Resources for Clinical Research in the JID
Noninferiority Randomized Controlled Trials
From 2005 to 2015, the publication of noninferiority trials increased by six-fold. Noninferiority trials assess whether a new treatment’s efficacy is comparable with that of the standard of care and have several appeals. Noninferiority trials can evaluate for both noninferiority and superiority of a new treatment. In addition, multiple treatment modalities exist, and new treatments may be advantageous for reasons beyond efficacy. Common elements of trial design such as the research question, outcomes, statistical analysis, and interpretation of results differ meaningfully between noninferiority trials and superiority trials.Research Techniques Made Simple: An Introduction to Qualitative Research
Qualitative research has gained increasing prominence in health-related research and is experiencing greater use in dermatology. A major strength of and reason to perform qualitative research is that it allows one to gain an understanding of the insider (e.g., patient, medical provider, other players in the healthcare system) perspectives on health and insights about their behaviors, motivations, and expectations. This is particularly important in the field of dermatology where most diseases are, fortunately, not directly fatal but have major effects on affected individuals’ lives in ways that are often not readily quantifiable.Research Techniques Made Simple: Developing and Validating QOL Outcome Measures for Skin Diseases
Skin conditions can significantly impact QOL. Dermatology QOL instruments may measure general skin-specific, disease-specific, or condition-specific QOL. Key components in the development of QOL instruments include (i) instrument and conceptual framework development, (ii) items and conceptual framework refinement, (iii) psychometric property testing, and (iv) clinical meaning and interpretation. First, a theoretical framework based on existing literature and subject experts (i.e., patients living with these conditions) is developed.Research Techniques Made Simple: Latent Class Analysis
Latent class analysis (LCA) is a statistical technique that allows for identification, in a population characterized by a set of predefined features, of hidden clusters or classes, that is, subgroups that have a given probability of occurrence and are characterized by a specific and predictable combination of the analyzed features. Compared with other methods of so called data segmentation, such as hierarchical clustering, LCA derives clusters using a formal probabilistic approach and can be used in conjunction with multivariate methods to estimate parameters.Research Techniques Made Simple: Randomized Controlled Trials for Topical Drugs in Dermatology: When and How Should We Use a Within-Person Design?
Topical drugs are often used as first-line treatment for dermatological conditions. Depending on the disease and the drug, three main designs can be used for randomized controlled trials assessing topical drugs: the classical individual parallel design, the cluster randomized design, and designs allowing within-individual comparisons, including the cross-over design (in which patients are randomized to a sequence of interventions) and the within-person design (also called the split-body design).Research Techniques Made Simple:Teledermatology in Clinical Trials
Telemedicine is well established as a means of providing high-quality healthcare at a distance, particularly to patients in underserved populations. Technologies in teledermatology can be used to complement traditional methodologies of clinical trials, expanding accessibility of trials to people typically unable to participate in research. Tools of communication technology may enhance many aspects of clinical trials in dermatology, from recruitment and retention of participants to collection of real-time data.Research Techniques Made Simple: Interpreting Measures of Association in Clinical Research
To bring evidence-based improvements in medicine and health care delivery to clinical practice, health care providers must know how to interpret clinical research findings and critically evaluate the strength of evidence. This requires an understanding of differences in clinical study designs and the various statistical methods used to identify associations. We aim to provide a foundation for understanding the common measures of association used in epidemiologic studies to quantify relationships between exposures and outcomes, including relative risks, odds ratios, and hazard ratios.Research Techniques Made Simple: Network Meta-Analysis
When making treatment decisions, it is often necessary to consider the relative efficacy and safety of multiple potential interventions. Unlike traditional pairwise meta-analysis, which allows for a comparison between two interventions by pooling head-to-head data, network meta-analysis (NMA) allows for the simultaneous comparison of more than two interventions and for comparisons to be made between interventions that have not been directly compared in a randomized controlled trial. Given these advantages, NMAs are being published in the medical literature with increasing frequency.Research Techniques Made Simple: Sample Size Estimation and Power Calculation
Sample size and power calculations help determine if a study is feasible based on a priori assumptions about the study results and available resources. Trade-offs must be made between the probability of observing the true effect and the probability of type I errors (α, false positive) and type II errors (β, false negative). Calculations require specification of the null hypothesis, the alternative hypothesis, type of outcome measure and statistical test, α level, β, effect size, and variability (if applicable).Research Techniques Made Simple: Web-Based Survey Research in Dermatology: Conduct and Applications
Web-based surveys, or e-surveys, are surveys designed and delivered using the internet. The use of these survey tools is becoming increasingly common in medical research. Their advantages are appealing to surveyors because they allow for rapid development and administration of surveys, fast data collection and analysis, low cost, and fewer errors due to manual data entry than telephone or mailed questionnaires. Internet surveys may be used in clinical and academic research settings with improved speed and efficacy of data collection compared with paper or verbal survey modalities.Research Techniques Made Simple: Pharmacoepidemiology Research Methods in Dermatology
Clinical trials have several important limitations for evaluating the safety of new medications, leading to many adverse events not being identified until the postmarketing period. Descriptive studies, including case reports, case series, cross-sectional, and ecologic studies, help identify potential safety signals and generate hypotheses. Further research using analytic study methods, including case-control studies and cohort studies, are necessary to determine if an association truly exists and to better understand the potential for causation.Research Techniques Made Simple: Choosing Appropriate Statistical Methods for Clinical Research
The statistical significance of results is an important component to drawing appropriate conclusions in a study. Choosing the correct statistical test to analyze results is essential in interpreting the validity of the study and centers on defining the study variables and purpose of the analysis. The complexity of statistical modeling makes this a daunting task, so we propose a basic algorithmic approach as an initial step in determining what statistical method will be appropriate for a particular clinical study.Research Techniques Made Simple: An Introduction to Use and Analysis of Big Data in Dermatology
Big data is a term used for any collection of datasets whose size and complexity exceeds the capabilities of traditional data processing applications. Big data repositories, including those for molecular, clinical, and epidemiology data, offer unprecedented research opportunities to help guide scientific advancement. Advantages of big data can include ease and low cost of collection, ability to approach prospectively and retrospectively, utility for hypothesis generation in addition to hypothesis testing, and the promise of precision medicine.Research Techniques Made Simple: Cost-Effectiveness Analysis
Cost-effectiveness analysis (CEA) is a research method used to determine the clinical benefit-to-cost ratio of a given intervention. CEA offers a standardized means of comparing cost-effectiveness among interventions. Changes in quality-adjusted life-years, disability-adjusted life-years, or survival and mortality are some of the common clinical benefit measures incorporated into CEA. Because accounting for all associated costs and benefits of an intervention is complex and potentially introduces uncertainty into the analysis, sensitivity analyses can be performed to test the analytic model under varying conditions.Research Techniques Made Simple: Workflow for Searching Databases to Reduce Evidence Selection Bias in Systematic Reviews
Clinical trials and basic science studies without statistically significant results are less likely to be published than studies with statistically significant results. Systematic reviews and meta-analyses that omit unpublished data are at high risk of distorted conclusions. Here, we describe methods to search beyond bibliographical databases to reduce evidence selection bias in systematic reviews. Unpublished studies may be identified by searching conference proceedings. Moreover, clinical trial registries—databases of planned and ongoing trials—and regulatory agency websites such as the European Medicine Agency (EMA) and the United States Food and Drug Administration (FDA) may provide summaries of efficacy and safety data.Research Techniques Made Simple: Assessing Risk of Bias in Systematic Reviews
Systematic reviews are increasingly utilized in the medical literature to summarize available evidence on a research question. Like other studies, systematic reviews are at risk for bias from a number of sources. A systematic review should be based on a formal protocol developed and made publicly available before the conduct of the review; deviations from a protocol with selective presentation of data can result in reporting bias. Evidence selection bias occurs when a systematic review does not identify all available data on a topic.Psoriasis and Cardiovascular Risk: Strength in Numbers Part 3
Over the last decade a large body of epidemiological, translational, and animal model research has suggested that psoriasis may be a risk factor for cardiovascular and metabolic disease. Outcome based studies often suggest that patients with more severe psoriasis have an increased risk of major cardiovascular events independent of traditional risk factors that are captured in electronic health data. The study by Parisi and colleagues finds that incident severe psoriasis is associated with a non-statistically significant increased risk of major cardiovascular events, HR 1.28 (95% CI 0.96–1.69) in their primary model and a statistically significant increased risk, HR 1.46 (95% CI 1.11, 1.92), in a sensitivity analysis that excludes patients with inflammatory arthritis.Databases for Clinical Research
The growing availability of digital health data offers many opportunities for clinical research. Studies drawing on electronic data are often efficient, although the usefulness and validity of the data depend on the research question. We briefly review types of epidemiologic study designs commonly used with patient databases and then describe the types of electronic databases available, outline considerations for the ad hoc design of new databases, and discuss potential limitations to consider when performing database research.Drug Survival Studies in Dermatology:Principles, Purposes, and Pitfalls
With rising health-care costs and a growth of pharmaceutical options, health professionals are continuously looking for better and more comprehensive methods to evaluate treatments. In recent years, the term “drug survival” (DS) has made its way through the field of dermatology. This methodological approach, which is based on regular Kaplan–Meier survival analysis, has its roots in rheumatology, where it was first described in 1991 (Wijnands et al., 1991). The method of DS has only relatively recently emerged in dermatology, with most publications limited to biological treatments for psoriasis.What Is a Pragmatic Clinical Trial?
This article summarizes the scientific concepts underlying pragmatic clinical trials as a research technique that is worthy of wider use in dermatology.Evaluating the Strength of Clinical Recommendations in the Medical Literature: GRADE, SORT, and AGREE
The medical community relies on scientific evidence to guide clinical practice. Evidence from systematic reviews, randomized controlled clinical trials (RCTs), case–control or cohort studies, observational studies, and expert opinions are used to make disease-specific practice recommendations. More than 100 grading systems are used to rate the strength of these recommendations (West et al., 2002). A centralized and transparent method for evaluating and comparing these studies with the goal of translating evidence-based medicine to clinical practice guidelines is the cornerstone of two such validation scales: the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Strength of Recommendation Taxonomy (SORT).Multivariable Analysis
In all observational research, one will sooner or later be confronted with the question of whether a certain exposure is related to an outcome. For example, is the risk of cutaneous melanoma affected by the use of nonsteroidal anti-inflammatory drugs (NSAIDs) or is psoriasis an independent predictor for the occurrence of cardiovascular diseases? Questions like these can be answered using multivariable regression analysis. This technique can be used in observational research to adjust for confounders, to assess the effect size of risk factors, or to develop prediction models.A Critical Evaluation of Clinical Research Study Designs
Prior to starting any clinical research, an investigator must determine the appropriate study design to answer the question at hand. Selecting the correct study type also depends on ethical considerations, disease of interest, and the resources available. A well-designed study will clearly identify an exposure and an outcome in an objective, quantifiable manner to answer a defined hypothesis. After thorough data analysis and discussion of the results, the study will ideally prompt further research on the subject.“Validation” of Outcome Measures in Dermatology
Outcome measures are powerful tools in a clinician’s armamentarium. These instruments capture clinical information and may supplement clinical judgment in order to optimize management approach, medical treatment, and referrals to other appropriate health-care providers. They may shed light on psychosocial issues while providing insight into gaps in understanding not previously considered by the clinician or the patient. These tools highlight variability between diseases when using the same scoring system and may influence clinical guideline recommendations.Comparative Effectiveness Research
Comparative effectiveness research (CER) aids clinicians faced with medical decision making by identifying the best strategies among a variety of available preventive, diagnostic, and treatment options. Differing from early-phase clinical trials—in which an intervention is compared with a placebo and assessed for efficacy—the goal of CER is to discriminate among clinical interventions on the basis of clinical effectiveness, cost-effectiveness, adverse effects, or other distinguishing factors.
