia are inconclusive regarding the extent to which levels of language proficiency and degree of linguistic distance between treated and non-treated languages influence cross-language generalisation and changes in levels of language activation and inhibition following treatment. In this study, we enrolled a 65-year-old multilingual speaker with aphasia and administered treatment in his L1, Dutch. We assessed pre- and post-treatment performance for seven of his languages, five of high proficiency and two of lower proficiency. We asked whether treatment in L1 would generalise to his other languages or increase interference among them. Forty hours of treatment were completed over the course of five weeks. Each language was tested three times at pretreatment and at post-treatment. Testing included measures of narrative production, answering questions, picture description and question generation. Dependent measures examined language efficiency, defined as Correct Information Units (CIUs)/min, as well as language mixing, defined as proportion of code-mixed whole words. We found that our participant's improved efficiency in Dutch was mirrored by parallel improvement in the four languages of high proficiency: English, German, Italian and French. In contrast, in his languages of lower proficiency, Norwegian and Spanish, improved efficiency was limited. An increase in code-mixing was noted in Spanish, but not in Norwegian. We interpret the increased code-mixing in Spanish as indication of heightened inhibition following improvement in a language of close linguistic proximity, Italian. We conclude that an interaction of language proficiency and linguistic similarity affects cross-language generalisation following intervention in multilingual aphasia.

Single-Case Design, Analysis, and Quality Assessment for Intervention Research.

BACKGROUND AND PURPOSE: The purpose of this article is to describe single-case studies and contrast them with case studies and randomized clinical trials. We highlight current research designs, analysis techniques, and quality appraisal tools relevant for single-case rehabilitation research. SUMMARY OF KEY POINTS: Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single-case studies involve repeated measures and manipulation of an independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, as well as external validity for generalizability of results, particularly when the study designs incorporate replication, randomization, and multiple participants. Single-case studies should not be confused with case studies/series (ie, case reports), which are reports of clinical management of a patient or a small series of patients. RECOMMENDATIONS FOR CLINICAL PRACTICE: When rigorously designed, single-case studies can be particularly useful experimental designs in a variety of situations, such as when research resources are limited, studied conditions have low incidences, or when examining effects of novel or expensive interventions. Readers will be directed to examples from the published literature in which these techniques have been discussed, evaluated for quality, and implemented.

The use of multilevel analysis for integrating single-case experimental design results within a study and across studies.

The use of multilevel models as a method for synthesising single-case experimental design results is receiving increased consideration. In this article we discuss the potential advantages and limitations of the multilevel modelling approach. We present a basic two-level model where observations are nested within cases, and then discuss extensions of the basic model to accommodate trends, moderators of the intervention effect, non-continuous outcomes, heterogeneity, autocorrelation, the nesting of cases within studies, and more complex single-case design types. We then consider methods for standardising the effect estimates and alternative approaches to estimating the models. These modelling and analysis options are followed by an illustrative example.

PROTOCOL: Key characteristics of effective preschool-based interventions to promote self-regulation: A systematic review and meta-analysis.

This is the protocol for a Cochrane Review. The objectives are as follows: The aim of this systematic review is to advance our understanding of the key characteristics of effective preschool-based interventions designed to foster self-regulation. To accomplish this, the review addresses the following questions: 1. What types of preschool-based interventions have been developed to promote self-regulation? 2. What is the average effect of these preschool-based interventions on self-regulation, focusing on four key constructs: integrative effortful control, integrative executive function, self-regulation, and self-regulated learning? 3. What characteristics-such as Resource Allocation, Activity Type, and Instruction Method-could potentially contribute to the effects of preschool-based interventions in promoting self-regulation?

Modeling external events in the three-level analysis of multiple-baseline across-participants designs: a simulation study.

In this study, we focus on a three-level meta-analysis for combining data from studies using multiple-baseline across-participants designs. A complicating factor in such designs is that results might be biased if the dependent variable is affected by not explicitly modeled external events, such as the illness of a teacher, an exciting class activity, or the presence of a foreign observer. In multiple-baseline designs, external effects can become apparent if they simultaneously have an effect on the outcome score(s) of the participants within a study. This study presents a method for adjusting the three-level model to external events and evaluates the appropriateness of the modified model. Therefore, we use a simulation study, and we illustrate the new approach with real data sets. The results indicate that ignoring an external event effect results in biased estimates of the treatment effects, especially when there is only a small number of studies and measurement occasions involved. The mean squared error, as well as the standard error and coverage proportion of the effect estimates, is improved with the modified model. Moreover, the adjusted model results in less biased variance estimates. If there is no external event effect, we find no differences in results between the modified and unmodified models.

Characteristics of Moderators in Meta-Analyses of Single-Case Experimental Design Studies.

Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant and-or study characteristics can be incorporated in the model in an attempt to explain intervention heterogeneity. The inclusion of moderators in the context of meta-analysis of SCED studies did not yet receive attention and is in need of methodological research. Prior to extending methodological work validating the hierarchical linear model including moderators at the different levels, an overview of characteristics of moderators typically encountered in the field is needed. This will inform design conditions to be embedded in future methodological studies and ensure that these conditions are realistic and representative for the field of SCED meta-analyses. This study presents the results of systematic review of SCED meta-analyses, with the particular focus on moderator characteristic. The initial search yielded a total of 910 articles and book chapters. After excluding duplicate studies and non peer-reviewed studies, 658 unique peer-reviewed studies were maintained and screened by two independent researchers. Sixty articles met the inclusion criteria and were eligible for data retrieval. The results of the analysis of moderator characteristics retrieved from these 60 meta-analyses are presented. The first part of the results section contains an overview of moderator characteristics per moderator level (within-participant level, participant level, and study level), including the types of moderators, the ratio of the number of moderators relative to the number of units at that level, the measurement scale, and the degree of missing data. The second part of the results section focuses on the metric used to quantify moderator effectiveness and the analysis approach. Based on the results of the systematic review, recommendations are given for conditions to be included in future methodological work.

Single-case design meta-analyses in education and psychology: a systematic review of methodology.

Meta-analysis is of increasing importance as this quantitative synthesis technique has the potential to summarize a tremendous amount of research evidence, which can help making evidence-based decisions in policy, practice, and theory. This paper examines the single-case meta-analyses within the Education and Psychology fields. The amount of methodological studies related to the meta-analysis of Single-Case Experimental Designs (SCEDs) is increasing rapidly, especially in these fields. This underscores the necessity of a succinct summary to help methodologists identify areas for further development in Education and Psychology research. It also aids applied researchers and research synthesists in discerning when to use meta-analytic techniques for SCED studies based on criteria such as bias, mean squared error, 95% confidence intervals, Type I error rates, and statistical power. Based on the summary of empirical evidence from 18 reports identified through a systematic search procedure, information related to meta-analytic techniques, data generation and analysis models, design conditions, statistical properties, conditions under which the meta-analytic technique is appropriate, and the study purpose(s) were extracted. The results indicate that three-level hierarchical linear modeling is the most empirically validated SCED meta-analytic technique, and parameter bias is the most prominent statistical property investigated. A large number of primary studies (more than 30) and at least 20 measurement occasions per participant are recommended for usage of SCED meta-analysis in Education and Psychology fields.

Systematic Review of Descriptions and Justifications Provided for Single-Case Quantification Techniques.

There are currently a multitude of quantification techniques that have been developed for use with single-case designs. As a result, choosing an appropriate quantification technique can be overwhelming and it can be difficult for researchers to properly describe and justify their use of quantification techniques. However, providing clear descriptions and justifications is important for enhancing the credibility of single-case research, and allowing others to evaluate the appropriateness of the quantification technique used. The aim of this systematic literature review is to provide an overview of the quantification techniques that are used to analyze single-case designs, with a focus on the descriptions and justifications that are provided. A total of 290 quantifications occurred across 218 articles, and the descriptions and justifications that were provided for the quantification techniques that were used are systematically examined. Results show that certain quantification techniques, such as the non-overlap indices, are more commonly used. Descriptions and justifications provided for using the quantification techniques are sometimes vague or subjective. Single-case researchers are encouraged to complement visual analysis with the use of quantification techniques for which they can provide objective and appropriate descriptions and justifications, and are encouraged to use tools to guide their choice of quantification techniques.

Reducing face touching through haptic feedback: A treatment evaluation against fomite-mediated self-infection.

Fomite-mediated self-infection via face touching is an understudied transmission pathway for infectious diseases. We evaluated the effect of computer-mediated vibrotactile cues (presented through experimental bracelets located on one or both hands of the participant) on the frequency of face touching among eight healthy adults in the community. We conducted a treatment evaluation totaling over 25,000 min of video observation. The treatment was evaluated through a multiple-treatment design and hierarchical linear modeling. The one-bracelet intervention did not produce significantly lower levels of face touching across both hands, whereas the two-bracelet intervention did result in significantly lower face touching. The effect increased over repeated presentations of the two-bracelet intervention, with the second implementation producing, on average, 31 fewer face-touching percentual points relative to baseline levels. Dependent on the dynamics of fomite-mediated self-infection via face touching, treatment effects could be of public health significance. The implications for research and practice are discussed.

Multilevel meta-analysis of single-subject experimental designs: a simulation study.

One way to combine data from single-subject experimental design studies is by performing a multilevel meta-analysis, with unstandardized or standardized regression coefficients as the effect size metrics. This study evaluates the performance of this approach. The results indicate that a multilevel meta-analysis of unstandardized effect sizes results in good estimates of the effect. The multilevel meta-analysis of standardized effect sizes, on the other hand, is suitable only when the number of measurement occasions for each subject is 20 or more. The effect of the treatment on the intercept is estimated with enough power when the studies are homogeneous or when the number of studies is large; the power of the effect on the slope is estimated with enough power only when the number of studies and the number of measurement occasions are large.

Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data.

We have entered an era in which scientific knowledge and evidence increasingly inform research practice and policy. As there is an exponential increase in the use of personalized trials, there is a remarkable growing interest in the quantitative synthesis of personalized trials. One technique that is developed and can be applied for this purpose is meta-analysis. Meta-analysis involves the quantitative integration of effect sizes from several personalized trials. In this study, aggregated data (AD) and individual patient data (IPD) methods for meta-analysis of personalized trials are discussed, together with an empirical demonstration using a subset of a real meta-analytic data set. For the empirical demonstration, 26 personalized trials received usual care and yoga intervention in a randomized sequence. Results show a general consensus between the AD and IPD approach in terms of conclusions-that both usual care and the yoga intervention are effective in reducing pain. However, the IPD approach provides more information about the intervention effectiveness and intervention heterogeneity. IPD is a more flexible modeling approach, allowing for a variety of modeling options.

Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue.

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.

A Priori Justification for Effect Measures in Single-Case Experimental Designs.

Due to the complex nature of single-case experimental design data, numerous effect measures are available to quantify and evaluate the effectiveness of an intervention. An inappropriate choice of the effect measure can result in a misrepresentation of the intervention effectiveness and this can have far-reaching implications for theory, practice, and policymaking. As guidelines for reporting appropriate justification for selecting an effect measure are missing, the first aim is to identify the relevant dimensions for effect measure selection and justification prior to data gathering. The second aim is to use these dimensions to construct a user-friendly flowchart or decision tree guiding applied researchers in this process. The use of the flowchart is illustrated in the context of a preregistered protocol. This is the first study that attempts to propose reporting guidelines to justify the effect measure choice, before collecting the data, to avoid selective reporting of the largest quantifications of an effect. A proper justification, less prone to confirmation bias, and transparent and explicit reporting can enhance the credibility of the single-case design study findings.