Variation In Treatment Effects Research Articles

Doubling down on dosage: exploring the interplay between dosage effects, antisocial traits, treatment attitudes, and recidivism-related therapy outcomes

Purpose Studies evaluating the external validity of theoretically informed (risk-need-responsivity [RNR]) cognitive behavioral therapy (CBT) programs have not systematically assessed antisocial personality traits as a source of variability in outcomes. The purpose of this brief report is to examine antisocial traits as a potential source of variability in outcomes. Design/methodology/approach Using longitudinal, program-evaluation data (a sample of incarcerated men who were exposed to RNR-informed CBT), the authors examined how antisocial personality traits, attitudes toward treatment and dosage impacted treatment outcomes. A linear regression assessed the relationship between antisocial personality traits and attitudes toward treatment. A latent profile analysis identified participants with elevated antisocial traits and negative attitudes toward treatment. Treatment dosage groups (0, 5, 10 and 15 weeks) were used to assess treatment response per pro-criminal attitudes, skills and rates of recidivism. Findings Antisocial traits predicted negative attitudes toward treatment. Elevated antisocial traits and negative treatment attitudes predicted less change in criminal attitudes among those who completed 15 weeks of treatment; higher dosage did not significantly improve rates of recidivism. Variable-centered post hoc analyses largely corroborated these findings. These results suggest RNR-informed CBT may need to be modified for justice-involved persons with elevated antisocial traits. Originality/value Few studies have empirically examined the sources of variability in treatment effects for justice-involved persons with antisocial personality traits. This brief report provides a structured examination of factors that may impact treatment outcomes in this population, and therefore aims to inform future research on the effectiveness of empirically supported interventions for people in the legal system.

Short-versus long-term effects of nitrogen addition and warming on soil nitrogen mineralization and leaching in a grass-dominated old field.

Increased atmospheric nitrogen (N) deposition and climate warming are both anticipated to influence the N dynamics of northern temperate ecosystems substantially over the next century. In field experiments with N addition and warming treatments, cumulative treatment effects can be important for explaining variation in treatment effects on N dynamics over time; however, comparisons between data collected in the early vs. later years potentially can be confounded with interactions between treatment effects and inter-annual variation in environmental conditions or other factors. We compared the short-term versus long-term effects of N addition and warming on net N mineralization and N leaching in a grass-dominated old field using in situ soil cores. We added new N addition and warming plots (3 years old) to an existing field experiment (16 years old), which enabled comparison of the treatment effects at both time scales while controlling for potential inter-annual variation in other factors. For net N mineralization, there was a significant interaction between plot age and N addition over the growing season, and for extractable inorganic N there was a significant interaction between plot age and warming over winter. In both cases, the directions of the treatment effects differed among old and new plots. Moreover, the responses in the new plots differed from the responses observed previously when the 16-year-old plots had been new. These results demonstrate how inter-annual variation in responses, independent from cumulative treatment effects, can play an important role in interpreting long-term effects on soil N cycling in global change field experiments.

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package, which is available at https://github.com/ngalanter/rct2otrbounds.

Disentangling Person-Dependent and Item-Dependent Causal Effects: Applications of Item Response Theory to the Estimation of Treatment Effect Heterogeneity

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment. This study demonstrates that the identical patterns of HTE on test score outcomes can emerge either from variation in treatment effects due to a preintervention participant characteristic or from correlations between treatment effects and item easiness parameters. We demonstrate analytically and through simulation that these two scenarios cannot be distinguished if analysis is based on summary scores alone. We then describe a novel approach that identifies the relevant data-generating process by leveraging item-level data. We apply our approach to a randomized trial of a reading intervention in second grade and show that any apparent HTE by pretest ability is driven by the correlation between treatment effect size and item easiness. Our results highlight the potential of employing measurement principles in causal analysis, beyond their common use in test construction.

Tackling Challenges in Assessing the Economic Value of Tumor-Agnostic Therapies: A Cost-Effectiveness Analysis of Pembrolizumab as a Case Study

ObjectivesAssessing the value of tumor-agnostic drugs (TAD) is challenging given the potential variability in treatment effects, trials with small sample sizes, different standards of care (SoC), and lack of comparative data from single-arm basket trials. Our study developed and applied novel methods to assess the value of pembrolizumab compared with SoC to inform coverage decisions. MethodsWe developed a partitioned survival model to evaluate the cost-utility of pembrolizumab for previously treated patients with 8 advanced or metastatic microsatellite instability-high or mismatch repair-deficient cancers from a US commercial payer perspective. Efficacy of pembrolizumab was based on data from trials directly or with adjustment using Bayesian hierarchical models. Eight chemotherapy-based external control arms were constructed from the TriNetX electronic health record databases. Tumor-specific health-state utility values were applied. All costs were adjusted to 2022 US dollars. ResultsAt a lifetime horizon, pembrolizumab was associated with increased effectiveness compared with chemotherapies in colorectal (quality-adjusted life years [QALYs]: +0.64, life years [LYs]: +0.64), endometrial (QALYs: +3.79, LYs: +5.47), and small intestine cancers (QALYs: +1.73, LYs: +2.48), but not for patients with metastatic gastric, cholangiocarcinoma, pancreatic, ovarian, and brain cancers. Incremental cost-effectiveness ratios varied substantially across tumor types. Pembrolizumab was found to be cost-effective in treating colorectal and endometrial cancers (incremental cost-effectiveness ratios: $121 967 and $139 257, respectively), and not cost-effective for other assessed cancers at a $150 000 willingness-to-pay/QALY threshold, compared with SoC chemotherapies. ConclusionsThe cost-effectiveness of TADs can vary by cancers. Using analytic tools such as external controls and Bayesian hierarchical models can tackle several challenges in assessing the value of TADs and uncertainties from basket trials.

Personalization of treatment based on obesity phenotypes

Introduction:
 Obesity is a significant clinical problem worldwide. The disease exhibits remarkable heterogeneity, leading to diverse pathogenesis among individuals. Despite a growing selection of weight-reducing medications and increased awareness of the importance of a healthy lifestyle, the effectiveness of obesity therapy often remains insufficient. This raises the question of whether tailoring obesity pharmacotherapy to specific phenotypes and individual patient characteristics results in improved treatment outcomes.
 Objective:
 To review literature on the effectiveness of obesity treatment based on the personalization of pharmacotherapy according to its underlying causes.
 Current Knowledge:
 Currently, the early outcome of weight loss is closely linked to the long-term effectiveness of obesity pharmacotherapy. However, due to the diversity of obesity pathogenesis, it is not helpful in selecting therapy at the initiation of treatment. Specific characteristics are sought to indicate the appropriate drug during therapy planning.
 In a U.S. study (Acosta et al., 2021) results showed that phenotype-guided obesity pharmacotherapy resulted in clinically more significant absolute weight loss than in the non-phenotyped group.
 Other studies demonstrated that for very rare types of obesity dependent on specific genetic mutations, targeted treatment addressing these defects yielded the best therapeutic outcomes (recombinant human leptin therapy for patients with leptin gene mutation – Farooqi et al., 2002; setmelanotide therapy for individuals with proopiomelanocortin deficiency – Clément et al., 2020).
 The impact of specific gene variants on therapeutic response should also be considered, leading to variability in treatment effects for different patients using the same drug.
 Conclusion:
 The concept of basing therapy on obesity phenotypes may currently serve as a guide for treatment selection, although it still requires confirmation in randomized clinical trials.

Uncontrolled pain: a call for better study design.

Studies assessing animal pain in veterinary research are often performed primarily for the benefit of animals. Frequently, the goal of these studies is to determine whether the analgesic effect of a novel treatment is clinically meaningful, and therefore has the capacity to improve the welfare of treated animals. To determine the treatment effect of a potential analgesic, control groups are necessary to allow comparison. There are negative control groups (where pain is unattenuated) and positive control groups (where pain is attenuated). Arising out of animal welfare concerns, there is growing reluctance to use negative control groups in pain studies. But for studies where pain is experimentally induced, the absence of a negative control group removes the opportunity to demonstrate that the study methods could differentiate a positive control intervention from doing nothing at all. For studies that are controlled by a single comparison group, the capacity to distinguish treatment effects from experimental noise is more difficult; especially considering that pain studies often involve small sample sizes, small and variable treatment effects, systematic error and use pain assessment measures that are unreliable. Due to these limitations, and with a focus on farm animals, we argue that many pain studies would be enhanced by the simultaneous inclusion of positive and negative control groups. This would help provide study-specific definitions of pain and pain attenuation, thereby permitting more reliable estimates of treatment effects. Adoption of our suggested refinements could improve animal welfare outcomes for millions of animals globally.

Explaining Variation in Treatment Effects: An Impact Evaluation and Mixed-Methods Study of Variation in Early Grade Reading Program Effects

Explaining Variation in Treatment Effects: An Impact Evaluation and Mixed-Methods Study of Variation in Early Grade Reading Program Effects

A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes.

Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emphasizing the number of beneficiaries and the impact level. To evaluate clinical significance, minimal clinically important risk difference (MCIRD) is proposed based on event magnitude (EM). Within this framework, risk difference (RD) is introduced as the primary measure. To assess the performance of RD, we compared its statistical power against other measures (risk ratio, RR; odds ratio, OR; Cohen's h) in individual study scenarios, and visual information conveyance in meta-analysis scenarios. RDs maintain statistical power in comparison to other measures in individual studies. They provide clarity on the true impact of clinical interventions without compromising statistical integrity. Meta-analytic results indicate that using RDs directly enhances transparency, uncovers heterogeneity, and addresses misaligned assumptions. This approach, by quantifying clinical effectiveness under a utilitarian perspective, facilitates the applicability of research to patient care and encourages shared decision-making. The study advocates for reporting baseline risks (BRs) with RDs and recommends a standardized presentation of these statistics. In a utilitarian perspective, adopting RD as the preferred ES can foster a transparent, patient-focused research ethos. This aids in accurately presenting the magnitude and variability of treatment effects, offering a new direction in methodology.

Longitudinal Clusters of Long-Term Trajectories in Patients with Early-Onset Chronic Depression: 2 Years of Naturalistic Follow-Up after Extensive Psychological Treatment

Introduction: In clinical trials, mostly group-level treatment effects of repeated cross-sectional measures are analyzed. However, substantial heterogeneity regarding individual symptom profiles and the variability of treatment effects are often neglected, especially over the long-term course. To provide effective personalized treatments, investigations of these characteristics are urgently needed. Methods: Depression severity ratings over 104 weeks of follow-up after year-long treatment with the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) or Supportive Psychotherapy (SP) were analyzed. Longitudinal cluster analysis and multinomial logistic regression analysis were conducted to investigate intraindividual trajectories from one of the largest psychotherapy trials in early-onset chronic depression. Results: Two-year post-study-treatment trajectories of N = 188 patients with early-onset chronic depression were grouped into four prototypical clusters. Overall, 16.0% of patients remitted (cluster 1) and most of them did not receive any treatment during the 2-year follow-up. However, 84.0% of patients continued to experience subthreshold (37.2% cluster 2) or major depressive symptoms (46.8% clusters 3–4) and spent on average more than half of the follow-up in pharmacological and psychological treatment. Hierarchical regression analysis indicated that previous study treatment with CBASP or SP did not significantly predict cluster allocation, while baseline variables accounted for a large proportion of explained variance (R² _N = 0.64). Conclusion: While some patients experienced stable remission over 2 years of follow-up, the majority of patients experienced subthreshold or major depressive symptoms regardless of former study treatment with CBASP or SP. This calls for a long-term perspective implementing staging and innovative treatment approaches such as the sequential model or modular psychotherapy.

Sampling Importance Resampling Algorithm with Nonignorable Missing Response Variable Based on Smoothed Quantile Regression

The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression. In contrast, quantile regression offers valuable insights into the conditional distribution. Consequently, this article places emphasis on the quantile regression approach to address nonrandom missing data. Taking inspiration from fractional imputation, this paper proposes a novel smoothed quantile regression estimation equation based on a sampling importance resampling (SIR) algorithm instead of nonparametric kernel regression methods. Additionally, we present an augmented inverse probability weighting (AIPW) smoothed quantile regression estimation equation to reduce the influence of potential misspecification in a working model. The consistency and asymptotic normality of the empirical likelihood estimators corresponding to the above estimating equations are proven under the assumption of a correctly specified parameter working model. Furthermore, we demonstrate that the AIPW estimation equation converges to an IPW estimation equation when a parameter working model is misspecified, thus illustrating the robustness of the AIPW estimation approach. Through numerical simulations, we examine the finite sample properties of the proposed method when the working models are both correctly specified and misspecified. Furthermore, we apply the proposed method to analyze HIV—CD4 data, thereby exploring variations in treatment effects and the influence of other covariates across different quantiles.

Key pathways and genes that are altered during treatment with hyperbaric oxygen in patients with sepsis due to necrotizing soft tissue infection (HBOmic study)

BackgroundFor decades, the basic treatment strategies of necrotizing soft tissue infections (NSTI) have remained unchanged, primarily relying on aggressive surgical removal of infected tissue, broad-spectrum antibiotics, and supportive intensive care. One treatment strategy that has been proposed as an adjunctive measure to improve patient outcomes is hyperbaric oxygen (HBO2) treatment. HBO2 treatment has been linked to several immune modulatory effects; however, investigating these effects is complicated due to the disease's acute life-threatening nature, metabolic and cell homeostasis dependent variability in treatment effects, and heterogeneity with respect to both patient characteristics and involved pathogens. To embrace this complexity, we aimed to explore the underlying biological mechanisms of HBO2 treatment in patients with NSTI on the gene expression level.MethodsWe conducted an observational cohort study on prospective collected data, including 85 patients admitted to the intensive care unit (ICU) for NSTI. All patients were treated with one or two HBO2 treatments and had one blood sample taken before and after the intervention. Total RNAs from blood samples were extracted and mRNA purified with rRNA depletion, followed by whole-transcriptome RNA sequencing with a targeted sequencing depth of 20 million reads. A model for differentially expressed genes (DEGs) was fitted, and the functional aspects of the obtained set of genes was predicted with GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of genes and Genomes) enrichment analyses. All analyses were corrected for multiple testing with FDR.ResultsAfter sequential steps of quality control, a final of 160 biological replicates were included in the present study. We found 394 protein coding genes that were significantly DEGs between the two conditions with FDR < 0.01, of which 205 were upregulated and 189 were downregulated. The enrichment analysis of these DEGs revealed 20 GO terms in biological processes and 12 KEGG pathways that were significantly overrepresented in the upregulated DEGs, of which the term; “adaptive immune response” (GO:0002250) (FDR = 9.88E-13) and “T cell receptor signaling pathway” (hsa04660) (FDR = 1.20E-07) were the most significant. Among the downregulated DEGs two biological processes were significantly enriched, of which the GO term “apoptotic process” (GO:0006915) was the most significant (FDR = 0.001), followed by “Positive regulation of T helper 1 cell cytokine production” (GO:2000556), and “NF-kappa B signaling pathway” (hsa04064) was the only KEGG pathway that was significantly overrepresented (FDR = 0.001).ConclusionsWhen one or two sessions of HBO2 treatment were administered to patients with a dysregulated immune response and systemic inflammation due to NSTI, the important genes that were regulated during the intervention were involved in activation of T helper cells and downregulation of the disease-induced highly inflammatory pathway NF-κB, which was associated with a decrease in the mRNA level of pro-inflammatory factors.Trial registration: Biological material was collected during the INFECT study, registered at ClinicalTrials.gov (NCT01790698).

Genetic variability in cisplatin metabolic pathways and outcome of locally advanced head and neck squamous cell carcinoma patients

Advanced head and neck squamous cell carcinoma (HNSCC) patients have been treated with cisplatin (CDDP) chemoradiation, and the variability of treatment effects has been attributed to single nucleotide variants (SNVs) in genes of metabolic pathways. This study investigated the roles of GSTM1, GSTT1, GSTP1 c.313A>G, XPC c.2815A>C, XPD c.934G>A and c.2251A>C, XPF c.2505T>C, ERCC1 c.354C>T, MLH1 c.93G>A, MSH2 c.211+9C>G, MSH3 c.3133G>A, EXO1 c.1765G>A, TP53 c.215G>C, CASP3 c.-1191A>G and c.-182-247G>T, FAS c.-1378G>A and c.-671A>G and FASL c.-844C>T SNVs in outcome of 109 patients treated with CDDP chemoradiation. Genotypes were identified in genomic DNA by PCR-based methods. Conventional criteria and tests analyzed response and survival. Patients with XPC c.2815AC or CC had 3.43 times more chances of presenting partial response or stable disease. Patients with FAS c.-671GG, GSTM1 present plus XPC c.2815AA, or plus XPD c.934GG, or plus XPD c.2251AA, or plus TP53 c.215GC or CC, and XPD c.2251AA plus XPF c.2505TT had up to 2.70 and 2.37 times more chances of presenting tumor progression and evolving to death, respectively. Our data indicate, for the first time, preliminary evidence that combined SNVs of CDDP metabolism act as independent prognostic factors and can be used to select patients for distinct treatments.

Does Tax Simplification Motivate Small Businesses to Be More Compliant? Evidence from a Regression Discontinuity in Indonesia

This research investigates how the tax simplification program in Indonesia affects tax compliance among small businesses. This study employs a regression discontinuity design and examines various parameters of tax compliance. The results indicate that tax simplification does not significantly influence tax payment behaviour. However, it does have a positive effect on overall tax compliance, as evidenced by increased reporting frequency and decreased penalties. Investigation of the heterogeneous treatment impact reveals a considerable variation in treatment effects across geographic areas and internet accessibility. Taxpayers located in areas with decent internet connectivity and proximity to the tax office appear to benefit more from the program's implementation.

Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model.

Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT COV. In detecting TRT COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood, 2021). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Acupuncture for the prevention of chemotherapy-induced nausea and vomiting in cancer patients: A systematic review and meta-analysis.

To assess the effectiveness and safety of acupuncture for the prevention of chemotherapy-induced nausea and vomiting (CINV), with a specific intention on exploring sources of between-study variation in treatment effects. MEDLINE, EMBASE, Cochrane CENTRAL, CINAHL, Chinese Biomedical Literature Database, VIP Chinese Science and Technology Periodicals Database, China National Knowledge Infrastructure, and Wanfang were searched to identify randomized controlled trials (RCTs) that compared acupuncture to sham acupuncture or usual care (UC). The main outcome is complete control (no vomiting episodes and/or no more than mild nausea) of CINV. GRADE approach was used to rate the certainty of evidence. Thirty-eight RCTs with a total of 2503 patients were evaluated. Acupuncture in addition to UC may increase the complete control of acute vomiting (RR, 1.13; 95% CI, 1.02 to 1.25; 10 studies) and delayed vomiting (RR, 1.47; 95% CI, 1.07 to 2.00; 10 studies) when compared with UC only. No effects were found for all other review outcomes. The certainty of evidence was generally low or very low. None of the predefined moderators changed the overall findings, but in an exploratory moderator analysis we found that an adequate reporting of planned rescue antiemetics might decrease the effect size of complete control of acute vomiting (p = 0.035). Acupuncture in addition to usual care may increase the complete control of chemotherapy-induced acute vomiting and delayed vomiting but the certainty of evidence was very low. Well-designed RCTs with larger sample sizes, standardized treatment regimens, and core outcome measures are needed.

Does single-visit root canal treatment of permanent teeth provide more benefit than a multiple-visit approach?

Cochrane Oral Health's Trials Register, Cochrane Central Register of Controlled Trials in the Cochrane Library, MEDLINE Ovid, Embase Ovid. Randomised controlled trials and quasi-randomised controlled trials were included. Participants aged ≥ 10 with a permanent tooth possessing a completely formed apex and without resorption; Intervention: Root canal treatment (RoCT) carried out in a single visit; Comparison: RoCT carried out over multiple visits; Outcome: Primary outcome was treatment success (retention of tooth or radiographic evidence of healing), with secondary outcomes investigating post-operative symptoms (pain, swelling, sinus tract formation). Standard Cochrane methods to assess internal validity were used. The Robins 1 tool (for quasi randomised controlled trials) or risk of bias (RoB) 1 tool (for randomised controlled trials) were used to assess RoB whereby a judgement was assigned as 'low', 'high' or 'unclear'. GRADE (GRADEpro GDT software) was used to assess certainty of evidence for each outcome. The certainty of evidence was defined as high, moderate, low or very low, having no downgrade, downgrade of one level, downgrade of two levels and downgrade of three or more levels, respectively. Of the various subgroups investigated to determine their relevance, only pretreatment conditions (vital teeth versus necrotic teeth) and endodontic technique (manual versus mechanical instrumentation) were available for subgroup analysis. The Cochrane's test for heterogeneity and I2 test were used to assess the variation in treatment effects. A random-effects model was used to combine risk ratio (RR) for dichotomous data and mean difference (MD) for continuous data. Sensitivity analysis was performed for each outcome, excluding studies at overall high or unclear RoB. Forty-seven studies were included in the meta-analysis and internal validity assessment, with 5693 teeth analysed. Ten studies were found to have a low RoB, 17 with a high RoB and 20 with an unclear RoB. No evidence was identified suggesting a difference between treatment carried out in a single visit compared to a multiple visits approach for the primary outcome measure, but there was very low certainty about the findings (RR 0.46, 95% confidence interval (CI) 0.09 to 2.50; I 2 = 0%; 2 studies, 402 teeth). No evidence was identified suggesting a difference between treatment carried out in a single visit compared to multiple visits with regards to radiological failure (RR 0.93, 95% CI: 0.81 to 1.07; I 2 = 0%; 13 studies, 1505 teeth; moderate-certainty evidence), participants reporting pain up to 72 h post obturation (RR 0.97, 95% CI: 0.81 to 1.16; I 2 = 70%; 12 studies, 1329 teeth; low-certainty evidence), pain for 72 h post obturation (MD 0.26, 95% CI: -4.76 to 5.29; I 2 = 98%; 12 studies, 1258 teeth; low-certainty evidence) or pain at 1 week post obturation (RR 1.05, 95% CI: 0.67 to 1.67; I2 = 61%; 9 studies, 1139 teeth; very low-certainty evidence). Similarly, no evidence was identified to prove that there was a difference between treatment carried out in a single visit compared to multiple visits with regards to swelling or flare-up (RR 0.56 95% CI: 0.16-1.92; I 2 = 0%; 6 studies; 605 teeth; very low-certainty evidence), analgesic use (RR 1.25 95% CI: 0.75-2.09; I 2 = 36%; 6 studies, 540 teeth; very low-certainty evidence) and sinus tract or fistula presence (RR 1.00, 95% CI: 0.24-4.28; I 2 = 0%; 5 studies, 650 teeth; very low-certainty evidence). Interestingly, however, there was evidence to show that more participants reported pain after 1 week following RoCT completed in a single visit, compared to those in multiple visit groups (RR 1.55, 95% CI: 1.14-2.09; I 2 = 18%; 5 studies, 638 teeth; moderate-certainty evidence). Subgroup analysis showed there was an increase in post-treatment pain after 1 week for RoCT carried out in a single visit on vital teeth (RR 2.16, 95% CI: 1.39-3.36; I 2 = 0%; 2 studies, 316 teeth), and with the use of mechanical instrumentation (RR 1.80, 95% CI: 1.10-2.92; I 2 = 56%; 2 studies, 278 teeth). The current evidence shows that RoCT carried out in a single visit is no more effective than RoCT carried out over multiple visits; after 12 months, there is no difference in pain or complications with either approach. However, single visit RoCT has been shown to have increased post-operative pain after 1 week compared to RoCT completed over multiple visits.

Is It Who You Are or Where You Are? Accounting for Compositional Differences in Cross-Site Treatment Effect Variation

In multisite trials, learning about treatment effect variation across sites is critical for understanding where and for whom a program works. Unadjusted comparisons, however, capture “compositional” differences in the distributions of unit-level features as well as “contextual” differences in site-level features, including possible differences in program implementation. Our goal in this article is to adjust site-level estimates for differences in the distribution of observed unit-level features: If we can reweight (or “transport”) each site to have a common distribution of observed unit-level covariates, the remaining treatment effect variation captures contextual and unobserved compositional differences across sites. This allows us to make apples-to-apples comparisons across sites, parceling out the amount of cross-site effect variation explained by systematic differences in populations served. In this article, we develop a framework for transporting effects using approximate balancing weights, where the weights are chosen to directly optimize unit-level covariate balance between each site and the common target distribution. We first develop our approach for the general setting of transporting the effect of a single-site trial. We then extend our method to multisite trials, assess its performance via simulation, and use it to analyze a series of multisite trials of adult education and vocational training programs. In our application, we find that distributional differences are potentially masking cross-site variation. Our method is available in the balancer R package.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption. Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD. We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS. Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.