The receiver operating characteristic (ROC) curve is a useful tool for evaluating the diagnostic accuracy of a test in distinguishing between healthy and diseased individuals. It helps to identify the optimal cut-off point between sensitivity and specificity.

Other methods, such as visual inspection of forest plots and Cochran’s Q test, can be used to assess heterogeneity in meta-analysis. Visual inspection of forest plots is a quick and easy method, while Cochran’s Q test is a more formal and widely accepted approach.

For more information on heterogeneity in meta-analysis, further reading is recommended.

Validity in statistics refers to how accurately something measures what it claims to measure. There are two main types of validity: internal and external. Internal validity refers to the confidence we have in the cause and effect relationship in a study, while external validity refers to the degree to which the conclusions of a study can be applied to other people, places, and times. There are various threats to both internal and external validity, such as sampling, measurement instrument obtrusiveness, and reactive effects of setting. Additionally, there are several subtypes of validity, including face validity, content validity, criterion validity, and construct validity. Each subtype has its own specific focus and methods for testing validity.

Opportunity Cost in Economics: Understanding the Value of Choices

Opportunity cost is a crucial concept in economics that helps us make informed decisions. It refers to the value of the next-best alternative that we give up when we choose one option over another. This concept is particularly relevant when we have limited resources, such as a fixed budget, and need to make choices about how to allocate them.

For instance, if we decide to spend our money on antidepressants, we cannot use that same money to pay for cognitive-behavioral therapy (CBT). Both options have a value, but we have to choose one over the other. The opportunity cost of choosing antidepressants over CBT is the value of the benefits we would have received from CBT but did not because we chose antidepressants instead.

To compare the opportunity cost of different choices, economists often use quality-adjusted life years (QALYs). QALYs measure the value of health outcomes in terms of both quantity (life years gained) and quality (health-related quality of life). By using QALYs, we can compare the opportunity cost of different healthcare interventions and choose the one that provides the best value for our resources.

In summary, understanding opportunity cost is essential for making informed decisions in economics and healthcare. By recognizing the value of the alternatives we give up, we can make better choices and maximize the benefits we receive from our limited resources.

The odds ratio is used in case-control studies to measure the association between exposure and outcome, while the relative risk is used in cohort studies to measure the risk of developing an outcome in the exposed group compared to the unexposed group. To convert the odds ratio to a relative risk, one can use the formula: relative risk = odds ratio / (1 – incidence in the unexposed group x odds ratio).

Types of Primary Research Studies and Their Advantages and Disadvantages

Primary research studies can be categorized into six types based on the research question they aim to address. The best type of study for each question type is listed in the table below. There are two main types of study design: experimental and observational. Experimental studies involve an intervention, while observational studies do not. The advantages and disadvantages of each study type are summarized in the table below.

Type of Question Best Type of Study

Therapy Randomized controlled trial (RCT), cohort, case control, case series
Diagnosis Cohort studies with comparison to gold standard test
Prognosis Cohort studies, case control, case series
Etiology/Harm RCT, cohort studies, case control, case series
Prevention RCT, cohort studies, case control, case series
Cost Economic analysis

Study Type Advantages Disadvantages

Randomized Controlled Trial – Unbiased distribution of confounders – Blinding more likely – Randomization facilitates statistical analysis – Expensive – Time-consuming – Volunteer bias – Ethically problematic at times
Cohort Study – Ethically safe – Subjects can be matched – Can establish timing and directionality of events – Eligibility criteria and outcome assessments can be standardized – Administratively easier and cheaper than RCT – Controls may be difficult to identify – Exposure may be linked to a hidden confounder – Blinding is difficult – Randomization not present – For rare disease, large sample sizes of long follow-up necessary
Case-Control Study – Quick and cheap – Only feasible method for very rare disorders of those with long lag between exposure and outcome – Fewer subjects needed than cross-sectional studies – Reliance on recall of records to determine exposure status – Confounders – Selection of control groups is difficult – Potential bias: recall, selection
Cross-Sectional Survey – Cheap and simple – Ethically safe – Establishes association at most, not causality – Recall bias susceptibility – Confounders may be unequally distributed – Neyman bias – Group sizes may be unequal
Ecological Study – Cheap and simple – Ethically safe – Ecological fallacy (when relationships which exist for groups are assumed to also be true for individuals)

In conclusion, the choice of study type depends on the research question being addressed. Each study type has its own advantages and disadvantages, and researchers should carefully consider these when designing their studies.

Hypothesis testing involves the possibility of two types of errors, namely type I and type II errors. A type I error occurs when the null hypothesis is wrongly rejected of the alternative hypothesis is incorrectly accepted. This error is also referred to as an alpha error, error of the first kind, of a false positive. On the other hand, a type II error occurs when the null hypothesis is wrongly accepted. This error is also known as the beta error, error of the second kind, of the false negative.

Understanding Hypothesis Testing in Statistics

In statistics, it is not feasible to investigate hypotheses on entire populations. Therefore, researchers take samples and use them to make estimates about the population they are drawn from. However, this leads to uncertainty as there is no guarantee that the sample taken will be truly representative of the population, resulting in potential errors. Statistical hypothesis testing is the process used to determine if claims from samples to populations can be made and with what certainty.

The null hypothesis (Ho) is the claim that there is no real difference between two groups, while the alternative hypothesis (H1 of Ha) suggests that any difference is due to some non-random chance. The alternative hypothesis can be one-tailed of two-tailed, depending on whether it seeks to establish a difference of a change in one direction.

Two types of errors may occur when testing the null hypothesis: Type I and Type II errors. Type I error occurs when the null hypothesis is rejected when it is true, while Type II error occurs when the null hypothesis is accepted when it is false. The power of a study is the probability of correctly rejecting the null hypothesis when it is false, and it can be increased by increasing the sample size.

P-values provide information on statistical significance and help researchers decide if study results have occurred due to chance. The p-value is the probability of obtaining a result that is as large of larger when in reality there is no difference between two groups. The cutoff for the p-value is called the significance level (alpha level), typically set at 0.05. If the p-value is less than the cutoff, the null hypothesis is rejected, and if it is greater or equal to the cut off, the null hypothesis is not rejected. However, the p-value does not indicate clinical significance, which may be too small to be meaningful.

The Beck Depression Inventory cannot be classified as a ratio of interval scale as the scores do not have a consistent and meaningful numerical value. Instead, it is considered an ordinal scale where scores can be ranked in order of severity, but the difference between scores may not be equal in terms of the level of depression experienced. For example, a change from 8 to 13 may be more significant than a change from 35 to 40.

Scales of Measurement in Statistics

In the 1940s, Stanley Smith Stevens introduced four scales of measurement to categorize data variables. Knowing the scale of measurement for a variable is crucial in selecting the appropriate statistical analysis. The four scales of measurement are ratio, interval, ordinal, and nominal.

Ratio scales are similar to interval scales, but they have true zero points. Examples of ratio scales include weight, time, and length. Interval scales measure the difference between two values, and one unit on the scale represents the same magnitude on the trait of characteristic being measured across the whole range of the scale. The Fahrenheit scale for temperature is an example of an interval scale.

Ordinal scales categorize observed values into set categories that can be ordered, but the intervals between each value are uncertain. Examples of ordinal scales include social class, education level, and income level. Nominal scales categorize observed values into set categories that have no particular order of hierarchy. Examples of nominal scales include genotype, blood type, and political party.

Data can also be categorized as quantitative of qualitative. Quantitative variables take on numeric values and can be further classified into discrete and continuous types. Qualitative variables do not take on numerical values and are usually names. Some qualitative variables have an inherent order in their categories and are described as ordinal. Qualitative variables are also called categorical of nominal variables. When a qualitative variable has only two categories, it is called a binary variable.

To examine differences between various groups, researchers may conduct subgroup analyses by dividing participant data into subsets. These subsets may include specific demographics (e.g. gender) of study characteristics (e.g. location). Subgroup analyses can help explain inconsistent findings of provide insights into particular patient populations, interventions, of study types.

Qualitative research is a method of inquiry that seeks to understand the meaning and experience dimensions of human lives and social worlds. There are different approaches to qualitative research, such as ethnography, phenomenology, and grounded theory, each with its own purpose, role of the researcher, stages of research, and method of data analysis. The most common methods used in healthcare research are interviews and focus groups. Sampling techniques include convenience sampling, purposive sampling, quota sampling, snowball sampling, and case study sampling. Sample size can be determined by data saturation, which occurs when new categories, themes, of explanations stop emerging from the data. Validity can be assessed through triangulation, respondent validation, bracketing, and reflexivity. Analytical approaches include content analysis and constant comparison.

The Delphi Method: A Widely Used Technique for Achieving Convergence of Opinion

The Delphi method is a well-established technique for soliciting expert opinions on real-world knowledge within specific topic areas. The process involves multiple rounds of questionnaires, with each round building on the previous one to achieve convergence of opinion among the participants. However, there are potential issues with the Delphi method, such as the time-consuming nature of the process, low response rates, and the potential for investigators to influence the opinions of the participants. Despite these challenges, the Delphi method remains a valuable tool for generating consensus among experts in various fields.

To address the issue of drop-outs in a study, an intention to treat (ITT) analysis can be employed. Drop-outs can lead to attrition bias, which creates systematic differences in attrition across treatment groups. In an ITT analysis, all patients are included in the groups they were initially assigned to through random allocation. To handle missing data, two common methods are last observation carried forward and worst case scenario analysis.

The observed link between alcohol consumption and lung cancer is likely due to confounding factors, such as cigarette smoking. Confounding variables are those that are associated with both the independent and dependent variables, in this case, alcohol consumption and lung cancer.

Measures of Effect in Clinical Studies

When conducting clinical studies, we often want to know the effect of treatments of exposures on health outcomes. Measures of effect are used in randomized controlled trials (RCTs) and include the odds ratio (of), risk ratio (RR), risk difference (RD), and number needed to treat (NNT). Dichotomous (binary) outcome data are common in clinical trials, where the outcome for each participant is one of two possibilities, such as dead of alive, of clinical improvement of no improvement.

To understand the difference between of and RR, it’s important to know the difference between risks and odds. Risk is a proportion that describes the probability of a health outcome occurring, while odds is a ratio that compares the probability of an event occurring to the probability of it not occurring. Absolute risk is the basic risk, while risk difference is the difference between the absolute risk of an event in the intervention group and the absolute risk in the control group. Relative risk is the ratio of risk in the intervention group to the risk in the control group.

The number needed to treat (NNT) is the number of patients who need to be treated for one to benefit. Odds are calculated by dividing the number of times an event happens by the number of times it does not happen. The odds ratio is the odds of an outcome given a particular exposure versus the odds of an outcome in the absence of the exposure. It is commonly used in case-control studies and can also be used in cross-sectional and cohort study designs. An odds ratio of 1 indicates no difference in risk between the two groups, while an odds ratio >1 indicates an increased risk and an odds ratio <1 indicates a reduced risk.

Standard Deviation and Standard Error of the Mean

Standard deviation (SD) and standard error of the mean (SEM) are two important statistical measures used to describe data. SD is a measure of how much the data varies, while SEM is a measure of how precisely we know the true mean of the population. The normal distribution, also known as the Gaussian distribution, is a symmetrical bell-shaped curve that describes the spread of many biological and clinical measurements.

68.3% of the data lies within 1 SD of the mean, 95.4% of the data lies within 2 SD of the mean, and 99.7% of the data lies within 3 SD of the mean. The SD is calculated by taking the square root of the variance and is expressed in the same units as the data set. A low SD indicates that data points tend to be very close to the mean.

On the other hand, SEM is an inferential statistic that quantifies the precision of the mean. It is expressed in the same units as the data and is calculated by dividing the SD of the sample mean by the square root of the sample size. The SEM gets smaller as the sample size increases, and it takes into account both the value of the SD and the sample size.

Both SD and SEM are important measures in statistical analysis, and they are used to calculate confidence intervals and test hypotheses. While SD quantifies scatter, SEM quantifies precision, and both are essential in understanding and interpreting data.

A p-value represents the likelihood of rejecting a null hypothesis that is actually true. A smaller p-value indicates a lower chance of mistakenly rejecting the null hypothesis, providing evidence in favor of the alternative hypothesis.

Understanding Hypothesis Testing in Statistics

In statistics, it is not feasible to investigate hypotheses on entire populations. Therefore, researchers take samples and use them to make estimates about the population they are drawn from. However, this leads to uncertainty as there is no guarantee that the sample taken will be truly representative of the population, resulting in potential errors. Statistical hypothesis testing is the process used to determine if claims from samples to populations can be made and with what certainty.

The null hypothesis (Ho) is the claim that there is no real difference between two groups, while the alternative hypothesis (H1 of Ha) suggests that any difference is due to some non-random chance. The alternative hypothesis can be one-tailed of two-tailed, depending on whether it seeks to establish a difference of a change in one direction.

Two types of errors may occur when testing the null hypothesis: Type I and Type II errors. Type I error occurs when the null hypothesis is rejected when it is true, while Type II error occurs when the null hypothesis is accepted when it is false. The power of a study is the probability of correctly rejecting the null hypothesis when it is false, and it can be increased by increasing the sample size.

P-values provide information on statistical significance and help researchers decide if study results have occurred due to chance. The p-value is the probability of obtaining a result that is as large of larger when in reality there is no difference between two groups. The cutoff for the p-value is called the significance level (alpha level), typically set at 0.05. If the p-value is less than the cutoff, the null hypothesis is rejected, and if it is greater or equal to the cut off, the null hypothesis is not rejected. However, the p-value does not indicate clinical significance, which may be too small to be meaningful.

Clinical tests are used to determine the presence of absence of a disease of condition. To interpret test results, it is important to have a working knowledge of statistics used to describe them. Two by two tables are commonly used to calculate test statistics such as sensitivity and specificity. Sensitivity refers to the proportion of people with a condition that the test correctly identifies, while specificity refers to the proportion of people without a condition that the test correctly identifies. Accuracy tells us how closely a test measures to its true value, while predictive values help us understand the likelihood of having a disease based on a positive of negative test result. Likelihood ratios combine sensitivity and specificity into a single figure that can refine our estimation of the probability of a disease being present. Pre and post-test odds and probabilities can also be calculated to better understand the likelihood of having a disease before and after a test is carried out. Fagan’s nomogram is a useful tool for calculating post-test probabilities.

The most crucial factor in Hill’s criteria for causation is temporality, of the temporal relationship between exposure and outcome. It is imperative that the exposure to a potential causal factor, such as factor ‘A’, always occurs before the onset of the disease. This criterion is the only absolute requirement for causation. The other criteria include the strength of the relationship, dose-response relationship, consistency, plausibility, consideration of alternative explanations, experimental evidence, specificity, and coherence.

The sampling method employed was convenience sampling, which involved recruiting participants through flyers posted in clinics. However, this approach may lead to an imbalanced sample. To be considered purposive sampling, the researcher would need to demonstrate a deliberate effort to recruit participants based on specific characteristics, such as targeting individuals who had experienced a delayed diagnosis.

Qualitative research is a method of inquiry that seeks to understand the meaning and experience dimensions of human lives and social worlds. There are different approaches to qualitative research, such as ethnography, phenomenology, and grounded theory, each with its own purpose, role of the researcher, stages of research, and method of data analysis. The most common methods used in healthcare research are interviews and focus groups. Sampling techniques include convenience sampling, purposive sampling, quota sampling, snowball sampling, and case study sampling. Sample size can be determined by data saturation, which occurs when new categories, themes, of explanations stop emerging from the data. Validity can be assessed through triangulation, respondent validation, bracketing, and reflexivity. Analytical approaches include content analysis and constant comparison.

Funnel plots are utilized in meta-analyses to visually display the potential presence of publication bias. However, it is important to note that an asymmetric funnel plot does not necessarily confirm the existence of publication bias, as other factors may contribute to its formation.

Stats Publication Bias

Publication bias refers to the tendency for studies with positive findings to be published more than studies with negative findings, leading to incomplete data sets in meta-analyses and erroneous conclusions. Graphical methods such as funnel plots, Galbraith plots, ordered forest plots, and normal quantile plots can be used to detect publication bias. Funnel plots are the most commonly used and offer an easy visual way to ensure that published literature is evenly weighted. The x-axis represents the effect size, and the y-axis represents the study size. A symmetrical, inverted funnel shape indicates that publication bias is unlikely, while an asymmetrical funnel indicates a relationship between treatment effect and study size, indicating either publication bias of small study effects.

The t-test is appropriate for analyzing data that meets parametric assumptions, while other tests are more suitable for non-parametric data.

Choosing the right statistical test can be challenging, but understanding the basic principles can help. Different tests have different assumptions, and using the wrong one can lead to inaccurate results. To identify the appropriate test, a flow chart can be used based on three main factors: the type of dependent variable, the type of data, and whether the groups/samples are independent of dependent. It is important to know which tests are parametric and non-parametric, as well as their alternatives. For example, the chi-squared test is used to assess differences in categorical variables and is non-parametric, while Pearson’s correlation coefficient measures linear correlation between two variables and is parametric. T-tests are used to compare means between two groups, and ANOVA is used to compare means between more than two groups. Non-parametric equivalents to ANOVA include the Kruskal-Wallis analysis of ranks, the Median test, Friedman’s two-way analysis of variance, and Cochran Q test. Understanding these tests and their assumptions can help researchers choose the appropriate statistical test for their data.

Disease Rates and Their Interpretation

Disease rates are a measure of the occurrence of a disease in a population. They are used to establish causation, monitor interventions, and measure the impact of exposure on disease rates. The attributable risk is the difference in the rate of disease between the exposed and unexposed groups. It tells us what proportion of deaths in the exposed group were due to the exposure. The relative risk is the risk of an event relative to exposure. It is calculated by dividing the rate of disease in the exposed group by the rate of disease in the unexposed group. A relative risk of 1 means there is no difference between the two groups. A relative risk of <1 means that the event is less likely to occur in the exposed group, while a relative risk of >1 means that the event is more likely to occur in the exposed group. The population attributable risk is the reduction in incidence that would be observed if the population were entirely unexposed. It can be calculated by multiplying the attributable risk by the prevalence of exposure in the population. The attributable proportion is the proportion of the disease that would be eliminated in a population if its disease rate were reduced to that of the unexposed group.

The variance can be expressed as the mean of the squared differences between each value and the mean.

Measures of dispersion are used to indicate the variation of spread of a data set, often in conjunction with a measure of central tendency such as the mean of median. The range, which is the difference between the largest and smallest value, is the simplest measure of dispersion. The interquartile range, which is the difference between the 3rd and 1st quartiles, is another useful measure. Quartiles divide a data set into quarters, and the interquartile range can provide additional information about the spread of the data. However, to get a more representative idea of spread, measures such as the variance and standard deviation are needed. The variance gives an indication of how much the items in the data set vary from the mean, while the standard deviation reflects the distribution of individual scores around their mean. The standard deviation is expressed in the same units as the data set and can be used to indicate how confident we are that data points lie within a particular range. The standard error of the mean is an inferential statistic used to estimate the population mean and is a measure of the spread expected for the mean of the observations. Confidence intervals are often presented alongside sample results such as the mean value, indicating a range that is likely to contain the true value.

The relative risk is the typical measure of outcome in cohort studies. It is important to distinguish between risk and odds. For example, if 20 individuals out of 100 who take an overdose die, the risk of dying is 0.2, while the odds are 0.25 (20/80).

Measures of Effect in Clinical Studies

When conducting clinical studies, we often want to know the effect of treatments of exposures on health outcomes. Measures of effect are used in randomized controlled trials (RCTs) and include the odds ratio (of), risk ratio (RR), risk difference (RD), and number needed to treat (NNT). Dichotomous (binary) outcome data are common in clinical trials, where the outcome for each participant is one of two possibilities, such as dead of alive, of clinical improvement of no improvement.

To understand the difference between of and RR, it’s important to know the difference between risks and odds. Risk is a proportion that describes the probability of a health outcome occurring, while odds is a ratio that compares the probability of an event occurring to the probability of it not occurring. Absolute risk is the basic risk, while risk difference is the difference between the absolute risk of an event in the intervention group and the absolute risk in the control group. Relative risk is the ratio of risk in the intervention group to the risk in the control group.

The number needed to treat (NNT) is the number of patients who need to be treated for one to benefit. Odds are calculated by dividing the number of times an event happens by the number of times it does not happen. The odds ratio is the odds of an outcome given a particular exposure versus the odds of an outcome in the absence of the exposure. It is commonly used in case-control studies and can also be used in cross-sectional and cohort study designs. An odds ratio of 1 indicates no difference in risk between the two groups, while an odds ratio >1 indicates an increased risk and an odds ratio <1 indicates a reduced risk.

Measures of Central Tendency

Measures of central tendency are used in descriptive statistics to summarize the middle of typical value of a data set. There are three common measures of central tendency: the mean, median, and mode.

The median is the middle value in a data set that has been arranged in numerical order. It is not affected by outliers and is used for ordinal data. The mode is the most frequent value in a data set and is used for categorical data. The mean is calculated by adding all the values in a data set and dividing by the number of values. It is sensitive to outliers and is used for interval and ratio data.

The appropriate measure of central tendency depends on the measurement scale of the data. For nominal and categorical data, the mode is used. For ordinal data, the median of mode is used. For interval data with a normal distribution, the mean is preferable, but the median of mode can also be used. For interval data with skewed distribution, the median is used. For ratio data, the mean is preferable, but the median of mode can also be used for skewed data.

In addition to measures of central tendency, the range is also used to describe the spread of a data set. It is calculated by subtracting the smallest value from the largest value.

An asymmetric funnel plot may indicate the presence of publication bias, although this is not a definitive confirmation. The x-axis typically represents a measure of effect, such as the risk ratio of odds ratio, although other measures may also be used.

Stats Publication Bias

Publication bias refers to the tendency for studies with positive findings to be published more than studies with negative findings, leading to incomplete data sets in meta-analyses and erroneous conclusions. Graphical methods such as funnel plots, Galbraith plots, ordered forest plots, and normal quantile plots can be used to detect publication bias. Funnel plots are the most commonly used and offer an easy visual way to ensure that published literature is evenly weighted. The x-axis represents the effect size, and the y-axis represents the study size. A symmetrical, inverted funnel shape indicates that publication bias is unlikely, while an asymmetrical funnel indicates a relationship between treatment effect and study size, indicating either publication bias of small study effects.

To calculate the event rates for the medication and control groups, we divide the number of events by the total number of subjects in each group. For the medication group, the event rate is 0.2 (30/150), and for the control group, it is 0.4 (80/200).

We can also calculate the absolute risk reduction (ARR) by subtracting the event rate in the medication group from the event rate in the control group: ARR = CER – EER = 0.4 – 0.2 = 0.2.

Finally, we can use the ARR to calculate the number needed to treat (NNT), which represents the number of patients who need to be treated with the medication to prevent one additional event compared to the control group. NNT = 1/ARR = 1/0.2 = 5.

A sensitivity analysis is a tool utilized to evaluate the degree to which the outcomes of a study of systematic review are influenced by modifications in the methodology employed. It is employed to determine the resilience of the findings to uncertain judgments of assumptions regarding the data and techniques employed.

Cronbach’s Alpha is a statistical measure used to assess the internal consistency of a test of questionnaire. It is a widely used method to determine the reliability of a test by measuring the extent to which the items on the test are measuring the same construct. Cronbach’s Alpha ranges from 0 to 1, with higher values indicating greater internal consistency. A value of 0.7 of higher is generally considered acceptable for research purposes. The calculation of Cronbach’s Alpha involves comparing the variance of the total score with the variance of the individual items. It is important to note that Cronbach’s Alpha assumes that all items are measuring the same construct, and therefore, it may not be appropriate for tests that measure multiple constructs.

When investigating prognosis, the hierarchy of study designs starts with a systematic review of cohort studies, followed by a cohort study, follow-up of untreated patients from randomized controlled trials, case series, and expert opinion. The strength of evidence provided by a study depends on its ability to minimize bias and maximize attribution. The Agency for Healthcare Policy and Research hierarchy of study types is widely accepted as reliable, with systematic reviews and meta-analyses of randomized controlled trials at the top, followed by randomized controlled trials, non-randomized intervention studies, observational studies, and non-experimental studies.

The t test is limited to parametric data that follows a normal distribution. However, inadequate statistical power due to a small sample size does not necessarily invalidate the t test results. While it is likely that a small sample size may not reveal any significant differences, it is still possible that large differences may be observed regardless of prior power calculations.

The effectiveness of a healthcare intervention, usually a medication, is measured by the number needed to treat (NNT). This represents the average number of patients who must receive treatment to prevent one additional negative outcome. An NNT of 1 would indicate that all treated patients improved while none of the control patients did, which is the ideal scenario. The NNT can be calculated by taking the inverse of the absolute risk reduction. A higher NNT indicates a less effective treatment, with the range of NNT being from 1 to infinity.

The specificity of the GHQ test is 91%, meaning that 91% of individuals who do not have depression are correctly identified as such by the general practitioner using the test.