Diagnostic Performance of the PalmScan VF2000 Virtual Reality Visual Field Analyzer for Identification and Classification of Glaucoma

Purpose: To evaluate the diagnostic test properties of the Palm Scan VF2000® Virtual Reality Visual Field Analyzer for diagnosis and classification of the severity of glaucoma. Methods: This study was a prospective cross-sectional analysis of 166 eyes from 97 participants. All of them were examined by the Humphrey® Field Analyzer (used as the gold standard) and the Palm Scan VF 2000® Virtual Reality Visual Field Analyzer on the same day by the same examiner. We estimated the kappa statistic (including 95% confidence interval [CI]) as a measure of agreement between these two methods. The diagnostic test properties were assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The sensitivity, specificity, PPV, and NPV for the Virtual Reality Visual Field Analyzer for the classification of individuals as glaucoma/non-glaucoma was 100%. The general agreement for the classification of glaucoma between these two instruments was 0.63 (95% CI: 0.56–0.78). The agreement for mild glaucoma was 0.76 (95% CI: 0.61–0.92), for moderate glaucoma was 0.37 (0.14–0.60), and for severe glaucoma was 0.70 (95% CI: 0.55–0.85). About 28% of moderate glaucoma cases were misclassified as mild and 17% were misclassified as severe by the virtual reality visual field analyzer. Furthermore, 20% of severe cases were misclassified as moderate by this instrument. Conclusion: The instrument is 100% sensitive and specific in detection of glaucoma. However, among patients with glaucoma, there is a relatively high proportion of misclassification of severity of glaucoma. Thus, although useful for screening of glaucoma, it cannot replace the Humphrey® Field Analyzer for the clinical management in its current form

Individual and Combined Effects of Diabetes and Glaucoma on Total Macular Thickness and Ganglion Cell Complex Thickness: A Cross-sectional Analysis

Purpose: Presence of diabetes in glaucoma patients may influence findings while documenting the progression of glaucoma. We conducted the study to compare individual and combined effects of diabetes and glaucoma on macular thickness and ganglion cell complex thickness. Methods: The present study is a cross-sectional analysis of 172 eyes of 114 individuals. The groups were categorized according to the following conditions: glaucoma, diabetes mellitus, both glaucoma and diabetes (‘both’ group), and none of these conditions (‘none’ group). Patients with diabetes did not have diabetic retinopathy (DR). We compared retinal nerve fiber layer (RNFL) thickness, ganglion cell complex (GCC) thickness, foveal loss of volume (FLV), and global loss of volume (GLV) among the groups. We used random effects multivariate analysis to adjust for potential confounders. Results: The mean (SD) age of these individuals was 60.7 (10.1) years. The total average RNFL and GCC were significantly lower in the glaucoma group (RNFL: –36.27, 95% confidence intervals [CI]: –42.79 to –29.74; P < 0.05, and GCC: –26.24, 95% CI: –31.49 to –20.98; P < 0.05) and the ‘both’ group (RNFL: –24.74, 95% CI: –32.84 to –16.63; P < 0.05, and GCC: –17.92, 95% CI: –24.58 to –11.26; P < 0.05) as compared with the ‘none’ group. There were no significant differences in the average RNFL values and total average GCC between the diabetes group and the ‘none’ group. The values of FLV and GLV were significantly higher in the ‘glaucoma’ group and the ‘both’ group as compared with the ‘none’ group. The foveal values were not significantly different across these four groups. Among the glaucoma cases, 25% were mild, 30% were moderate, and 45% were severe; there was no significant difference in the proportion of severity of glaucoma between the ‘glaucoma only’ and ‘both’ groups (P = 0.32). After adjusting for severity and type of glaucoma, there were no statistically significant differences in the values of average RNFL (6.6, 95% CI: –1.9 to 15.2; P = 0.13), total average GCC (3.6, -95% CI: –2.4 to 9.6; P = 0.24), and GLV (–3.9, 95% CI: –9.5 to 1.6; P = 0.16) in the ‘both group’ as compared with the glaucoma only group. Conclusion: We found that diabetes with no DR did not significantly affect the retinal parameters in patients with glaucoma. Thus, it is less likely that thickness of these parameters will be overestimated in patients with glaucoma who have concurrent diabetes without retinopathy

Methodology series module 10: Qualitative health research

Although quantitative designs are commonly used in clinical research, some studies require qualitative methods. These designs are different from quantitative methods; thus, researchers should be aware of data collection methods and analyses for qualitative research. Qualitative methods are particularly useful to understand patient experiences with the treatment or new methods of management or to explore issues in detail. These methods are useful in social and behavioral research. In qualitative research, often, the main focus is to understand the issue in detail rather than generalizability; thus, the sampling methods commonly used are purposive sampling; quota sampling; and snowball sampling (for hard to reach groups). Data can be collected using in-depth interviews (IDIs) or focus group discussions (FGDs). IDI is a one-to-one interview with the participant. FGD is a method of group interview or discussion, in which more than one participant is interviewed at the same time and is usually led by a facilitator. The commonly used methods for data analysis are: thematic analysis; grounded theory analysis; and framework analysis. Qualitative data collection and analysis require special expertise. Hence, if the reader plans to conduct qualitative research, they should team up with a qualitative researcher

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

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 9: Designing questionnaires and clinical record forms – Part II

This article is a continuation of the previous module on designing questionnaires and clinical record form in which we have discussed some basic points about designing the questionnaire and clinical record forms. In this section, we will discuss the reliability and validity of questionnaires. The different types of validity are face validity, content validity, criterion validity, and construct validity. The different types of reliability are test-retest reliability, inter-rater reliability, and intra-rater reliability. Some of these parameters are assessed by subject area experts. However, statistical tests should be used for evaluation of other parameters. Once the questionnaire has been designed, the researcher should pilot test the questionnaire. The items in the questionnaire should be changed based on the feedback from the pilot study participants and the researcher's experience. After the basic structure of the questionnaire has been finalized, the researcher should assess the validity and reliability of the questionnaire or the scale. If an existing standard questionnaire is translated in the local language, the researcher should assess the reliability and validity of the translated questionnaire, and these values should be presented in the manuscript. The decision to use a self- or interviewer-administered, paper- or computer-based questionnaire depends on the nature of the questions, literacy levels of the target population, and resources

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

Men who have sex with men and transgenders in Mumbai, India: An emerging risk group for STIs and HIV

Background: Men who have sex with men and transgenders are an important risk group for sexually transmitted infections (STIs) and human immunodeficiency virus (HIV). They have risky sexual behaviors but low risk perception. Objectives: To assess the sexual behavior, STIs, HIV, and identify factors associated with HIV in men who have sex with men (MSM) and transgenders (TGs) in Mumbai. Methods: Participants were enrolled from two clinics in Mumbai. They completed an interviewer-administered questionnaire and were evaluated for STIs and HIV infection. Results: A total of 150 participants, 122 MSM and 28 TGs were evaluated; 17% of MSM and 68% of the TGs were HIV infected. HIV infection in MSM was associated with serological positivity for HSV2 IgG [adjusted odds ratio (aOR), 95% confidence interval (CI): 9.0 (2.2-36.9)], a positive Treponema pallidum hemagglutination assay (TPHA) [aOR (95% CI): 6.0 (1.5-24.0)], greater than five acts of receptive anal sex in the past six months [aOR (95% CI): 4.3 (1.2-15.0)] and per category increase in age (18-24 yrs, 25-29 yrs, > 30 yrs) [aOR (95% CI): 3.1 (1.3-7.1)] in multivariate analysis. Consistent condom use during receptive anal sex in the past six months was low (27%). Many MSM were married (22%) or had sex with females and may act as a 'bridge population'. HIV infection in TGs was associated with a positive TPHA [OR (95% CI): 9.8 (1.5-63.9)] and HSV 2 IgG [OR (95% CI): 6.7 (1.1-40.4)] in univariate analysis. Conclusion: Prior STIs were strongly associated with HIV infection in MSM and TGs. These groups should be the focus of intensive intervention programs aimed at STI screening and treatment, reduction of risky sexual behavior and promotion of HIV counseling and testing

Summary and synthesis: How to present a research proposal

This concluding module attempts to synthesize the key learning points discussed during the course of the previous ten sets of modules on methodology and biostatistics. The objective of this module is to discuss how to present a model research proposal, based on whatever was discussed in the preceding modules. The lynchpin of a research proposal is the protocol, and the key component of a protocol is the study design. However, one must not neglect the other areas, be it the project summary through which one catches the eyes of the reviewer of the proposal, or the background and the literature review, or the aims and objectives of the study. Two critical areas in the “methods” section that cannot be emphasized more are the sampling strategy and a formal estimation of sample size. Without a legitimate sample size, none of the conclusions based on the statistical analysis would be valid. Finally, the ethical parameters of the study should be well understood by the researchers, and that should get reflected in the proposal