Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only. [ FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)-a systematic review protocol.

BACKGROUND: Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system with an increasing worldwide prevalence. Since 1993, more than 15 disease-modifying immunotherapies (DMTs) have been licenced and have shown moderate efficacy in clinical trials. Based on the heterogeneity of the disease and the partial effectiveness of therapies, a personalised medicine approach would be valuable taking individual prognosis and suitability of a chosen therapy into account to gain the best possible treatment effect. The primary objective of this review is to assess the differential treatment effects of all approved DMTs in subgroups of adults with clinically isolated syndrome or relapsing forms of MS. We will analyse possible treatment effect modifiers (TEM) defined by baseline demographic characteristics (gender, age), and diagnostic (i.e. MRI measures) and clinical (i.e. relapses, disability level) measures of MS disease activity. METHODS: We will include all published and accessible unpublished primary and secondary analyses of randomised controlled trials (RCTs) with a follow-up of at least 12 months investigating the efficacy of at least one approved DMT, with placebo or other approved DMTs as control intervention(s) in subgroups of trial participants. As the primary outcome, we will address disability as defined by the Expanded Disability Status Scale or multiple sclerosis functional composite scores followed by relapse frequency, quality of life measures, and side effects. MRI data will be analysed as secondary outcomes. MEDLINE, EMBASE, CINAHL, LILACS, CENTRAL and major trial registers will be searched for suitable studies. Titles and abstracts and full texts will be screened by two persons independently using Covidence. The risk of bias will be analysed based on the Cochrane "Risk of Bias 2" tool, and the certainty of evidence will be assessed using GRADE. Treatment effects will be reported as rate ratio or odds ratio. Primary analyses will follow the intention-to-treat principle. Meta-analyses will be carried out using random-effects models. DISCUSSION: Given that individual patient data from clinical studies are often not available, the review will allow to analyse the evidence on TEM in MS immunotherapy and thus support clinical decision making in individual cases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021279665 .

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Acute respiratory infections in an adult refugee population: an observational study.

The presence of acute infectious respiratory diseases (ARD) is one of the main reasons why recently arrived refugees seek medical help. This paper investigates the incidence rates of acute respiratory diseases in an adult refugee population as well as associated sociodemographic factors and drug treatments. We conducted a retrospective observational study of deidentified medical records. The data were collected between 2015 and 2019 in the health care centers of two large German initial reception centers for refugees. Multivariable analyses controlling for sociodemographics were carried out using generalized estimating equations. Out of 10,431 eligible residents, 6965 medical encounters of 2840 adult patients were recorded over 30 months. Of all the adult patients, 34.4% sought medical help for a respiratory symptom or diagnosis at least once. Older patients and patients from Sub-Saharan Africa sought help less often. The occurrence of ARD showed a typical distribution over the course of the year. Facility occupancy was not associated with ARD occurrence. Acute respiratory symptoms are a leading cause for adult refugee patients to seek medical care. The doctor contact rates due to ARD were consistently two to three times higher among refugees than among German residents.

Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events.

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

Validation of a Prospective Urinalysis-Based Prediction Model for ICU Resources and Outcome of COVID-19 Disease: A Multicenter Cohort Study.

In COVID-19, guidelines recommend a urinalysis on hospital admission as SARS-CoV-2 renal tropism, post-mortem, was associated with disease severity and mortality. Following the hypothesis from our pilot study, we now validate an algorithm harnessing urinalysis to predict the outcome and the need for ICU resources on admission to hospital. Patients were screened for urinalysis, serum albumin (SA) and antithrombin III activity (AT-III) obtained prospectively on admission. The risk for an unfavorable course was categorized as (1) "low", (2) "intermediate" or (3) "high", depending on (1) normal urinalysis, (2) abnormal urinalysis with SA ≥ 2 g/dL and AT-III ≥ 70%, or (3) abnormal urinalysis with SA or AT-III abnormality. Time to ICU admission or death served as the primary endpoint. Among 223 screened patients, 145 were eligible for enrollment, 43 falling into the low, 84 intermediate, and 18 into high-risk categories. An abnormal urinalysis significantly elevated the risk for ICU admission or death (63.7% vs. 27.9%; HR 2.6; 95%-CI 1.4 to 4.9; p = 0.0020) and was 100% in the high-risk group. Having an abnormal urinalysis was associated with mortality, a need for mechanical ventilation, extra-corporeal membrane oxygenation or renal replacement therapy. In conclusion, our data confirm that COVID-19-associated urine abnormalities on admission predict disease aggravation and the need for ICU (ClinicalTrials.gov number NCT04347824).

Heart failure with preserved ejection fraction according to the HFA-PEFF score in COVID-19 patients: clinical correlates and echocardiographic findings.

AIMS: Viral-induced cardiac inflammation can induce heart failure with preserved ejection fraction (HFpEF)-like syndromes. COVID-19 can lead to myocardial damage and vascular injury. We hypothesised that COVID-19 patients frequently develop a HFpEF-like syndrome, and designed this study to explore this. METHODS AND RESULTS: Cardiac function was assessed in 64 consecutive, hospitalized, and clinically stable COVID-19 patients from April-November 2020 with left ventricular ejection fraction (LVEF) ≥50% (age 56 ± 19 years, females: 31%, severe COVID-19 disease: 69%). To investigate likelihood of HFpEF presence, we used the HFA-PEFF score. A low (0-1 points), intermediate (2-4 points), and high (5-6 points) HFA-PEFF score was observed in 42%, 33%, and 25% of patients, respectively. In comparison, 64 subjects of similar age, sex, and comorbidity status without COVID-19 showed these scores in 30%, 66%, and 4%, respectively (between groups: P = 0.0002). High HFA-PEFF scores were more frequent in COVID-19 patients than controls (25% vs. 4%, P = 0.001). In COVID-19 patients, the HFA-PEFF score significantly correlated with age, estimated glomerular filtration rate, high-sensitivity troponin T (hsTnT), haemoglobin, QTc interval, LVEF, mitral E/A ratio, and H2 FPEF score (all P < 0.05). In multivariate, ordinal regression analyses, higher age and hsTnT were significant predictors of increased HFA-PEFF scores. Patients with myocardial injury (hsTnT ≥14 ng/L: 31%) vs. patients without myocardial injury, showed higher HFA-PEFF scores [median 5 (interquartile range 3-6) vs. 1 (0-3), P < 0.001] and more often showed left ventricular diastolic dysfunction (75% vs. 27%, P < 0.001). CONCLUSION: Hospitalized COVID-19 patients frequently show high likelihood of presence of HFpEF that is associated with cardiac structural and functional alterations, and myocardial injury. Detailed cardiac assessments including echocardiographic determination of left ventricular diastolic function and biomarkers should become routine in the care of hospitalized COVID-19 patients.

Ferric carboxymaltose for iron deficiency at discharge after acute heart failure: a multicentre, double-blind, randomised, controlled trial.

BACKGROUND: Intravenous ferric carboxymaltose has been shown to improve symptoms and quality of life in patients with chronic heart failure and iron deficiency. We aimed to evaluate the effect of ferric carboxymaltose, compared with placebo, on outcomes in patients who were stabilised after an episode of acute heart failure. METHODS: AFFIRM-AHF was a multicentre, double-blind, randomised trial done at 121 sites in Europe, South America, and Singapore. Eligible patients were aged 18 years or older, were hospitalised for acute heart failure with concomitant iron deficiency (defined as ferritin <100 µg/L, or 100-299 µg/L with transferrin saturation <20%), and had a left ventricular ejection fraction of less than 50%. Before hospital discharge, participants were randomly assigned (1:1) to receive intravenous ferric carboxymaltose or placebo for up to 24 weeks, dosed according to the extent of iron deficiency. To maintain masking of patients and study personnel, treatments were administered in black syringes by personnel not involved in any study assessments. The primary outcome was a composite of total hospitalisations for heart failure and cardiovascular death up to 52 weeks after randomisation, analysed in all patients who received at least one dose of study treatment and had at least one post-randomisation data point. Secondary outcomes were the composite of total cardiovascular hospitalisations and cardiovascular death; cardiovascular death; total heart failure hospitalisations; time to first heart failure hospitalisation or cardiovascular death; and days lost due to heart failure hospitalisations or cardiovascular death, all evaluated up to 52 weeks after randomisation. Safety was assessed in all patients for whom study treatment was started. A pre-COVID-19 sensitivity analysis on the primary and secondary outcomes was prespecified. This study is registered with ClinicalTrials.gov, NCT02937454, and has now been completed. FINDINGS: Between March 21, 2017, and July 30, 2019, 1525 patients were screened, of whom 1132 patients were randomly assigned to study groups. Study treatment was started in 1110 patients, and 1108 (558 in the carboxymaltose group and 550 in the placebo group) had at least one post-randomisation value. 293 primary events (57·2 per 100 patient-years) occurred in the ferric carboxymaltose group and 372 (72·5 per 100 patient-years) occurred in the placebo group (rate ratio [RR] 0·79, 95% CI 0·62-1·01, p=0·059). 370 total cardiovascular hospitalisations and cardiovascular deaths occurred in the ferric carboxymaltose group and 451 occurred in the placebo group (RR 0·80, 95% CI 0·64-1·00, p=0·050). There was no difference in cardiovascular death between the two groups (77 [14%] of 558 in the ferric carboxymaltose group vs 78 [14%] in the placebo group; hazard ratio [HR] 0·96, 95% CI 0·70-1·32, p=0·81). 217 total heart failure hospitalisations occurred in the ferric carboxymaltose group and 294 occurred in the placebo group (RR 0·74; 95% CI 0·58-0·94, p=0·013). The composite of first heart failure hospitalisation or cardiovascular death occurred in 181 (32%) patients in the ferric carboxymaltose group and 209 (38%) in the placebo group (HR 0·80, 95% CI 0·66-0·98, p=0·030). Fewer days were lost due to heart failure hospitalisations and cardiovascular death for patients assigned to ferric carboxymaltose compared with placebo (369 days per 100 patient-years vs 548 days per 100 patient-years; RR 0·67, 95% CI 0·47-0·97, p=0·035). Serious adverse events occurred in 250 (45%) of 559 patients in the ferric carboxymaltose group and 282 (51%) of 551 patients in the placebo group. INTERPRETATION: In patients with iron deficiency, a left ventricular ejection fraction of less than 50%, and who were stabilised after an episode of acute heart failure, treatment with ferric carboxymaltose was safe and reduced the risk of heart failure hospitalisations, with no apparent effect on the risk of cardiovascular death. FUNDING: Vifor Pharma.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19.

The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.

Factors Influencing the Frequency of Airway Infections in Underage Refugees: A Retrospective, Cross Sectional Study.

Background: Infections are a leading cause of refugee morbidity. Recent data on the rate of airway infections and factors influencing their spread in refugee reception centers is scarce. Methods: A retrospective, cross-sectional study of de-identified medical records with a focus on respiratory infections in underage refugees was conducted at two large German refugee reception centers. Results: In total, medical data from n = 10,431 refugees over an observational period of n = 819 days was analyzed. Among pediatric patients (n = 4289), 55.3% presented at least once to the on-site medical ward with an acute respiratory infection or signs thereof. In 38.4% of pediatric consultations, acute airway infections or signs thereof were present. Airway infections spiked during colder months and were significantly more prevalent amongst preschool and resettled children. Their frequency displayed a positive correlation with the number of refugees housed at the reception centers. Conclusions: We show that respiratory infections are a leading cause for morbidity in young refugees and that their rate is influenced age, season, status, and residential density. This illustrates the need to protect refugee children from contracting airway infections which may also reduce the spread of coronavirus disease 2019 (COVID-19) during the current pandemic.

Data monitoring committees for clinical trials evaluating treatments of COVID-19.

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.

Impact of COVID-19 outbreak on regional STEMI care in Germany.

AIMS: To assess the impact of the lockdown due to coronavirus disease 2019 (COVID-19) on key quality indicators for the treatment of ST-segment elevation myocardial infarction (STEMI) patients. METHODS: Data were obtained from 41 hospitals participating in the prospective Feedback Intervention and Treatment Times in ST-Elevation Myocardial Infarction (FITT-STEMI) study, including 15,800 patients treated for acute STEMI from January 2017 to the end of March 2020. RESULTS: There was a 12.6% decrease in the total number of STEMI patients treated at the peak of the pandemic in March 2020 as compared to the mean number treated in the March months of the preceding years. This was accompanied by a significant difference among the modes of admission to hospitals (p = 0.017) with a particular decline in intra-hospital infarctions and transfer patients from other hospitals, while the proportion of patients transported by emergency medical service (EMS) remained stable. In EMS-transported patients, predefined quality indicators, such as percentages of pre-hospital ECGs (both 97%, 95% CI = - 2.2-2.7, p = 0.846), direct transports from the scene to the catheterization laboratory bypassing the emergency department (68% vs. 66%, 95% CI = - 4.9-7.9, p = 0.641), and contact-to-balloon-times of less than or equal to 90 min (58.3% vs. 57.8%, 95%CI = - 6.2-7.2, p = 0.879) were not significantly altered during the COVID-19 crisis, as was in-hospital mortality (9.2% vs. 8.5%, 95% CI = - 3.2-4.5, p = 0.739). CONCLUSIONS: Clinically important indicators for STEMI management were unaffected at the peak of COVID-19, suggesting that the pre-existing logistic structure in the regional STEMI networks preserved high-quality standards even when challenged by a threatening pandemic. CLINICAL TRIAL REGISTRATION: NCT00794001.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Conducting clinical trials in heart failure during (and after) the COVID-19 pandemic: an Expert Consensus Position Paper from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC).

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has important implications for the safety of participants in clinical trials and the research staff caring for them and, consequently, for the trials themselves. Patients with heart failure may be at greater risk of infection with COVID-19 and the consequences might also be more serious, but they are also at risk of adverse outcomes if their clinical care is compromised. As physicians and clinical trialists, it is our responsibility to ensure safe and effective care is delivered to trial participants without affecting the integrity of the trial. The social contract with our patients demands no less. Many regulatory authorities from different world regions have issued guidance statements regarding the conduct of clinical trials during this COVID-19 crisis. However, international trials may benefit from expert guidance from a global panel of experts to supplement local advice and regulations, thereby enhancing the safety of participants and the integrity of the trial. Accordingly, the Heart Failure Association of the European Society of Cardiology on 21 and 22 March 2020 conducted web-based meetings with expert clinical trialists in Europe, North America, South America, Australia, and Asia. The main objectives of this Expert Position Paper are to highlight the challenges that this pandemic poses for the conduct of clinical trials in heart failure and to offer advice on how they might be overcome, with some practical examples. While this panel of experts are focused on heart failure clinical trials, these discussions and recommendations may apply to clinical trials in other therapeutic areas.