Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.

Data Challenges for Externally Controlled Trials: Viewpoint.

The preferred evidence of a large randomized controlled trial is difficult to adopt in scenarios, such as rare conditions or clinical subgroups with high unmet needs, and evidence from external sources, including real-world data, is being increasingly considered by decision makers. Real-world data originate from many sources, and identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, has several challenges. In this viewpoint article, we provide an overview of the technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy, such as identification, outcome, and time selection challenges. By breaking down these challenges, we provide practical solutions for researchers to consider through the approaches of detailed planning, collection, and record linkage to analyze external data for comparative efficacy.

Data capture and sharing in the COVID-19 pandemic: a cause for concern.

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Economic Evaluation of Cost and Time Required for a Platform Trial vs Conventional Trials.

Importance: Platform trial design allows the introduction of new interventions after the trial is initiated and offers efficiencies to clinical research. However, limited guidance exists on the economic resources required to establish and maintain platform trials. Objective: To compare cost (US dollars) and time requirements of conducting a platform trial vs a series of conventional (nonplatform) trials using a real-life example. Design, Setting, and Participants: For this economic evaluation, an online survey was administered to a group of international experts (146 participants) with publication records of platform trials to elicit their opinions on cost and time to set up and conduct platform, multigroup, and 2-group trials. Using the reported entry dates of 10 interventions into Systemic Therapy in Advancing Metastatic Prostate Cancer: Evaluation of Drug Efficacy, the longest ongoing platform trial, 3 scenarios were designed involving a single platform trial (scenario 1), 1 multigroup followed by 5 2-group trials (scenario 2), and a series of 10 2-group trials (scenario 3). All scenarios started with 5 interventions, then 5 more interventions were either added to the platform or evaluated independently. Simulations with the survey results as inputs were used to compare the platform vs conventional trial designs. Data were analyzed from July to September 2021. Exposure: Platform trial design. Main Outcomes and Measures: Total trial setup and conduct cost and cumulative duration. Results: Although setup time and cost requirements of a single trial were highest for the platform trial, cumulative requirements of setting up a series of multiple trials in scenarios 2 and 3 were larger. Compared with the platform trial, there was a median (IQR) increase of 216.7% (202.2%-242.5%) in cumulative setup costs for scenario 2 and 391.1% (365.3%-437.9%) for scenario 3. In terms of total cost, there was a median (IQR) increase of 17.4% (12.1%-22.5%) for scenario 2 and 57.5% (43.1%-69.9%) for scenario 3. There was a median (IQR) increase in cumulative trial duration of 171.1% (158.3%-184.3%) for scenario 2 and 311.9% (282.0%-349.1%) for scenario 3. Cost and time reductions in the platform trial were observed in both the initial and subsequently evaluated interventions. Conclusions and Relevance: Although setting up platform trials can take longer and be costly, the findings of this study suggest that having a single infrastructure can improve efficiencies with respect to costs and efforts.

Data flow within global clinical trials: a scoping review.

OBJECTIVE: To document clinical trial data flow in global clinical trials published in major journals between 2013 and 2021 from Global South to Global North. DESIGN: Scoping analysis METHODS: We performed a search in Cochrane Central Register of Controlled Trials (CENTRAL) to retrieve randomised clinical trials published between 2013 and 2021 from The BMJ, BMJ Global Health, the Journal of the American Medical Association, the Lancet, Lancet Global Health and the New England Journal of Medicine. Studies were included if they involved recruitment and author affiliation across different country income groupings using World Bank definitions. The direction of data flow was extracted with a data collection tool using sites of trial recruitment as the starting point and the location of authors conducting statistical analysis as the ending point. RESULTS: Of 1993 records initially retrieved, 517 studies underwent abstract screening, 348 studies underwent full-text screening and 305 studies were included. Funders from high-income countries were the sole funders of the majority (82%) of clinical trials that recruited across income groupings. In 224 (73.4%) of all assessable studies, data flowed exclusively to authors affiliated with high-income countries or to a majority of authors affiliated with high-income countries for statistical analysis. Only six (3.2%) studies demonstrated data flow to lower middle-income countries and upper middle-income countries for analysis, with only one with data flow to a lower middle-income country. CONCLUSIONS: Global clinical trial data flow demonstrates a Global South to Global North trajectory. Policies should be re-examined to assess how data sharing across country income groupings can move towards a more equitable model.

Global incidence and prevalence of idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive debilitating lung disease with considerable morbidity. Heterogeneity in epidemiologic studies means the full impact of the disease is unclear. METHODS: A targeted literature search for population-based, observational studies reporting incidence and/or prevalence of IPF from January 2009 to April 2020 was conducted. Identified studies were aggregated by country. For countries with multiple publications, a weighted average was determined. Incidence and prevalence data were adjusted for between-study differences where possible. The final model included adjusted estimates of incidence and prevalence per 10,000 of the population with 95% confidence intervals. As prevalence estimates vary depending on the definitions used, estimates were based on a specific case definition of IPF. RESULTS: Overall, 22 studies covering 12 countries met the inclusion criteria, with 15 reporting incidence and 18 reporting prevalence estimates. The adjusted incidence estimates (per 10,000 of the population) ranged from 0.35 to 1.30 in Asia-Pacific countries, 0.09 to 0.49 in Europe, and 0.75 to 0.93 in North America. Unadjusted and adjusted incidence estimates were consistent. The adjusted prevalence estimates ranged from 0.57 to 4.51 in Asia-Pacific countries, 0.33 to 2.51 in Europe, and 2.40 to 2.98 in North America. South Korea had the highest incidence and prevalence estimates. When prevalence estimates were compared to country-specific rare disease thresholds, IPF met the definition of a rare disease in all countries except South Korea. There were notable geographic gaps for IPF epidemiologic data. CONCLUSIONS: Due to differences in study methodologies, there is worldwide variability in the reported incidence and prevalence of IPF. Based on the countries included in our analysis, we estimated the adjusted incidence and prevalence of IPF to be in the range of 0.09-1.30 and 0.33-4.51 per 10,000 persons, respectively. According to these prevalence estimates, IPF remains a rare disease. For consistency, future epidemiologic studies of IPF should take age, sex, smoking status, and the specificity of case definitions into consideration.

The Need for a Practical Approach to Evaluate the Effectiveness of COVID-19 Vaccines for Low- and Middle-Income Countries.

The global demand for coronavirus disease 2019 (COVID-19) vaccines currently far outweighs the available global supply and manufacturing capacity. As a result, securing doses of vaccines for low- and middle-income countries has been challenging, particularly for African countries. Clinical trial investigation for COVID-19 vaccines has been rare in Africa, with the only randomized clinical trials (RCTs) for COVID-19 vaccines having been conducted in South Africa. In addition to addressing the current inequities in the vaccine roll-out for low- and middle-income countries, there is a need to monitor the real-world effectiveness of COVID-19 vaccines in these regions. Although RCTs are the superior method for evaluating vaccine efficacy, the feasibility of conducting RCTs to monitor COVID-19 vaccine effectiveness during mass vaccine campaigns will likely be low. There is still a need to evaluate the effectiveness of mass COVID-19 vaccine distribution in a practical manner. We discuss how target trial emulation, the application of trial design principles from RCTs to the analysis of observational data, can be used as a practical, cost-effective way to evaluate real-world effectiveness for COVID-19 vaccines. There are several study design considerations that need to be made in the analyses of observational data, such as uncontrolled confounders and selection biases. Target trial emulation accounts for these considerations to improve the analyses of observational data. The framework of target trial emulation provides a practical way to monitor the effectiveness of mass vaccine campaigns for COVID-19 using observational data.

Moving forward in clinical research with master protocols.

With billions of dollars in research and development (R&D) funding continuing to be invested, the novel coronavirus disease 2019 (COVID-19) has become into a singular focus for the scientific community. However, the collective response from the scientific communities have seen poor return on investment, particularly for therapeutic research for COVID-19, revealing the existing weaknesses and inefficiencies of the clinical trial enterprise. In this article, we argue for the importance of structural changes to existing research programs for clinical trials in light of the lessons learned from COVID-19.

The role and challenges of cluster randomised trials for global health.

Evaluating whether an intervention works when trialled in groups of individuals can pose complex challenges for clinical research. Cluster randomised controlled trials involve the random allocation of groups or clusters of individuals to receive an intervention, and they are commonly used in global health research. In this paper, we describe the potential reasons for the increasing popularity of cluster trials in low-income and middle-income countries. We also draw on key areas of global health research for an assessment of common trial planning practices, and we address their methodological shortcomings and pitfalls. Lastly, we discuss alternative approaches for population-level intervention trials that could be useful for research undertaken in low-income and middle-income countries for situations in which the use of cluster randomisation might not be appropriate.

Clinical Trial Data Sharing for COVID-19-Related Research.

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

The Landscape of Emerging Randomized Clinical Trial Evidence for COVID-19 Disease Stages: A Systematic Review of Global Trial Registries.

PURPOSE: A multitude of randomized controlled trials (RCTs) have emerged in response to the novel coronavirus disease (COVID-19) pandemic. Understanding the distribution of trials among various settings is important to guide future research priorities and efforts. The purpose of this review was to describe the emerging evidence base of COVID-19 RCTs by stages of disease progression, from pre-exposure to hospitalization. METHODS: We collated trial data across international registries: ClinicalTrials.gov; International Standard Randomised Controlled Trial Number Registry; Chinese Clinical Trial Registry; Clinical Research Information Service; EU Clinical Trials Register; Iranian Registry of Clinical Trials; Japan Primary Registries Network; German Clinical Trials Register (up to 7 October 2020). Active COVID-19 RCTs in international registries were eligible for inclusion. We extracted trial status, intervention(s), control, sample size, and clinical context to generate descriptive frequencies, network diagram illustrations, and statistical analyses including odds ratios and the Mann-Whitney U-test. RESULTS: Our search identified 11503 clinical trials registered for COVID-19 and identified 2388 RCTs. After excluding 45 suspended RCTs and 480 trials with unclear or unreported disease stages, 1863 active RCTs were included and categorized into four broad disease stages: pre-exposure (n=107); post-exposure (n=208); outpatient treatment (n=266); hospitalization, including the intensive care unit (n=1376). Across all disease stages, most trials had two arms (n=1500/1863, 80.52%), most often included (hydroxy)chloroquine (n=271/1863, 14.55%) and were US-based (n=408/1863, 21.90%). US-based trials had lower odds of including (hydroxy)chloroquine than trials in other countries (OR: 0.63, 95% CI: 0.45-0.90) and similar odds of having two arms compared to other geographic regions (OR: 1.05, 95% CI: 0.80-1.38). CONCLUSION: There is a marked difference in the number of trials across settings, with limited studies on non-hospitalized persons. Focus on pre- and post-exposure, and outpatients, is worthwhile as a means of reducing infections and lessening the health, social, and economic burden of COVID-19.

Utilizing Bayesian predictive power in clinical trial design.

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.

Prevalence and incidence of HIV among female sex workers and their clients: modelling the potential effects of intervention in Rwanda.

BACKGROUND: Rwanda has identified several targeted HIV prevention strategies, such as promotion of condom use and provision of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) for female sex workers (FSWs). Given this country's limited resources, understanding how the HIV epidemic will be affected by these strategies is crucial. METHODS: We developed a Markov model to estimate the effects of targeted strategies to FSWs on the HIV prevalence/incidence in Rwanda from 2017 to 2027. Our model consists of the six states: HIV-; HIV+ undiagnosed/diagnosed pre-ART; HIV+ diagnosed with/without ART; and death. We considered three populations: FSWs, sex clients and the general population. For the period 2017-2027, the HIV epidemic among each of these population was estimated using Rwanda's demographic, sexual risk behaviour and HIV-associated morbidity and mortality data. RESULTS: Between 2017 and 2027, with no changes in the current condom and ART use, the overall number of people living with HIV is expected to increase from 344,971 to 402,451. HIV incidence will also decrease from 1.36 to 1.20 100 person-years. By 2027, a 30% improvement in consistent condom use among FSWs will result in absolute reduction of HIV prevalence among FSWs, sex clients and the general population by 7.86%, 5.97% and 0.17%, respectively. While recurring HIV testing and improving the ART coverage mildly reduced the prevalence/incidence among FSWs and sex clients, worsening the two (shown by our worst-case scenario) will result in an increase in the HIV prevalence/incidence among FSWs and sex clients. Introduction of PrEP to FSWs in 2019 will reduce the HIV incidence among FSWs by 1.28%. CONCLUSIONS: Continued efforts toward improving condom and ART use will be critical for Rwanda to continue their HIV epidemic control. Implementing a targeted intervention strategy in PrEP for FSWs will reduce the HIV epidemic in this high-risk population.

Accelerating Clinical Evaluation of Repurposed Combination Therapies for COVID-19.

As the global COVID-19 pandemic continues, unabated and clinical trials demonstrate limited effective pharmaceutical interventions, there is a pressing need to accelerate treatment evaluations. Among options for accelerated development is the evaluation of drug combinations in the absence of prior monotherapy data. This approach is appealing for a number of reasons. First, combining two or more drugs with related or complementary therapeutic effects permits a multipronged approach addressing the variable pathways of the disease. Second, if an individual component of a combination offers a therapeutic effect, then in the absence of antagonism, a trial of combination therapy should still detect individual efficacy. Third, this strategy is time saving. Rather than taking a stepwise approach to evaluating monotherapies, this strategy begins with testing all relevant therapeutic options. Finally, given the severity of the current pandemic and the absence of treatment options, the likelihood of detecting a treatment effect with combination therapy maintains scientific enthusiasm for evaluating repurposed treatments. Antiviral combination selection can be facilitated by insights regarding SARS-CoV-2 pathophysiology and cell cycle dynamics, supported by infectious disease and clinical pharmacology expert advice. We describe a clinical evaluation strategy using adaptive combination platform trials to rapidly test combination therapies to treat COVID-19.

The Efficacy and Safety of Piribedil Relative to Pramipexole for the Treatment of Early Parkinson Disease: A Systematic Literature Review and Network Meta-Analysis.

OBJECTIVES: Patients with early Parkinson disease (PD) frequently defer initiation of levodopa treatment to minimize long-term complications. Nonergoline dopamine agonists, such as pramipexole and piribedil, are frequent first-line therapies for early PD patients, yet limited head-to-head randomized controlled trial (RCT) evidence exists for dopamine agonists in this population. We therefore conducted a systematic literature review and network meta-analysis. METHODS: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched (until January 7, 2020), identifying RCTs assessing the efficacy of piribedil or pramipexole in early PD. Eligible trial data were incorporated into fixed- and random-effects Bayesian network meta-analyses. RESULTS: No RCTs were identified directly comparing piribedil with pramipexole, but 6 trials provided data for pramipexole versus placebo and 2 compared piribedil versus placebo, facilitating indirect comparisons. Across all time points assessed, no significant differences were found between pramipexole and piribedil for change in the Unified Parkinson's Disease Rating Scale (UPDRS) score from baseline. Piribedil and pramipexole demonstrated superiority relative to placebo for UPDRS II/III change at weeks 22 to 30. No significant differences were noted between the treatments at weeks 20 to 35 for anxiety, constipation, hypotension, nausea, and somnolence. Sensitivity analyses on adjustment for dose titration periods and baseline risk yielded the same pattern of results. CONCLUSIONS: No significant differences were found for pramipexole versus piribedil in the UPDRS II/III scores from baseline in early PD, with similar safety profiles.

Synthetic and External Controls in Clinical Trials - A Primer for Researchers.

There has been a rapid expansion in the use of non-randomized evidence in the regulatory approval of treatments globally. An emerging set of methodologies have been utilized to provide greater insight into external control data used for these purposes, collectively known as synthetic control methods. Through this paper, we provide the reader with a set of key questions to help assess the quality of literature publications utilizing synthetic control methodologies. Common challenges and real-life examples of synthetic controls are provided throughout, alongside a critical appraisal framework with which to assess future publications.

An overview of platform trials with a checklist for clinical readers.

OBJECTIVES: The objective of the study was to outline key considerations for general clinical readers when critically evaluating publications on platform trials and for researchers when designing these types of clinical trials. STUDY DESIGN AND SETTING: In this review, we describe key concepts of platform trials with case study discussion of two hallmark platform trials in STAMPEDE and I-SPY2. We provide reader's guide to platform trials with a critical appraisal checklist. RESULTS: Platform trials offer flexibilities of dropping ineffective arms early based on interim data and introducing new arms into the trial. For platform trials, it is important to consider how interventions are compared and evaluated throughout and how new interventions are introduced. For intervention comparisons, it is important to consider what the primary analysis is, what and how many interventions are active simultaneously, and allocation between different arms. Interim evaluation considerations should include the number and timing of interim evaluations and outcomes and statistical rules used to drop interventions. New interventions are usually introduced based on scientific merits, so consideration of these merits is important, together with the timing and mechanisms in which new interventions are added. CONCLUSION: More efforts are needed to improve the scientific literacy of platform trials. Our review provides an overview of the important concepts of platform trials.