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In research, outcomes are often categorized as primary and secondary. The primary outcome is the most important one; it determines whether the study is considered ‘successful’ or not. Secondary outcomes are chosen because they provide supporting evidence for the results of the primary outcome or additional information about the subject being studied. For reasons that are explained in this paper, secondary outcomes should be cautiously interpreted. There are varying practices regarding publishing secondary outcomes. Some authors publish these separately, while others include them in the main publication. In some contexts, the former can lead to concerns about the quality and relevance of the data being published. In this article, we discuss primary and secondary outcomes, the importance and interpretation of secondary outcomes, and considerations for publishing multiple outcomes in separate papers. We also discuss the special case of secondary analyses and post hoc analyses and provide guidance on good publishing practices. Throughout the article, we use relevant examples to make these concepts easier to understand. While the article is primarily aimed at early career researchers, it offers insights that may be helpful to researchers, reviewers, and editors across all levels of expertise.

This article presents a table containing redacted data from a real study. The table contains three curiosities: statistical significance in the absence of clinical significance, narrow standard deviations, and the absence of a placebo effect. The data in the table had been obtained by an inexperienced rater; how the inexperience compromised the data is explained. Action points for rater experience, rater training, and rating procedures are suggested.

Research outcomes in mental health disciplines are usually assessed using rating instruments that were developed as English language versions. However, in countries such as India, English is not the native language, and patients at even a single research center may speak in different regional tongues. It is permissible to assess such patients using rater-administered English language instruments designed to be scored after an unstructured interview conducted in the patient’s preferred language. For many reasons, related to reliability and validity, it is not permissible to assess such patients in their preferred language by translating, impromptu, from English language versions of instruments that were designed to be self-administered or administered as a structured interview. In such situations, standardized, local language versions of the instruments should be used; that is, local language versions with established reliability and validity.

Management of negative symptoms is one of the most challenging and important unmet needs of schizophrenia treatment. Negative symptoms together with positive symptoms result in significant psychosocial impairment and poor quality of life. Existing studies on atypical antipsychotics reported limited treatment adherence due to higher prevalence of treatment-emergent adverse events, such as diabetes, weight gain, hyperlipidemia, hyperprolactinemia and hypertension. A compound with greater affinity for dopamine D2/D3 receptors may improve negative symptoms, mood, and cognitive impairment associated with schizophrenia. In 2015, the US FDA has approved cariprazine, a partial D2/D3 agonist for treatment of schizophrenia, mania or mixed episodes. Midlands and Lancashire Commissioning Support Unit, UK (2019) has particularly suggested cariprazine for the treatment of predominant negative symptoms of schizophrenia. India’s Central Drugs Standard Control Organization (CDSCO) has approved cariprazine in 2021 for the treatment of schizophrenia, manic or mixed episodes associated with bipolar I disorder. A ten-fold greater affinity for D3 receptors and partial agonism to serotonin receptors, along with longer half-life make cariprazine distinct when compared with other atypical antipsychotics. Cariprazine is also reported to have fewer incidents of metabolic and hormonal adverse events, and has been shown to provide better relapse prevention. Recent evidence indicates promising effect of cariprazine in ameliorating negative symptoms as well as psychotic symptoms in patients with schizophrenia. In addition, improved adherence to treatment (adjunctive/monotherapy) with cariprazine in patients having inadequate response to an ongoing antipsychotic treatment has also been clinically established. This review presents the evidence-based safety and efficacy of cariprazine for treatment of predominant negative symptoms of schizophrenia.

A factorial design examines the effects of two independent variables on a single, continuous dependent variable. The statistical test employed to analyze the data is a two-way analysis of variance (ANOVA). This test yields three results: a main effect for each of the independent variables and an interaction effect between the two independent variables. This article explains factorial designs and two-way ANOVA with the help of a worked example using hypothetical data in a spreadsheet provided as a supplementary file. The main effects and interaction effects are explained and illustrated using tables and figures. A short discussion provides general notes about the concepts explained in this article, along with brief notes on repeated measures ANOVA and higher order ANOVAs. Many additional examples, with figures and explanations, are provided in the supplementary materials, which the reader is strongly encouraged to view.

The terms independent variables, covariates, confounding variables , and confounding by indication are often imprecisely used in the context of regression. Independent variables are the full set of variables whose influence on the outcome is studied. Covariates are the independent variables that are included not because they are of interest but because their influence on the outcome can be adjusted for, leaving a more precise understanding of how the single remaining independent variable influences the outcome. Confounding variables are variables that are associated with both independent variables and outcomes; so, the relationship identified between independent variables and outcomes may be due to the confounding variable rather than to the independent variable. Potential confounders should be identified, measured, and adjusted for in regression, just as other covariates are. Confounding by indication occurs when the presence of the independent variable is driven by the confounding variable. Confounding by indication is a special kind of confounding; a confounding variable is a special kind of covariate; and a covariate is a special kind of independent variable in regression analysis. These terms and concepts are explained with the help of examples.

In research, there are different, overlapping ways in which the plan of analysis may be described. This article explains planned (prespecified) vs post hoc, primary vs secondary, hypothesis-driven vs exploratory, and subgroup and sensitivity analyses; intent-to-treat vs per-protocol vs completer analysis was explained in an earlier article in this column. A prespecified analysis is one that is outlined before the study starts; it is usually separated into primary and secondary analyses for the single primary outcome and the many secondary outcomes, respectively. Exploratory analyses examine relationships between variables in the absence of well-defined expectations; these tend to be statistical fishing expeditions. Subgroup analyses examine findings in categories of subjects in the sample. Sensitivity analyses examine whether the findings remain similar if the data are handled in a way that differs from the original plan of analysis. Post hoc analyses examine hypotheses that are conceptualized after the data are seen. All these terms are explained with the help of examples, and strengths and limitations are briefly discussed.

Patients are filtered by rigorously defined study selection criteria for recruitment into research; this is necessary to improve signal detection, improve internal validity, reduce study-related risks, and meet ethical standards. Research patients are assessed and managed in ways that differ from usual practice. So, neither patients nor the treatment environment resembles everyday patients treated in everyday practice. This diminishes the generalizability of study findings; that is, their external validity. There is, therefore, an increasing trend to conduct “real-world studies.” In this context, “real-world patients” are those who are not filtered by restrictive study selection criteria, and “real-world settings” are those in which patients are managed with few study-related guidelines and restrictions. The elephant in the room is that the glamour associated with such real-world studies is an illusion. This is because real-world patients in one real-world setting can differ widely from real-world patients in another real-world setting. So, even in real-world studies, we can only generalize study findings to the population from which the sample was drawn and the setting in which the sample was managed. As a final note, many assessments in research, such as computerized or pen-and-paper neuropsychological tests, are not real-world measures as are, for example, measures of activities of daily living or quality of life.

Survival analysis is used to analyze data from patients who are followed for different periods of time and in whom the outcome of interest, a dichotomous event, may or may not have occurred at the time the study is halted; data from all patients are used in the analysis, including data from patients who dropped out, regardless of the duration of follow-up. This article discusses basic concepts in survival analysis, explains technical terms such as censoring, and provides reasons why ordinary methods of analysis cannot be applied to such data. The Kaplan-Meier survival curve is described, as is the Cox proportional hazards regression and the hazard ratio. Supplementary information includes a data file, graphs with explanations, and additional discussions; these are provided to enhance the reader’s experience and understanding.

In regression analysis, the adjusted R 2 value describes the proportion of the variance in the dependent variable that is explained by the independent variables in the equation. What remains is unexplained or residual variance. Residual variance has two components: the contribution of unmeasured variables (residual confounding) and measurement error (noise). These concepts are explained using examples.

The previous article in this series explained basic concepts related to statistical signals and statistical noise in research. This article explains statistical noise in the context of randomized controlled trials (RCTs) and observational studies and offers suggestions on how noise in such studies may be reduced so as to better detect and understand the signal. Postrandomization bias related to RCTs and confounding in observational studies are discussed. Examples are provided to facilitate understanding.

The signal is the outcome of interest in a study; it may be the value of a variable or it may be the value of a relationship between variables. Signals in research are distorted by statistical noise. This statistical noise is generated by extraneous variables that may be adequately measured, inadequately measured, unmeasured, or unknown; the subject-to-subject variation in the signal resulting from the effects of these extraneous variables is captured by the standard deviation. Thus, the standard deviation is a measure of statistical noise. This article, the first in a series, explains all of these concepts with the help of examples.

Researchers who have completed a study need to transfer their data from individual patient research files to a mastersheet. This article is a beginner’s guide to creating data spreadsheets that can be shared with others and imported into statistical programs. The article discusses choice of spreadsheet software, use of separate worksheets for main data and granular data, use of columns for variables and rows for patient data, coding data, entry of data into cells in the worksheets, and other matters.

A formally recruited, studied, and followed cohort is a rich source of data for a wide range of research questions; the larger the cohort, the greater the quantity of data systematically collected, and the longer the duration of follow-up, the greater the value of the cohort. Despite the ease of conducting cohort studies and the research value of such studies, few centers with long-term research goals conduct such studies. This article provides easy-to-understand practical guidance on how to start and run a cohort study.

In randomized controlled trials, randomization creates groups that are reasonably well balanced on all baseline variables, whether measured, unmeasured, or unknown. Postbaseline events disturb this balance, resulting in postrandomization biases. Drop-out is one such event. There are two main methods for data analysis when there are dropouts. One method is to analyze data from only those who complete the study (completer analysis), or only those who complete the study and also comply with all its key elements (per-protocol analysis, a special type of completer analysis). The other method is to analyze the data from all randomized patients, regardless of dropout (intent-to-treat [ITT] analysis), or all randomized patients who meet an additional criterion, such as taking at least one dose of study drug (modified ITT [mITT] analysis, a special type of ITT analysis). Completer analyses present results in the ideal situation in which patients take medications as advised. ITT analyses present results related to real-world practice, where patients may be irregular with dosing or stop taking medications. The advantages and disadvantages of each type of analysis are discussed. The handling of missing data in ITT and mITT analysis is also briefly discussed.

Case-control studies are observational studies in which cases are subjects who have a characteristic of interest, such as a clinical diagnosis, and controls are (usually) matched subjects who do not have that characteristic. After cases and controls are identified, researchers “look back” to determine what past events (exposures), if any, are significantly associated with caseness. For “looking back,” data may be obtained by clinical history-taking or from medical records such as case files or large electronic health care databases. The data are analyzed using logistic regression, which adjusts for confounding variables and yields an odds ratio and a probability value for the association between the exposure of interest (independent variable) and caseness (dependent variable). Because case-control studies are not randomized controlled studies, cause–effect relationships do not necessarily explain significant associations detected in the regressions; unexplored confounding may be responsible. These concepts are explained with the help of examples.

In a cohort study, a group of subjects (the cohort) is followed for a period of time; assessments are conducted at baseline, during follow-up, and at the end of follow-up. Cohort studies are, therefore, empirical, longitudinal studies based on data obtained from a sample; they are also observational and (usually) naturalistic. Analyses can be conducted for the cohort as a whole or for subgroups amongst which comparisons can be drawn. Because there is no randomization to the subgroups of interest, cause and effect relationships cannot be determined, and relationships between variables must be stated as associations that may or may not be influenced by confounding. The cohort that is studied can be prospectively or retrospectively defined, and each method has its advantages and disadvantages. These and other issues are explained with the help of examples. A special note is made of cohort studies in Indian psychiatry.

Since 1997, studies have found that Asians need lower clozapine doses than Caucasians. Caucasians with average clozapine metabolism may need from 300 to 600 mg/day to reach the therapeutic range (350 ng/ml). Thus, serum clozapine concentration-to-dose (C/D) ratios typically range between 0.60 (male smokers) and 1.20 (female non-smokers). A 2019 systematic review of clozapine levels demonstrated weighted mean C/D ratios of 1.57 in 876 East Asians and 1.07 in 1147 Caucasians (P < .001). In Asian countries, average clozapine doses are lower than 300 mg/day. After sex and smoking stratification in 5 Asian samples with clozapine concentrations, the clozapine dose required to reach 350 ng/ml in female non-smokers ranged from 145 to 189 mg/day and in male smokers, from 259 to 294 mg/day. Thus, in Asian patients with average metabolism (with no inducers other than smoking, with no inhibitors, and in the absence of extreme obesity), the dose needed for clinical response may range between 150 mg/day for female non-smokers to 300 mg/day for male smokers. Clozapine levels may help personalize dosing in clozapine poor metabolizers (PMs) and ultrarapid metabolizers (UMs). Asian PMs may need very low doses (50-150 mg/day) to obtain therapeutic concentrations. About 10% (range 2-13%) of Asians are genetic PM cases. Other PMs are patients taking CYP1A2 inhibitors such as fluvoxamine, oral contraceptives, and valproate. Temporary clozapine PM status may occur during severe systemic infections/inflammations with fever and C-reactive protein (CRP) elevations. Asian UMs include patients taking potent inducers such as phenytoin, and rarely, valproate.

Many students are not aware that research design can be simultaneously described in many different ways; for example, a drug trial may be described as being prospective, longitudinal, randomized, double-blind, and placebo-controlled, all at the same time. This article provides examples to explain how studies can be simultaneously prospective and cross-sectional, prospective and longitudinal, retrospective and cross-sectional, and retrospective and longitudinal. The term prospective indicates that the study data are newly collected, whereas the term retrospective indicates that the study data already exist in records and merely need to be extracted for study. The term cross-sectional indicates that the study subjects are studied on a single occasion; that is, at a single point in time. The term longitudinal indicates that the study subjects are followed up and that there is almost always more than one time point at which the subjects are assessed. This article also describes unusual designs, such as cross-sectional randomized controlled trials and retrospective studies with prospective data ascertainment.

Patients may be assessed using a battery of tests where different tests yield scores in different units, where different tests have different minimum and maximum scores, and where higher or lower scores mean different things in different tests. Therefore, a composite test score cannot be obtained by simple addition or averaging of scores in the individual tests. However, if performances in individual tests are converted to Z scores, the Z scores can be added or averaged to yield a composite score that can be interpreted or processed using conventional statistical methods. This article explains in simple ways how Z scores are calculated, what the properties of Z scores are, how Z scores can be interpreted, and how Z scores can be converted into other standard scores.

A quasi-experimental (QE) study is one that compares outcomes between intervention groups where, for reasons related to ethics or feasibility, participants are not randomized to their respective interventions; an example is the historical comparison of pregnancy outcomes in women who did versus did not receive antidepressant medication during pregnancy. QE designs are sometimes used in noninterventional research, as well; an example is the comparison of neuropsychological test performance between first degree relatives of schizophrenia patients and healthy controls. In QE studies, groups may differ systematically in several ways at baseline, itself; when these differences influence the outcome of interest, comparing outcomes between groups using univariable methods can generate misleading results. Multivariable regression is therefore suggested as a better approach to data analysis; because the effects of confounding variables can be adjusted for in multivariable regression, the unique effect of the grouping variable can be better understood. However, although multivariable regression is better than univariable analyses, there are inevitably inadequately measured, unmeasured, and unknown confounds that may limit the validity of the conclusions drawn. Investigators should therefore employ QE designs sparingly, and only if no other option is available to answer an important research question.

Randomized controlled trials (RCTs) of a new investigational drug often include active as well as placebo control arms. The active arm, comprising an approved treatment for the indication under study, along with the placebo arm, are together required to establish assay sensitivity; if the active treatment outperforms placebo, as expected, the results of the RCT can be further interpreted, but if the active treatment is no better than placebo (such as because of ceiling or floor effects), the RCT is a failed trial. The concepts involved are explained from scientific and ethical perspectives.

The calculation of a P value in research and especially the use of a threshold to declare the statistical significance of the P value have both been challenged in recent years. There are at least two important reasons for this challenge: research data contain much more meaning than is summarized in a P value and its statistical significance, and these two concepts are frequently misunderstood and consequently inappropriately interpreted. This article considers why 5% may be set as a reasonable cut-off for statistical significance, explains the correct interpretation of P < 0.05 and other values of P, examines arguments for and against the concept of statistical significance, and suggests other and better ways for analyzing data and for presenting, interpreting, and discussing the results.

Background: Despite exponential growth in Indian research, Indian journals have low impact factors. A previous study by one of the authors (CA) of this paper showed that articles published in the Indian Journal of Psychiatry (IJP) under-referenced previously published relevant papers in the same journal. Based on this, we decided to investigate the citation characteristics of contemporary scientific articles published in the Indian Journal of Psychological Medicine (IJPM). Methods: The citation characteristics of scientific articles published in 2018 (Vol 40, issues 1–6) in the IJPM were examined to determine how well the authors cited relevant articles published during the past ten years in the IJPM and the IJP. Results: There were 145 and 142 citation-worthy articles in the IJPM and the IJP, respectively; of these, 85.5% and 65.5%, respectively, had not been cited. Conclusions: Authors publishing in the IJPM under-reference previous relevant research published in the IJPM and IJP. This suggests unawareness of, deliberate disregard of, or even disdain for prior Indian research in the field. Additionally, if Indian researchers do not cite previous Indian research in the field published in Indian journals, the citation metrics of Indian journals will not grow.

Students without prior research experience may not know how to conceptualize and design a study. This is the second of a two-part article that explains how an understanding of the classification and operationalization of variables is the key to the process. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. They may be operationalized as categorical or continuous variables. Categorical variables are expressed as category frequencies in the sample as a whole, while continuous variables are expressed as absolute numbers for each subject in the sample. Continuous variables should not be converted into categorical variables; there are many reasons for this, the most important being that precision and statistical power are lost. However, in certain circumstances, such as when variables cannot be accurately measured, when there is an administrative or public health need, or when the data are not normally distributed, it may be justifiable to do so. Confounding variables are those that increase (or decrease) the apparent effect of an independent variable on the dependent variable, thereby producing spurious (or suppressing true) relationships. These and other concepts are explained with the help of clinically relevant examples.

Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.

Most research is conducted on convenience and purposive samples that may be randomly or nonrandomly drawn. A convenience sample is the one that is drawn from a source that is conveniently accessible to the researcher. A purposive sample is the one whose characteristics are defined for a purpose that is relevant to the study. The findings of a study based on convenience and purposive sampling can only be generalized to the (sub)population from which the sample is drawn and not to the entire population. This article explains the concepts involved with the help of examples of both good and bad sampling practice. Database studies and studies with enriched designs are cited as special examples of convenience and purposive sampling. Issues related to the internal and external validity of convenience and purposive samples are explained. The importance of good sampling techniques in the design and interpretation of research is understated; this must change.

Online surveys are growing in popularity, perhaps because they are an easy, convenient, and inexpensive means of data collection. Online surveys commonly suffer from two serious methodological limitations: the population to which they are distributed cannot be described, and respondents with biases may select themselves into the sample. Research is of value only when the findings from a sample can be generalized to a meaningful population. When the population addressed by the survey cannot be described, and when the sample is contaminated by respondents with biases, findings from online surveys cannot be generalized and may therefore mislead.

Knowledge, attitude, and practice (KAP) surveys are popular in health care because they provide useful information and appear easy to design and execute. There are subtleties, however, in such surveys that early career researchers need to be aware of. This article does not provide a detailed review of the subject, nor does it address theory; rather, it provides practical guidance on matters such as identifying the need for the survey; defining the target population; preparing the questions that address knowledge, attitudes, and practice; preparing options for the answers to the items in the questionnaire; deciding how to score the instrument and analyze the results; and validating the instrument. Specific examples are presented to help readers understand and apply the guidance in various contexts.

Sexuality in dementia is infrequently addressed. Dementia is characterized by a progressive deterioration in all domains of functioning, including loss of sexual function. However, the diagnosis of dementia does not mean an immediate or complete cessation of sexuality in the person, or a loss of the ability to consent to sexual activity with a partner. A discussion of sexuality in dementia occurs infrequently in clinical care for several reasons. These include (a) a discomfort in discussing sexuality in older adults, (b) the fear of causing social or cultural offense, and (c) the assumption that the cessation of the reproductive period implies the end of sexual life in older adults. There is also a tendency to focus on the preservation of cognition and independence, with relative neglect of the need for physical and emotional intimacy or quality of life. Patients with dementia are more likely to be sexually active than not. The most common change is a lack or loss or sexual desire. Inappropriate sexual behaviors occur in a minority of patients (28%) and can be usually managed with behavioral measures, with the use of pharmacotherapy for symptomatic management in refractory cases. Other clinical and ethical concerns in dementia include the capacity to consent to sexual intimacy, the formation of new relationships, sexuality in long-term residential facilities, and vulnerability to sexual abuse. Dementia care guidelines recommend a low threshold of suspicion for abuse, with a focus on patient safety. These must, however, be counterweighed by respect for patient autonomy and wishes.

Many authors are unsure of whether to present the mean along with the standard deviation (SD) or along with the standard error of the mean (SEM). The SD is a descriptive statistic that estimates the scatter of values around the sample mean; hence, the SD describes the sample. In contrast, the SEM is an estimate of how close the sample mean is to the population mean; it is an intermediate term in the calculation of the 95% confidence interval around the mean, and (where applicable) statistical significance; the SEM does not describe the sample. Therefore, the mean should always be accompanied by the SD when describing the sample. There are many reasons why the SEM continues to be reported, and it is argued that none of these is justifiable. In fact, presentation of SEMs may mislead readers into believing that the sample data are more precise than they actually are. Given that the standard error is not presented for other parameters, such as difference between means or proportions, and difference between proportions, it is suggested that presentation of SEM values can be done away with, altogether.