Causally-interpretable meta-analysis: clearly-defined causal effects and two case studies

Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally-interpretable meta-analysis methods and apply their treatment effect estimators to two individual-participant data sets. These estimators transport estimated treatment effects from studies in the meta-analysis to a specified target population using individuals' potentially effect-modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated-data meta-analysis methods. In our applications, certain versions of the causally-interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally-interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally-interpretable approach gives meta-analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.Comment: 31 pages, 2 figures Submitted to Research Synthesis Method

Metacognition, Social Cognition, and Mentalizing In Psychosis: Are These Distinct Constructs When It Comes To Subjective Experience Or Are We Just Splitting Hairs?

Research using the integrated model of metacognition has suggested that the construct of metacognition could quantify the spectrum of activities that, if impaired, might cause many of the subjective disturbances found in psychosis. Research on social cognition and mentalizing in psychosis, however, has also pointed to underlying deficits in how persons make sense of their experience of themselves and others. To explore the question of whether metacognitive research in psychosis offers unique insight in the midst of these other two emerging fields, we have offered a review of the constructs and research from each field. Following that summary, we discuss ways in which research on metacognition may be distinguished from research on social cognition and mentalizing in three broad categories: (1) experimental procedures, (2) theoretical advances, and (3) clinical applications or indicated interventions. In terms of its research methods, we will describe how metacognition makes a unique contribution to understanding disturbances in how persons make sense of and interpret their own experiences within the flow of life. We will next discuss how metacognitive research in psychosis uniquely describes an architecture which when compromised – as often occurs in psychosis – results in the loss of persons’ sense of purpose, possibilities, place in the world and cohesiveness of self. Turning to clinical issues, we explore how metacognitive research offers an operational model of the architecture which if repaired or restored should promote the recovery of a coherent sense of self and others in psychosis. Finally, we discuss the concrete implications of this for recovery-oriented treatment for psychosis as well as the need for further research on the commonalities of these approaches

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging

The Periotest Method: Implant-Supported Framework Precision of Fit Evaluation

: In this study, the Periotest instrument was used to measure the precision of fit between cast high noble-metal frameworks and the supporting implants in a patient-simulation model. Three framework conditions and three implant-location variables were used to evaluate the rigidity of the assembly as measured by the Periotest method. The framework variables were (1) one-piece castings (OPC); (2) sectioned-soldered inaccurate castings (SSIC); and (3) sectioned-soldered accurate castings (SSAC). The implant-location variables were right anterior (RA), center (C), and left anterior (LA). Materials and Methods : The patient simulation model used consisted of three self-tapping BrÅnemark implants in a reasonable arch curvature in bovine bone. Three working casts were fabricated from the patient-simulation model using polyvinyl siloxane and tapered impression copings. From the working casts, three sets of three frameworks were fabricated as OPCs, SSICs, and SSACs using type 3 high noble alloy. The SSICs were fabricated with a quantitative misfit of 101.6 Μm at the facial surface, between the abutment-to-gold cylinder interface at the C implant location. Periotest value (PTV) measurements were made at the midfacial surface of the frameworks directly above each abutment-to-gold cylinder interface. Three measurements were made for each test condition. The data were analyzed to compare framework condition(s) and implant location(s) using ANOVA and Fisher's Protected Least Significant Difference Comparison Test. Results : The ANOVA showed that significant differences exist between the mean PTV data for framework condition and for implant location (p < .01). Significant differences were shown between the mean PTV data for the SSAC assemblies and the OPC and SSIC assemblies. The SSICs displayed a more positive (+) mean PTV than the OPCs. The OPC assemblies had a more positive mean PTV than the SSAC assemblies. The mean PTV data for the SSAC assemblies had a significantly different PTV (p < .01) than the other two framework condition assemblies. The OPC and the SSIC assemblies had PTVs that were not significantly different. The C implant location was significantly different from the RA and the LA implant locations (p < .01). The RA and the LA implant locations were not significantly different from each other. The C implant location always demonstrated the most positive mean PTV regardless of the framework condition being tested. Conclusions : The Periotest instrument quantified differences in the precision of fit between three framework conditions. The SSAC assemblies were significantly more rigid than the OPC and SSIC assemblies. The OPC and SSIC assemblies' mean PTVs were not significantly different. The mean PTVs for the C implant location and the RA and LA implant locations were significantly different (p < .01). The mean PTVs of the RA and LA implant locations were not significantly different. The implant-location PTVs followed the same rank order for all three framework conditions. The procedures used to fabricate a more precise fit between the framework and the supporting implants is influenced by the skill of the clinician and technician.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75096/1/j.1532-849X.1996.tb00298.x.pd

Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study

Background While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size. Methods Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome. Results 6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32–1.44), and mortality (RR 0.82, 0.41–1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28–0.98, p = 0.044) and mortality (RR 0.56, 95% CI 0.33–0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control. Conclusions There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide

Vaccination Against SARS-CoV-2 Is Associated With a Lower Viral Load and Likelihood of Systemic Symptoms

Background: Data conflict on whether vaccination decreases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load. The objective of this analysis was to compare baseline viral load and symptoms between vaccinated and unvaccinated adults enrolled in a randomized trial of outpatient coronavirus disease 2019 (COVID-19) treatment. Methods: Baseline data from the first 433 sequential participants enrolling into the COVID-OUT trial were analyzed. Adults aged 30-85 with a body mass index (BMI) ≥25 kg/m2 were eligible within 3 days of a positive SARS-CoV-2 test and <7 days of symptoms. Log10 polymerase chain reaction viral loads were normalized to human RNase P by vaccination status, by time from vaccination, and by symptoms. Results: Two hundred seventy-four participants with known vaccination status contributed optional nasal swabs for viral load measurement: median age, 46 years; median (interquartile range) BMI 31.2 (27.4-36.4) kg/m2. Overall, 159 (58%) were women, and 217 (80%) were White. The mean relative log10 viral load for those vaccinated <6 months from the date of enrollment was 0.11 (95% CI, -0.48 to 0.71), which was significantly lower than the unvaccinated group (P = .01). Those vaccinated ≥6 months before enrollment did not differ from the unvaccinated with respect to viral load (mean, 0.99; 95% CI, -0.41 to 2.40; P = .85). The vaccinated group had fewer moderate/severe symptoms of subjective fever, chills, myalgias, nausea, and diarrhea (all P < .05). Conclusions: These data suggest that vaccination within 6 months of infection is associated with a lower viral load, and vaccination was associated with a lower likelihood of having systemic symptoms

Randomized Trial of Metformin, Ivermectin, and Fluvoxamine for Covid-19

BACKGROUND Early treatment to prevent severe coronavirus disease 2019 (Covid-19) is an important component of the comprehensive response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. METHODS In this phase 3, double-blind, randomized, placebo-controlled trial, we used a 2-by-3 factorial design to test the effectiveness of three repurposed drugs - metformin, ivermectin, and fluvoxamine - in preventing serious SARS-CoV-2 infection in nonhospitalized adults who had been enrolled within 3 days after a confirmed diagnosis of infection and less than 7 days after the onset of symptoms. The patients were between the ages of 30 and 85 years, and all had either overweight or obesity. The primary composite end point was hypoxemia (≤93% oxygen saturation on home oximetry), emergency department visit, hospitalization, or death. All analyses used controls who had undergone concurrent randomization and were adjusted for SARSCoV-2 vaccination and receipt of other trial medications. RESULTS A total of 1431 patients underwent randomization; of these patients, 1323 were included in the primary analysis. The median age of the patients was 46 years; 56% were female (6% of whom were pregnant), and 52% had been vaccinated. The adjusted odds ratio for a primary event was 0.84 (95% confidence interval [CI], 0.66 to 1.09; P=0.19) with metformin, 1.05 (95% CI, 0.76 to 1.45; P=0.78) with ivermectin, and 0.94 (95% CI, 0.66 to 1.36; P=0.75) with fluvoxamine. In prespecified secondary analyses, the adjusted odds ratio for emergency department visit, hospitalization, or death was 0.58 (95% CI, 0.35 to 0.94) with metformin, 1.39 (95% CI, 0.72 to 2.69) with ivermectin, and 1.17 (95% CI, 0.57 to 2.40) with fluvoxamine. The adjusted odds ratio for hospitalization or death was 0.47 (95% CI, 0.20 to 1.11) with metformin, 0.73 (95% CI, 0.19 to 2.77) with ivermectin, and 1.11 (95% CI, 0.33 to 3.76) with fluvoxamine. CONCLUSIONS None of the three medications that were evaluated prevented the occurrence of hypoxemia, an emergency department visit, hospitalization, or death associated with Covid-19