Methodology Series Module 4: Clinical Trials

In a clinical trial, study participants are (usually) divided into two groups. One group is then given the intervention and the other group is not given the intervention (or may be given some existing standard of care). We compare the outcomes in these groups and assess the role of intervention. Some of the trial designs are (1) parallel study design, (2) cross-over design, (3) factorial design, and (4) withdrawal group design. The trials can also be classified according to the stage of the trial (Phase I, II, III, and IV) or the nature of the trial (efficacy vs. effectiveness trials, superiority vs. equivalence trials). Randomization is one of the procedures by which we allocate different interventions to the groups. It ensures that all the included participants have a specified probability of being allocated to either of the groups in the intervention study. If participants and the investigator know about the allocation of the intervention, then it is called an “open trial.” However, many of the trials are not open – they are blinded. Blinding is useful to minimize bias in clinical trials. The researcher should familiarize themselves with the CONSORT statement and the appropriate Clinical Trials Registry of India.     

Methodology Series Module 2: Case-control Studies

Case-Control study design is a type of observational study. In this design, participants are selected for the study based on their outcome status. Thus, some participants have the outcome of interest (referred to as cases), whereas others do not have the outcome of interest (referred to as controls). The investigator then assesses the exposure in both these groups. The investigator should define the cases as specifically as possible. Sometimes, definition of a disease may be based on multiple criteria; thus, all these points should be explicitly stated in case definition. An important aspect of selecting a control is that they should be from the same ‘study base’ as that of the cases. We can select controls from a variety of groups. Some of them are: General population; relatives or friends; and hospital patients. Matching is often used in case-control control studies to ensure that the cases and controls are similar in certain characteristics, and it is a useful technique to increase the efficiency of the study. Case-Control studies can usually be conducted relatively faster and are inexpensive – particularly when compared with cohort studies (prospective). It is useful to study rare outcomes and outcomes with long latent periods. This design is not very useful to study rare exposures. Furthermore, they may also be prone to certain biases – selection bias and recall bias.     

Methodology Series Module 3: Cross-sectional Studies

Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case–control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.     

Methodology Series Module 1: Cohort Studies

Cohort design is a type of nonexperimental or observational study design. In a cohort study, the participants do not have the outcome of interest to begin with. They are selected based on the exposure status of the individual. They are then followed over time to evaluate for the occurrence of the outcome of interest. Some examples of cohort studies are (1) Framingham Cohort study, (2) Swiss HIV Cohort study, and (3) The Danish Cohort study of psoriasis and depression. These studies may be prospective, retrospective, or a combination of both of these types. Since at the time of entry into the cohort study, the individuals do not have outcome, the temporality between exposure and outcome is well defined in a cohort design. If the exposure is rare, then a cohort design is an efficient method to study the relation between exposure and outcomes. A retrospective cohort study can be completed fast and is relatively inexpensive compared with a prospective cohort study. Follow-up of the study participants is very important in a cohort study, and losses are an important source of bias in these types of studies. These studies are used to estimate the cumulative incidence and incidence rate. One of the main strengths of a cohort study is the longitudinal nature of the data. Some of the variables in the data will be time-varying and some may be time independent. Thus, advanced modeling techniques (such as fixed and random effects models) are useful in analysis of these studies.     

Understanding and Evaluating Systematic Reviews and Meta-analyses

A systematic review is a summary of existing evidence that answers a specific clinical question, contains a thorough, unbiased search of the relevant literature, explicit criteria for assessing studies and structured presentation of the results. A systematic review that incorporates quantitative pooling of similar studies to produce an overall summary of treatment effects is a meta-analysis. A systematic review should have clear, focused clinical objectives containing four elements expressed through the acronym PICO (Patient, group of patients, or problem, an Intervention, a Comparison intervention and specific Outcomes). Explicit and thorough search of the literature is a pre-requisite of any good systematic review. Reviews should have pre-defined explicit criteria for what studies would be included and the analysis should include only those studies that fit the inclusion criteria. The quality (risk of bias) of the primary studies should be critically appraised. Particularly the role of publication and language bias should be acknowledged and addressed by the review, whenever possible. Structured reporting of the results with quantitative pooling of the data must be attempted, whenever appropriate. The review should include interpretation of the data, including implications for clinical practice and further research. Overall, the current quality of reporting of systematic reviews remains highly variable.     

Biostatistics Series Module 6: Correlation and Linear Regression

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson''s correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman''s rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r² denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.     

Biostatistics Series Module 2: Overview of Hypothesis Testing

Hypothesis testing (or statistical inference) is one of the major applications of biostatistics. Much of medical research begins with a research question that can be framed as a hypothesis. Inferential statistics begins with a null hypothesis that reflects the conservative position of no change or no difference in comparison to baseline or between groups. Usually, the researcher has reason to believe that there is some effect or some difference which is the alternative hypothesis. The researcher therefore proceeds to study samples and measure outcomes in the hope of generating evidence strong enough for the statistician to be able to reject the null hypothesis. The concept of the P value is almost universally used in hypothesis testing. It denotes the probability of obtaining by chance a result at least as extreme as that observed, even when the null hypothesis is true and no real difference exists. Usually, if P is < 0.05 the null hypothesis is rejected and sample results are deemed statistically significant. With the increasing availability of computers and access to specialized statistical software, the drudgery involved in statistical calculations is now a thing of the past, once the learning curve of the software has been traversed. The life sciences researcher is therefore free to devote oneself to optimally designing the study, carefully selecting the hypothesis tests to be applied, and taking care in conducting the study well. Unfortunately, selecting the right test seems difficult initially. Thinking of the research hypothesis as addressing one of five generic research questions helps in selection of the right hypothesis test. In addition, it is important to be clear about the nature of the variables (e.g., numerical vs. categorical; parametric vs. nonparametric) and the number of groups or data sets being compared (e.g., two or more than two) at a time. The same research question may be explored by more than one type of hypothesis test. While this may be of utility in highlighting different aspects of the problem, merely reapplying different tests to the same issue in the hope of finding a P < 0.05 is a wrong use of statistics. Finally, it is becoming the norm that an estimate of the size of any effect, expressed with its 95% confidence interval, is required for meaningful interpretation of results. A large study is likely to have a small (and therefore “statistically significant”) P value, but a “real” estimate of the effect would be provided by the 95% confidence interval. If the intervals overlap between two interventions, then the difference between them is not so clear-cut even if P < 0.05. The two approaches are now considered complementary to one another.     

Risk of Herpes Zoster and Family History: A Meta-analysis of Case-control Studies

Background:Herpes zoster (HZ) results from the reactivation of latent varicella zoster virus (VZV) residing in dorsal root and cranial nerve ganglia. Advanced age and dysfunctional cell-mediated immune responses are well-established risk factors for VZV reactivation. There have been recent interests in whether there is an increased risk of the disease associated with a positive family history.Results:Five studies, yielding a total of 4169 subjects, were identified for meta-analysis. Cases with HZ were 3.03 (95% confidence interval [CI]: 1.86–4.94, P < 0.001) and 3.27 (95% CI: 1.75–6.10, P < 0.001) times more likely to report the first-degree relatives and total relatives with a history of HZ, respectively. A significant positive dose-response relationship between the risk of HZ infection and the number of relatives with a history of HZ was also demonstrated (P < 0.001).Conclusions:This meta-analysis demonstrated that family history is a significant risk factor for HZ infection. This risk has a dose-response relationship with the number of relatives with a history of HZ.     

Biostatistics Series Module 3: Comparing Groups: Numerical Variables

Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov–Smirnov test. The widely used Student''s t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey''s honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann–Whitney U-test as the nonparametric counterpart of the unpaired Student''s t-test, Wilcoxon signed-rank test as the counterpart of the paired Student''s t-test, Kruskal–Wallis test as the nonparametric equivalent of ANOVA and the Friedman''s test as the counterpart of repeated measures ANOVA.     

Risk of Parkinson's Disease Among Patients with Psoriasis: A Systematic Review and Meta-analysis

Background:Patients with psoriasis might be at a higher risk of developing Parkinson''s disease (PD) as a result of the detrimental effect of chronic inflammation on the neuronal tissue. This meta-analysis aimed to investigate this risk by comprehensively reviewing all available data.Methods:We conducted a systematic review and meta-analysis of cohort and case–control studies that reported relative risk, hazard ratio, odds ratio, or standardized incidence ratio comparing the risk of PD in patients with psoriasis versus subjects without psoriasis. Pooled risk ratio and 95% confidence interval (CI) were calculated using random-effect, generic inverse variance methods of DerSimonian and Laird.Results:Three retrospective studies and one case–control study met our eligibility criteria and were included in this meta-analysis. The pooled risk ratio of PD in patients with psoriasis versus participants without psoriasis was 1.38 (95% CI, 1.15–1.66). The statistical heterogeneity was low with an I² of 35%.Conclusions:Our meta-analysis demonstrated a statistically significant increased risk of PD among patients with psoriasis.     

WHY IS THE CENTER OF EVIDENCE-BASED DERMATOLOGY RELEVANT TO INDIAN DERMATOLOGY?

Evidence-based dermatology is the application of high-quality evidence to the care of individual patients with skin diseases. The Centre of Evidence-Based Dermatology in the UK promotes activities in this field through its three interlinking cogs, composed of the international Cochrane Skin Group, the UK Dermatology Clinical Trials Network (UKDCTN), and the UK national electronic library for skin disorders. The Cochrane Skin Group summarises what is already known about health care interventions by supporting systematic reviews of relevant randomized controlled trials (RCTs). The UKDCTN then addresses the key research gaps identified in systematic reviews by coordinating and carrying out well-designed RCTs. The Skin Disorders specialist library then plays a key role in disseminating new knowledge from systematic reviews and RCTs to a community of clinical users. The electronic resources at the Centre of Evidence-Based Dermatology are all freely available to Indian Dermatologists who can use the resources in a way that could benefit their patients. Such new knoweldge only has value if it is shared and used.     

Subungual Squamous Cell Carcinoma: A Case Series

Subungual tumors are rare in general. Of all tumors, subungual squamous cell carcinoma (SSCC) is the most frequent one. Protean clinical presentations and the lack of awareness of the disease are responsible for an incorrect or delayed diagnosis and subsequent delayed treatment. We have reported here four patients with SSCC who were previously wrongly diagnosed with a benign process and treated unsuccessfully for years. We would like to highlight the need of a biopsy in chronic or recurrent nail lesions that fail to respond to a previous conservative treatment in order to rule out SSCC.     

Applicability of Proposed Diagnostic Criteria of Pityriasis Rosea: Results of a Prospective Case-Control Study in India

Background:

The diagnosis of pityriasis rosea (PR) is generally clinical. Previous studies usually recruited relatively small numbers of patients and control subjects, leading to low power of study results. Systematic reviews and meta-analyses cannot be readily performed, as the inclusion and exclusion criteria of these studies were not uniform. We have previously validated a set of diagnostic criteria (DC) in Chinese patients with PR.

Aim:

Our aim is to evaluate the validity and applicability of the DC of PR in Indian patients with PR.

Study Design:

Prospective unblinded pair-matched case-control study.

Materials and Methods:

The setting is a dermatology clinic in India served by one board-certified dermatologist. We recruited all 88 patients seen by us during five years diagnosed to have PR to join our study. For each study subject, we recruited the next patient who consulted us with differential diagnoses of PR as control subjects. We applied the DC of PR on all study and control subjects.

Result:

The sensitivity, specificity, positive predictive value, and negative predictive value of the DC were all 100%. Two-tailed Fisher''s exact probability test result was 0.036. Φ was 1.00.

Conclusion:

The set of DC can be validly applied to Indian patients with PR.     

Dyschromias: A Series of Five Interesting Cases from India

Dyschromatosis is a pigmentary genodermatosis which presents with hyper and hypopigmented skin lesions giving a mottled appearance. It is a rare entity in India reported mainly in the East Asian population. Classically, two forms have been described; dyschromatosis universalis hereditaria (DUH) and dyschromatosis symmetrica hereditaria. Here we report four cases of DUH and one case of dyschromatosis symmetrica hereditaria from India.     

Genital/Perigenital Inflammatory Linear Verrucous Epidermal Nevus: A Case Series

Background:

Inflammatory linear verrucous epidermal nevus (ILVEN) is a distinct variety of keratinocytic epidermal naevus. In contrast to non-inflammatory epidermal naevi, ILVEN are far less common, usually erythematous and intractably pruritic. ILVEN usually appears at birth or early childhood and has a linear distribution following the Blaschko lines. Genital/perigenital involvement is relatively rare.

Objectives:

To describe the clinical features of 9 children with ILVEN localized to the genital and perigenital areas.

Method:

A retrospective study of 9 children with ILVEN presenting to a tertiary care Dermatology Clinic between 2007 and 2014 was undertaken. The clinical and histopathological features were reviewed.

Results:

Nine children (6 females, 3 males) were included in the study based on their characteristic clinicopathological features. The lesions were associated with severe itching in all cases. The mean age at presentation was 4 years (range 1-11 years). Onset of lesions was before 6 months of age in 8 patients. Left sided involvement was twice as common as the right sided one. Male patients had penoscrotal and groin involvement while all the female children had vulvar lesions. None of the children had any extracutaneous abnormalities. The children were treated with topical agents with variable relief or symptoms.

Conclusions:

The possibility of ILVEN should be considered in every linear genital lesion in children. We have presented the largest series of perigenital ILVEN reported in English literature.     

OXIDATIVE STRESS IN EXPERIMENTAL VITILIGO C57BL/6 MICE

Aim:

To evaluate whether oxidative stress is implicated in melanocyte damage in vitiligo.

Background:

Vitiligo is a complex disorder characterized by gradually enlarging areas of depigmentation. A new unifying hypothesis for the etiology of this pigment disorder is proposed, in which we postulate that the final destruction of melanocytes in vitiligo results from a cascade of reactions initiated by a disregulation of melanogenesis, as the result of a breakdown in free radical defense.

Methods:

We evaluated 18 vitiligo mice and 12 controls that were age matched. Parameters of oxidative stress such as catalase (CAT), superoxide dismutase (SOD), and plasma malondialdehyde (MDA) were measured by spectrophotometry.

Results:

MDA levels in vitiligo mice were significantly higher than in controls (P < 0.001). CAT, SOD, and glutathione peroxidase (GPx) activities in mice were significantly lower than controls (P < 0.05 and P < 0.001, respectively).

Conclusion:

Our results confirmed that oxidative stress plays an important role in the pathogenesis of vitiligo. Melanocyte damage in vitiligo might be linked to generalized oxidative stress. This study is the first report on antioxidant parameters in experimental vitiligo mice.     

Strengths and Limitations of Evidence-Based Dermatology

The need for understanding and reflecting on evidence-based dermatology (EBD) has never been greater given the exponential growth of new external evidence to inform clinical practice. Like any other branch of medicine, dermatologists need to acquire new skills in constructing answerable questions, efficiently searching electronic bibliographic databases, and critically appraising different types of studies. Secondary summaries of evidence in the form of systematic reviews (SR), that is, reviews that are conducted in a systematic, unbiased and explicit manner, reside at the top of the evidence hierarchy, because they are less prone to bias than traditional expert reviews. In addition to providing summaries of the best external evidence, systematic reviews and randomized controlled trials (RCTs) are also powerful ways of identifying research gaps and ultimately setting the agenda of future clinical research in dermatology. But like any paradigm, EBD can have its limitations. Wrong application, misuse and overuse of EBD can have serious consequences. For example, mindless pooling together of data from dissimilar studies in a meta-analysis may render it a form of reductionism that does not make any sense. Similarly, even highly protocolised study designs such as SRs and RCTs are still susceptible to some degree of dishonesty and bias. Over-reliance on randomized controlled trials (RCT) may be inappropriate, as RCTs are not a good source for picking up rare but important adverse effects such as lupus syndrome with minocycline. A common criticism leveled against SRs is that these frequently conclude that there is lack of sufficient evidence to inform current clinical practice, but arguably, such a perception is grounded more on the interpretation of the SRs than anything else. The apparent absence of evidence should not paralyze the dermatologist to adopt a state of therapeutic nihilism. Poor primary data and an SR based on evidence that is not up-to-date are also limitations that can only improve with better primary studies and updated reviews such as those done by the Cochrane Collaboration. Most dermatologists are interested in integrating the best external evidence with the care of individual patients and have been practicing good EBD without realizing it.     

A Clinicopathological Analysis of Primary Cutaneous Lymphomas: A 6-year Observational Study at a Tertiary Care Center of South India

Background:Little data are available concerning clinical and pathological patterns of cutaneous lymphomas in India.Aim:To analyze the clinical and histopathological characteristics of cutaneous lymphomas in Indian patientsResults:Among 35 cases, 33 (94.3%) were T-cell, and 2 (5.7%) were B-cell lymphomas. The mean age was 52.66, and the male to female ratio was 2.5:1. The most common types of T-cell lymphomas included mycosis fungoides (MF) (57.1%) followed by adult T-cell lymphoma/leukemia (ATL) (17.1%). Primary cutaneous peripheral T-cell lymphoma not otherwise specified was diagnosed in 17.1% and anaplastic large cell lymphoma in 2.9%. The morphological types of MF included polymorphic, poikilodermatous, folliculotropic, hypopigmented, hyperpigmented, mixed, and purpuric. Skin manifestations of ATL included ulcerated plaques and erythroderma. Epidermotropism was very marked in ATL (83.3%) than in MF (70%). Larger Pautrier''s microabscess was noted in ATL compared to smaller ones in MF. Markedly dense, diffuse infiltrate of atypical cells was noted in ATL in contrast to mild to moderate nodular or perivascular infiltrate in MF. ATL had an extremely poor prognosis.Limitations:Identification of DNA integration of HTLV-1 by Southern blot could not be analyzed, and the number of cases studied is limited.Conclusions:The study showed unique patterns of subtypes of cutaneous lymphomas in our country. Variations in the clinical pattern and histopathological analysis will help to differentiate T-cell lymphoma types which have prognostic implications.     

Chemical leucoderma: Indian scenario, prognosis, and treatment

Chemical leucoderma is an industrial disorder in developed countries and the common causative chemicals are phenols and catechols. Due to stringent controls and preventive measures the incidence has come down. In the recent past various chemicals in consumer products have also been documented to produce depigmentation. In India due to lax quality control measures chemical leucoderma due to consumer items is not uncommon.The various consumer items documented to cause contact depigmentation are sticker bindis, rain shoes, plastic chappals, hair dye/ black henna(kali mehndi), alta, wallets and even mobile plastic covers.     

Cutaneous drug hypersensitivity: immunological and genetic perspective

Drug hypersensitivity is an unpredictable, immunologically mediated adverse reaction, clustered in a genetically predisposed individual. The role of "hapten concept" in immune sensitization has recently been contested by the "pharmacological interaction" hypothesis. After completion of the "human genome project" and with the availability of high-resolution genotyping, genetic susceptibility to hypersensitivity for certain drugs has been proved beyond doubt though the trend is ethnicity and phenotype dependent. Application of this newly acquired knowledge may reduce or abolish the morbidity and mortality associated with cutaneous drug hypersensitivity.