Mental Health Research Methods Quiz

A variable that can take on any value within a range, usually measured on a scale.

A statistical method used to investigate the relationship between a continuous outcome variable and a binary exposure variable. It helps to understand how much the exposure group's average outcome score differs from the unexposed group.

A measure of the average score of a group. For example, the average BDI-II score of participants in the mirtazapine group.

A score that represents the spread of scores around the average. A larger standard deviation indicates a greater spread.

A measure of the difference between two groups' means. It represents how much the mean score of the exposed group differs from the mean score of the unexposed group.

Mental health research often analyzes data to understand how common problems are, who is more likely to experience them, and what causes and outcomes are associated with them.

Binary outcomes have only two possible values, like yes or no, or present or absent, such as whether someone has a diagnosis.

Count outcomes are measured by counting the number of times something occurs within a certain period, such as the number of panic attacks a person has in a week.

The PICO framework helps define the core elements of a research question: the Patient/problem, Intervention, Comparison, and Outcome.

Hypothesis testing is a statistical process to evaluate if there's enough evidence to reject a null hypothesis, which is the default assumption. For example, does a new treatment significantly improve symptoms compared to standard care?

A p-value tells the probability of observing the results if the null hypothesis were true. A low p-value, like less than 0.05, suggests strong evidence to reject the null hypothesis.

A confidence interval is a range of plausible values for a parameter, like the true effect of a treatment. A narrow confidence interval indicates a more precise estimate.

The chance or likelihood of an event occurring.

The ratio of the probability of an event occurring to the probability of it not occurring.

The ratio of the odds of an outcome in the exposed group to the odds of the outcome in the unexposed group.

The risk of an outcome in the exposed group divided by the risk of the outcome in the unexposed group.

An OR or RR greater than 1 indicates an increased risk or odds of an outcome in the exposed group.

An OR or RR less than 1 indicates a decreased risk or odds of an outcome in the exposed group.

An OR or RR of 1 indicates no difference in risk or odds between the exposed and unexposed groups.

A value representing the null hypothesis in OR or RR analysis, where there is no difference in risk or odds between groups.

Odds ratios are the effect estimates produced from logistic regression, a statistical technique used to predict the probability of a binary outcome (e.g., presence or absence of a condition) based on one or more predictor variables. These odds ratios represent the multiplicative change in the odds of the outcome for a one-unit increase in the predictor variable.

Used to estimate the incidence rate ratio (IRR), which compares the rate of new events in two groups (e.g., exposed vs unexposed) over a period of time. Poisson regression is applied when the outcome is a count variable, such as the number of events occurring within a specified period.

A statistical technique used to estimate the association between an exposure and an outcome, while controlling for potential confounding variables that might influence the relationship.

The total amount of time individuals contribute to a study. It is calculated by summing the time each participant is at risk for the outcome. For example, if 10 participants are followed for 5 years each, the total PYAR would be 50 person-years.

The probability of an event occurring during a specific time period, calculated by dividing the number of new events by the total observation time. For example, the heart attack rate per 1000 person-years.

A measure of the proportion of individuals who experience an event during a specified time period, calculated by dividing the number of new events by the number at risk at the start of the observation period.

A bias that occurs when time spent in the study without the outcome (immortal time) is incorrectly included in the analysis. This can inflate the apparent effect of the exposure and lead to misleading results.

A statistical value representing the odds of observing the results you got if there were NO real effect or difference. It helps determine if your findings are due to chance or a genuine effect.

The null hypothesis in odds or risk ratios is represented by 1, indicating no difference or association between the groups.

A smaller p-value indicates stronger evidence against the null hypothesis, meaning it's less likely the results are due to chance.

A larger p-value indicates weaker evidence against the null hypothesis, suggesting the observed differences could be due to random chance.

While it's tempting to interpret a p-value by comparing it to a threshold like 0.05, a p-value should be considered as a continuous measure of evidence, not just significant or not.

While p-values can be helpful, they don't tell the whole story. It's crucial to look at the effect size and confidence interval alongside the p-value to get a fuller picture.

You should always report the p-value precisely rather than stating 'p<0.05' to ensure more precise and transparent presentation of your findings.

Frequentist statistics is a framework that focuses on testing the strength of evidence against the null hypothesis. It uses p-values to determine if the null is likely true or not.

The incidence rate ratio (IRR) is a statistical measure used to compare the incidence rates of an outcome (like depression) between two groups (e.g., females and males). It tells us how many times higher the incidence rate is in the exposed group (females) compared to the unexposed group (males)

Poisson regression is a statistical model commonly used in mental health research to analyze count data (like the number of depressive episodes) and estimate the association between an exposure (e.g., being female) and an outcome (depression). It helps determine if there's a significant relationship, and measures the impact of the exposure (IRR) on the outcome.

The null hypothesis is the starting assumption in hypothesis testing, stating that there is no difference or association between the groups being compared. The goal is to find evidence to disprove the null hypothesis and support an alternative hypothesis that suggests there is a difference or association.

A confidence interval (CI) is a range of plausible values for an effect estimate (like the IRR) that provides an idea of the precision of the estimate. A narrower CI indicates a more precise estimate, while a wider CI suggests more uncertainty.

The p-value tells us the probability of obtaining the observed results if the null hypothesis were true. A low p-value (typically less than 0.05) indicates that the observed results are unlikely if the null hypothesis is true, providing evidence to reject the null.

When conducting hypothesis testing, our goal is to gather enough evidence to confidently reject the null hypothesis. This means demonstrating that the observed results are unlikely to have occurred by chance alone, indicating a real difference or association between the groups.

The mean difference is a measure of how much the average outcome score of the exposed group differs from the average outcome score of the unexposed group. A larger mean difference indicates a greater difference in outcomes between the groups.

A covariate is a variable that is not the primary focus of the study, but may potentially influence the relationship between the exposure and outcome. Controlling for covariates helps adjust for potential confounding factors and provide a more accurate assessment of the association between the exposure and outcome.