Single Case Research Designs Methods For Clinical And Applied Settings – A case-control study consists of two groups of people: one with a health problem (the case group) and this group is ‘matched’ to a control group without a health problem based on characteristics such as age, sex and occupation. In this type of study, we can look back at the patient’s history for exposure to risk factors common to the case group, but not to the control group. This is a case and control study that demonstrated an association between lung cancer and tobacco smoking. These studies estimate associations between exposure and health outcomes, although they cannot prove causation. Case-control studies may also be referred to as retrospective studies or reference case studies.
This graph suggests taking both the status (disease) and control (no disease) groups and looking at their histories to determine their exposure to potential contributing factors. Researchers determine the likelihood of these contributing factors to the disease.
Single Case Research Designs Methods For Clinical And Applied Settings
(For accessibility: A case study is likely to show that many, but not all, exposed people end up with a health problem, and some unexposed individuals may also develop a health problem)
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* Confusion occurs when factors in the study design invalidate the outcome. This is usually accidental. It is important to avoid confusion, which in some ways can occur in case-control studies. This explains why it is lower in the evidence hierarchy, and only superior to case studies.
Dahl, R.; and Hill, A.B. (1950). Lung smoking and cancer. Preliminary report. British Medical Journal, 2 (4682), 739-748. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/
Greenhalgh, Trisha. How to Read Article: Essentials of Evidence-Based Medicine, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib//detail.action?docID=1642418.
Hoffman, T.; Bennett S, and Del Mar C. (2017). Evidence-based practice across the health professions (3rd ed.): Elsevier.
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Pelham, B.W.A, and Blanton, H. (2013). Conducting Research in Psychology: Smoke Weighing / Brett W. Pelham, Hart Blanton (4th ed.): Wadsworth Cengage Learning.
Reese A-M, Austin M-B, Owen C, and Parker G (2009). Omega-3 deficiency associated with perinatal depression: a case study. Psychiatry Research, 166(2), 254-259. Retrieved from http://www.sciencedirect.com/science/article/pii/S0165178107004398
Richardson, K., Fox, C., Maidment, I., Steele, N., Locke, Y.K, Arthur, A.,…Savva, G.M (2018). Anticholinergic drugs and dementia risk: a case-control study. BMJ, 361, k1315. Retrieved from http://www.bmj.com/content/361/bmj.k1315.ab Abstract Scientific evidence in the field of psychiatry derives primarily from cohort (“primitive”) studies, which often yield cohort pooled results. Answering questions that apply to individuals is essential. Especially in the presence of large interindividual differences and temporal complexities, information at the interindividual level may be valuable for individual treatment decisions, individual assessments, and prognosis. A single-subject study design can be used to make conclusions about individuals. However, the individual study is not used very often in the field of psychiatry. We believe this is due to a lack of understanding of its value rather than a lack of utility or feasibility. In this paper, we aim to address some of the common misconceptions and beliefs about single subject studies by discussing some commonly heard “facts and myths”. We also discuss some situations in which a single subject study may be appropriate and the possibility of combining subject and single study designs in a single study. While we do not advocate single-subject studies at the expense of group-based studies, we hope to raise awareness of the value of single-subject research by informing readers of the many aspects of this design, resolving misunderstandings, and offering suggestions. Read more.
Scientific evidence in the field of psychiatry is primarily based on studies that predict what is true on average in a population or group. In many cases, these studies provide valuable information, but particularly when the goal is to improve patient care, we need to answer questions that apply to individual patients. For example, to know if an antidepressant medication is effective in a given patient, it is not sufficient to know that the drug causes a mean 0.31 SD reduction in depressive symptoms in the population (1). Also, knowing that depressive symptoms are associated with increased inflammatory markers at the group level (2) does not tell us whether depressive symptoms are increased when a given patient has elevated levels of inflammatory markers. It is increasingly recognized that mental disorders, their symptoms, and their response to treatment [eg, (3, 4)] have significant interindividual differences in causes, risk factors, and course over time.
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To illustrate the potential magnitude of this heterogeneity, Figure 1 shows the course of depressive symptoms assessed over 3 years in 267 individuals who were depressed at baseline [see (5) for study details]. The figure shows that there are significant interindividual differences in trajectories and that many individuals show large fluctuations in symptom levels over time. For very few people, the average path (left panel) applies, even to an extent. Therefore, one might wonder how much of these group-level results give us information about what is going on among the many individuals studied in that group.
Figure 1. Weekly assessment of severity of depressive symptoms over 3 years in 267 initially depressed individuals. Left: mean (95% CI) symptom severity. Right: individual tracks.
What the figure shows is not an unusual pattern, and several authors have noted the problem of relying on averages when there are no objective averages [eg, (6-12)]. Many of the phenomena we study in the field of psychiatry vary greatly between people, and many phenomena are not static but highly dynamic (eg mood regulation and stress physiology). In the presence of such large interindividual variability, information at the individual level is very valuable for making personalized treatment decisions or for identifying individual indications for changes in symptoms. Moreover, to understand the highly dynamic nature of some phenomena, we need many repeated assessments over time. A single-subject study is a useful study design for making inferences about individuals and revealing the highly dynamic nature of our variables of interest. However, this design is rarely used in the field of psychiatry. We believe that this may be due to a lack of understanding of its value, which may be due to the many persistent misconceptions regarding individual studies. In this paper, we aim to further recognize the value of single-subject studies in the field of psychiatry by discussing some of the key facts and myths of single-subject research.
Single-subject studies are characterized by focusing on single individuals. This differs from traditional group-based (“primitive”) study designs, which focus on group averages and compare them to other populations (eg randomized controlled trials, cohort studies, or case-control studies). In individual studies, each individual’s data is analyzed separately and the individuals are compared with themselves (13, 14). Because of the multiple evaluations collected within the individual, the individual can act as their own control over time. This allows us to examine whether changes in one variable are systematically associated with changes in another variable within an individual (single-subject observational design), or whether experimental manipulation is associated with consistent change within that individual (single-subject experimental design); See Figure 2).
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Figure 2. Schematic illustration of different types of individual studies and some examples of objectives for both types of individual subject studies.
More generalized conclusions can be drawn by repeating multiple single-subject studies on a given topic. In the presence of significant inter- and intra-individual variability, when each participant is analyzed at the intra-individual level, it only answers questions applicable to individual patients. If the same effect is found in a series of individual studies, this may be the basis for a generalizable conclusion. That is, the association may be true for the majority of people [i.e. true in general; (15)]. In the case of significant heterogeneity, the probability of finding such similarities in the processes underlying psychiatric disorders may not be great. In this case, single-subject studies can be used to relate findings at the individual level to specific individual characteristics or be used in clinical practice to inform the treatment process.
While the single-subject study may be rare these days, it was widely used in previous centuries and provided important information about human behavior, physiology, and pathology (see Box 1). The use of this design began to decline in the early 20th century, when people became interested in the evolution of genera or species (27). Around that time, scientists (and eugenicists) such as Pearson and Fisher introduced statistical methods that focused on group averages, which led to a paradigm shift toward combined results for the group. This shift towards statistics based on group averages is a logical step to take if one is interested in improving plant species or cultivars. For example, if a farmer wants to know what factors improve the growth of lettuce plants, he is not interested in the growth of individual lettuce plants, but in the average yield.
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