Resources for Clinical Research in the JID
- 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.
- 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.
- This article summarizes the scientific concepts underlying pragmatic clinical trials as a research technique that is worthy of wider use in dermatology.
- 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).
- 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.
- 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.
- 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 (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.