High-cited favorable studies for COVID-19 treatments ineffective in large trials

Importance. COVID-19 has resulted in massive production, publication and wide dissemination of clinical studies trying to identify effective treatments. However, several widely touted treatments failed to show effectiveness in large well-done randomized controlled trials (RCTs). Objective. To evaluate for COVID-19 treatments that showed no benefits in subsequent large RCTs how many of their most-cited clinical studies had declared favorable results for these interventions. Methods. Scopus (last update December 23, 2021) identified articles on lopinavir-ritonavir, hydroxycholoroquine/azithromycin, remdesivir, convalescent plasma, colchicine or interferon (index interventions) that represented clinical trials and that had received >150 citations. Their conclusions were assessed and correlated with study design features. The ten most recent citations for the most-cited article on each index intervention were examined on whether they were critical to the highly-cited study. Altmetric scores were also obtained. Findings. 40 articles of clinical studies on these index interventions had received >150 citations (7 exceeded 1,000 citations). 20/40 (50%) had favorable conclusions and 4 were equivocal. Highly-cited articles with favorable conclusions were rarely RCTs while those without favorable conclusions were mostly RCTs (3/20 vs 15/20, p=0.0003). Only 1 RCT with favorable conclusions had sample size >160. Citation counts correlated strongly with Altmetric scores, in particular news items. Only 9 (15%) of 60 recent citations to the most highly-cited studies with favorable or equivocal conclusions were critical to the highly-cited study. Conclusion. Many clinical studies with favorable conclusions for largely ineffective COVID-19 treatments are uncritically heavily cited and disseminated. Early observational studies and small randomized trials may cause spurious claims of effectiveness that get perpetuated.

The Contribution of “Individual Participant Data” Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review

While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, “individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a systematic review of IPD meta-analyses on psychological treatments of depression to provide an overview of predictors and moderators identified. We included 10 (eight pairwise and two network) IPD meta-analyses. Six meta-analyses showed that higher baseline depression severity was associated with better outcomes, and two found that older age was associated with better outcomes. Because power was high in most IPD meta-analyses, non-significant findings are also of interest because they indicate that these variables are probably not relevant as predictors and moderators. We did not find in any IPD meta-analysis that gender, education level, or relationship status were significant predictors or moderators. This review shows that IPD meta-analyses on psychological treatments can identify predictors and moderators of treatment effects and thereby contribute considerably to the development of personalized treatments of depression.

Conformal properties of hyperinvariant tensor networks

Hyperinvariant tensor networks (hyMERA) were introduced as a way to combine the successes of perfect tensor networks (HaPPY) and the multiscale entanglement renormalization ansatz (MERA) in simulations of the AdS/CFT correspondence. Although this new class of tensor network shows much potential for simulating conformal field theories arising from hyperbolic bulk manifolds with quasiperiodic boundaries, many issues are unresolved. In this manuscript we analyze the challenges related to optimizing tensors in a hyMERA with respect to some quasiperiodic critical spin chain, and compare with standard approaches in MERA. Additionally, we show two new sets of tensor decompositions which exhibit different properties from the original construction, implying that the multitensor constraints are neither unique, nor difficult to find, and that a generalization of the analytical tensor forms used up until now may exist. Lastly, we perform randomized trials using a descending superoperator with several of the investigated tensor decompositions, and find that the constraints imposed on the spectra of local descending superoperators in hyMERA are compatible with the operator spectra of several minimial model CFTs.

Hyperthermic Intraperitoneal Chemotherapy in the Treatment Armamentarium of Epithelial Ovarian Cancer: Time to End the Dichotomy

<b><i>Background:</i></b> Advanced epithelial ovarian cancer (EOC) is an incurable disease with over 75% of the patients developing recurrence in the peritoneum. Hyperthermic intraperitoneal chemotherapy (HIPEC) is a promising treatment option for both first-line therapy and treatment of recurrence. In this article, we review the rationale and current evidence for performing HIPEC and the role of HIPEC in the light of targeted systemic therapies. <b><i>Summary:</i></b> There are few randomized trials and several retrospective studies on the role of HIPEC in the management of EOC. A 12-month-overall survival (OS) benefit of the addition of HIPEC to interval cytoreductive surgery (CRS) was demonstrated in 1 randomized trial following which HIPEC has been included as a treatment option for this indication in several national/international guidelines. One retrospective propensity score-matched analysis showed a 16-month OS benefit of adding HIPEC to primary CRS. One randomized trial showed no benefit of the addition of carboplatin HIPEC to secondary CRS over secondary CRS alone. For patients undergoing primary CRS and secondary CRS for recurrence, the results of ongoing randomized trials are needed to define the role of HIPEC in these situations. All clinical trials have shown that the morbidity of HIPEC performed after CRS is acceptable. Along with the emergence of HIPEC as a promising surgical therapy, targeted therapies like bevacizumab and poly adenosine diphosphate-ribose polymerase inhibitors have been developed that have shown a survival benefit in selected patients. In principle, HIPEC and targeted therapies work in different ways and it is plausible to assume that their benefit could be additive, and their combination should be evaluated in clinical trials. The impact of prognostic factors like the disease extent, pathological response to systemic chemotherapy (SC), the histological subtype and molecular profile on the benefit of HIPEC, and targeted therapies has not been evaluated in clinical trials. <b><i>Key Messages:</i></b> HIPEC is an important therapeutic strategy in the treatment of EOC. While its role in patients undergoing interval CRS has been established, the results of ongoing randomized trials are needed to define its benefit at other time points. The morbidity of HIPEC in addition to CRS is acceptable. More research is needed to define subgroups that benefit most from HIPEC based on the extent of disease, response to SC, histology, and molecular profile. The combination of HIPEC and maintenance therapies should be evaluated in well-designed randomized clinical trials that evaluate not just the survival benefit and morbidity but also the cost-effectiveness of each therapy.

Impact of the COVID-19 Pandemic on Non-COVID-19 Clinical Trials

Randomized controlled trials (RCT) were impacted by the COVID-19 pandemic, but no systematic analysis has evaluated the overall impact of COVID-19 on non-COVID-19-related RCTs. The ClinicalTrials.gov database was queried in February 2020. Eligible studies included all randomized trials with a start date after 1 January 2010 and were active during the period from 1 January 2015 to 31 December 2020. The effect of the pandemic period on non-COVID-19 trials was determined by piece-wise regression models using 11 March 2020 as the start of the pandemic and by time series analysis (models fitted using 2015–2018 data and forecasted for 2019–2020). The study endpoints were early trial stoppage, normal trial completion, and trial activation. There were 161,377 non-COVID-19 trials analyzed. The number of active trials increased annually through 2019 but decreased in 2020. According to the piece-wise regression models, trial completion was not affected by the pandemic (p = 0.56) whereas trial stoppage increased (p = 0.001). There was a pronounced decrease in trial activation early during the pandemic (p < 0.001) which then recovered. The findings from the time series models were consistent comparing forecasted and observed results (trial completion p = 0.22; trial stoppage p < 0.01; trial activation, p = 0.01). During the pandemic, there was an increase in non-COVID-19 RCTs stoppage without changes in RCT completion. There was a sharp decline in new RCTs at the beginning of the pandemic, which later recovered.

Indications and Parameters Around Postoperative Radiation Therapy for Lung Cancer

Patients with locally advanced resected non–small-cell lung cancer present a high risk of relapse. Although adjuvant platinum–based chemotherapy has become the standard of care, the role of postoperative radiation therapy (PORT) has been controversial for years. In patients with incomplete resection, PORT should be proposed, on the basis of a strong consensus, despite the absence of randomized evidence. In patients with completely resected (R0) non–small-cell lung cancer, a meta-analysis showed poorer outcomes after PORT in the absence of mediastinal involvement (pN0 and pN1). In patients with pN2, the role of PORT was less clear and required further research. The meta-analysis included trials using older radiation techniques and poorer quality of surgery according to today's standards, and selection of patients was not positron emission tomography–based. Newer retrospective and nonrandomized studies and subgroup analyses of randomized trials evaluating adjuvant chemotherapy suggested a survival benefit of PORT in patients with pN2 R0. Two recent randomized trials (Lung ART and PORT-C) evaluating conformal PORT versus no PORT retrieved no disease-free survival advantage for stage IIIA-N2 patients, even if mediastinal relapse was significantly decreased with PORT. PORT had no effect on survival, possibly given the high rate of distant relapse and risk of additional cardiopulmonary toxicity. Ongoing and future analyses are planned in Lung ART to identify patients for whom PORT could be recommended. Incorporation of newer systemic treatments (immune checkpoint inhibitors or targeted therapy in oncogene-addicted patients) is underway in the neoadjuvant and/or adjuvant setting. Better identification of patients at a high risk of disease recurrence, with analysis of circulating tumor DNA, on the basis of the detection of postsurgical minimal (or molecular) residual disease is warranted in future studies.

A Comparison of Covariate Adjustment Approaches Under Model Misspecification In Individually Randomized Trials

Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on small to medium-sized individually randomized trials, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate-outcome relationship or through an omitted covariate-treatment interaction. Specifically, we aim to identify potential settings where G-computation, Inverse Probability of Treatment Weighting ( IPTW ), Augmented Inverse Probability of Treatment Weighting ( AIPTW ) and Targeted Maximum Likelihood Estimation ( TMLE ) offer improvement over the commonly used Analysis of Covariance ( ANCOVA ). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate-treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW ) show improved results compared to ANCOVA . When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; development of small sample corrections is needed.

A New Multistate Transition Model For Effect Estimation In Randomized Trials With Treatment Switching And A Cured Subgroup

Background: Many methods, including multistate models, have been proposed in the literature to estimate the treatment effect on overall survival in randomized trials with treatment switching permit after the disease progression. Nevertheless, the cured fraction of patients has not been considered. The cured would never experience the progressive disease, but they may suffer death with a hazard comparable to that of people without the disease. With the mix of the cured subgroup, existing methods yield highly biased effect estimation and fail to reflect the truth in uncured patients. Methods: In this paper, we propose a new multistate transition model to incorporate the cure, progression, treatment switching, and death states during trials. In the proposed model, the probability of cure and the death hazard of the cured are modeled separately. For the not cured patients, the semi-competing risks model is used with the treatment effect evaluated via transitional hazards between states. The particle swarm optimization algorithm is adopted to estimate the model parameters. Results: Extensive simulation studies have been conducted to evaluate the performance of the proposed multistate model and compare it with existing treatment switching adjustment methods. Results show that in all scenarios, the treatment effect estimation of the proposed model is more accurate than that of existing treatment switching adjustment methods. Besides, the application to diffuse large B-cell lymphoma data has also illustrated the superiority of the proposed model.Conclusions: The superiority and robustness of the proposed multistate transition model qualify it to estimate the treatment effect in trials with the treatment switching permit after progression and a cured subgroup.