Professor Evans scholarly contributions include applied and methodological research. He has published 60 manuscripts in peer-reviewed journals since 2015 including twelve first author and seven senior author papers. Eighteen are published in Clinical Infectious Diseases (CID), a top infectious disease journal, while other publications appeared in top medical journals such as the New England Journal of Medicine (NEJM), JAMA, and the Annals of Internal Medicine. Thirteen publications are in statistics journals.
Leadership in Infectious Disease Collaborations: Professor Evans has extensive leadership in infectious disease study collaborations including: (1) a Phase II trial that implemented a Simon’s two-stage optimal design which concluded that oral etoposide was effective for treating relapsed or progressed AIDS-associated Kaposi’s sarcoma (Evans et.al., Journal of Clinical Oncology, 2002), (2) a randomized trial evaluating a therapy for the treatment of HIV-associated neuropathic pain, innovatively measured by an electronic diary using random prompts (Evans et.al., PLOS ONE, 2007), (3) a study of the agreement between methods of measuring LDL cholesterol in HIV (Evans et.al., HIV Clinical Trials, 2007), and (4) a study evaluating the prevalence and risk factors for peripheral neuropathy in HIV disease (Evans et.al., AIDS, 2011).
Strategy Clinical Trials: Patient management of patients with bacterial infections is not a single decision but a dynamic process, based on a sequence of decisions with therapeutic adjustments made over time. Adjustments are personalized, tailored to individual patients as new information becomes available. However strategies allowing for such adjustments are infrequently studied. Traditional antibiotic trials are often nonpragmatic, comparing drugs for definitive therapy when drug susceptibilities are known. Professor Evans and colleagues developed COMparing Personalized Antibiotic StrategieS (COMPASS), a trial design that compares strategies consistent with clinical practice, decision-rules that guide empiric and definitive therapy decisions. Sequential Multiple Assignment Randomized Trials (SMART) COMPASS allows evaluation when there are multiple definitive therapy options. SMART COMPASS is pragmatic, mirroring clinical antibiotic treatment decision-making and addressing the most relevant issue for treating patients: identification of the patient-management strategy that optimizes ultimate patient outcomes. SMART COMPASS is valuable in the setting of antibiotic resistance when therapeutic adjustments may be necessary due to resistance.
Diagnostic Studies: Typical antimicrobial susceptibility testing (AST) takes 48-72 hours, critically delaying appropriate therapy. Rapid diagnostics are needed. Professor Evans and colleagues are evaluating the use of rapid molecular diagnostics to inform clinical decision-making with emphasis on the World Health Organization’s (WHO) three Priority 1 (critical) pathogens: carbapenem-resistant Enterobacteriaceae (Evans et.al., CID, 2015), Acinetobacter baumannii (Evans et.al., Journal of Clinical Microbiology, 2016), and Pseudomonas aeruginosa (Evans et.al., CID, 2016).
Professor Evans was the senior author on the Antibacterial Resistance Leadership Group (ALRG) proposal master protocol for evaluating multiple infection diagnostics (MASTERMIND) (Patel et.al., CID, 2017) for advancement of infectious diseases diagnostics. MASTERMIND uses a single subject’s sample(s) to evaluate multiple diagnostic tests simultaneously, providing efficiencies of specimen collection and characterization. MASTERMIND offers central trial organization, standardization of methods and definitions, and common comparators.
Benefit:risk Assessment: Randomized trials are the gold standard for evaluating intervention effects. Diagnostic studies with appropriate reference standards are the quintessential model for evaluating classification accuracy. However studies often fail to provide the necessary evidence to inform medical decision-making. The important implications of these deficiencies are largely absent from discourse in medical research communities. Motivated by pragmatism, I am developing benefit:risk methodologies to inform patient management. The methods are impacting studies in infectious diseases and beyond.
Typical analyses of clinical trials involve intervention comparisons for each efficacy and safety outcome. Outcome-specific effects are estimated and potentially combined in benefit:risk analyses believing that this informs the totality of effects on patients. However such approaches do not incorporate associations between outcomes, are confounded by competing risks, and since efficacy and safety analyses are often conducted on different analysis populations, the population to which these analyses apply, is unclear.
Professor Evans proposed the desirability of outcome ranking (DOOR) (Evans, et.al., CID, 2015) and partial credit methodologies described in “Using Outcomes to Analyze Patients Rather than Patients to Analyze Outcomes: A Step toward Pragmatism in Benefit:risk Evaluation” (Evans et.al., Statistics in Biopharmaceutical Research, 2016) as a remedy to these issues. The methods can incorporate patient values and estimate personalized effects. The methods were used to compare ceftazidime-avibactam vs. colistin for the treatment of infections due to CRE (van Duin, et.al., CID, 2017), a pathogen characterized as having an urgent hazard level by the CDC and as a Priority 1 (Critical) pathogen by the WHO.
Standard evaluation of diagnostics consists of estimating sensitivity, specificity, and positive/negative predictive values and likelihood ratios. However these measures have limited utility for guiding clinical decision-making. Diagnostic utility depends on prevalence and the relative importance of potential errors (false positive vs. false negative). Professor Evans and colleagues proposed benefit-risk evaluation of diagnostics: a framework (BED-FRAME) (Evans, et.al., CID, 2016; Pennello et.al., Stat in Biopharm Res, 2016), for pragmatic diagnostic evaluation. They defined weighted accuracy and diagnostic yield measures to communicate the expected clinical impact of diagnostic application and the tradeoffs of diagnostic alternatives. The methods are being used to design a study evaluating the utility of a host response-based diagnostic test categorizing acute respiratory tract illness into bacterial, viral, or neither etiology in a regulatory setting.
Methodologies for Interim Monitoring of Clinical Trials: Dr. Evans and colleagues introduced use of prediction for data monitoring of clinical trials and as a valuable tool for DSMBs (Evans et.al., DIJ, 2007; Li et.al., Stat in Biopharm Res, 2009). The methods are the foundation for the commercial software EAST PREDICT.
Goodness of Fit Tests: Many studies utilize binary outcomes, e.g., success vs. failure. Often clusters of correlated observations arise through repeated measurements or other mechanisms (e.g., measurements on people within the same family). Logistic regression models utilizing generalized estimating equations and mixed/random-effects evaluate outcomes accounting for the correlation. I developed goodness-of-fit tests in logistic mixed/random-effects models and logistics GEE models (Evans, et.al. Comm in Stat, 2004a; Evans, et.al. Comm in Stat, 2004b; Evans et.al. Stat in Med, 2005). The methods are now widely applied.
Educational and Guidance Contributions: Professor Evans has published educational papers for medical researchers on important statistical concepts. For example, comparisons of protocol-defined to published endpoints revealed that many trials have changed endpoints. Dr. Evans developed guiding principles for modification to trial endpoints after trial initiation (Evans SR. PLOS Clinical Trials, 2007). He and colleagues have recently published papers: (1) describing issues in the selection of the analysis population in anti-infective clinical trials, (2) evaluating whether non-randomized controls can be used to evaluate anti-infective drugs in the resistant-pathogen setting (Evans et.al., Stat Comm in Inf Dis, 2017), (3) discussing adaptive clinical trial designs in healthcare epidemiology research (Huskins et.al., CID. 2017), (4) discussing methods and issues in studies of CRE (Evans, et.al., Virulence, 2016), and (5) describing alternatives for event-time data analyses that are more robust than standard methods (Uno et.al., Annals of Internal Medicine, 2015).
Professor Evans has authored three books including a textbook on clinical trials, Fundamentals for New Clinical Trialists (2016). Two statistical methodology books were co-authored with colleagues based on a series of papers from our research group: (1) Sample Size Determination in Clinical Trials with Multiple Endpoints (2015), and (2) Group-Sequential Clinical Trials with Multiple Co-Objectives (2016).
Interviews of Professor and his work can be found here:
- How can Novel Statistical Methods Tackle Antibiotic Resistance? Interview with Scott Evans. Cytel Blog. http://www.cytel.com/blog/novel-statistics-antibiotic-resistance-scott-evans. 2017.
- Q&A with Scott Evans, CAHNCE Executive Editor. American Statistical Association. http://www.amstat.org/ASA/Publications/Q-and-As/Scott-Evans.aspx.
- Outsmarting Superbugs. Plymouth Magazine. https://www.plymouth.edu/magazine/alumni-green/outsmarting-superbugs/. 2016.
- Superbugs: An Epidemic Begins. Harvard Magazine. http://harvardmagazine.com/2014/05/superbug. 2014.
Sites of Note
Statistical Communications in Infectious Diseases
Fundamental Concepts for New Clinical Trialists
Sample Size Determination in Clinical Trials with Multiple Endpoints
Group-Sequential Clinical Trials with Multiple Co-Objectives