The median is the value of the observation that comes halfway when the observations are ranked in order.

A prospective cohort study assembles participants and follows them into the future. Cohort studies are observational. They evaluate the natural history of a condition. Patients are divided into specific groups based on their exposure characteristics and followed up over a while to determine if the outcome or disease of interest develops. The distractor here is a case-control study.

Case-control studies are retrospective. They look at two groups at the start: one with the outcome or disease and one without the outcome or disease. They look retrospectively (back in the past) to assess if there was a significant difference in the rates of exposure to a defined risk factor between the groups.

CLINICAL STUDIES Case series and case reports.

These are reports of cases and use no control groups or patient groups to compare outcomes. They are similar to writing a report on a topic that you have read. Case-control study. This is a study in which patients who already have a specific condition are compared with people who do not have that particular condition. These study designs are observational, meaning that researchers study but do not alter what occurs.

They look retrospectively (back in the past) to assess if there was a significant difference in the rates of exposure to a defined risk factor between the groups. For example, group A consists of patients with lung cancer, and group B consists of patients without lung cancer. Information is collected by looking back at their smoking history. The hypothesis would be that smoking would incur a higher risk of lung cancer.

Cohort study- These studies identify a group of people who share a defining characteristic. This group is then compared with a similar group of people with the same defining characteristic. Cohort studies are purely observational and may be prospective (going forward in time) or retrospective (happening in the past). Because these studies are observational, the researchers (like in case-control studies) do not intervene with the groups of people. Example of a prospective cohort study, group A are patients who smoke, group B are patients who do not smoke. They are followed in time to determine who develops lung cancer. What is the difference between a case-control study and a retrospective cohort study.

In case-control, one starts with the outcomes and studies the exposure. In retrospective cohort studies, one already has determined the exposure, but now needs to study the association of exposure to disease outcome.

Randomized controlled clinical trial- These are experiments that introduce a treatment or exposure to study its effect on real patients. It usually compares an active group with a control group and employs methods such as randomization and blinding to reduce bias.

Typically, new drugs are tested this way.

For example, a group of patients with disease X who meet specific criteria, are randomly allocated to receive either the experimental treatment or the control treatment (either the standard treatment for disease X or a placebo). Both groups are then followed in time, and the outcomes are observed.

Cross-sectional study- This is a study that describes the relationship between diseases and other factors at one point in time in a defined population.

They are often used for comparing diagnostic tests. A good example would be researchers performing a survey on a population to determine the relationship between increased levels of serum cholesterol and electrocardiographic evidence of coronary artery disease. Both the exposure and the disease outcome are taken at the same point in time. It is like a snapshot of a population at a specific time.

Systemic review- This focuses on a clinical topic and answers a specific question that arises.

Meta-analysis- This is a study that examines several studies on a topic and mathematically combines the results to report the results as if it were one large study.

For a test to be a screening test, it has to be cheap, and it has to have high sensitivity. As described in this stem, there are many false positives for the newly developed cervical smear screen (only 100 out of the 1000 women were seen to have premalignant changes on histology). This is in keeping with a test that has low specificity.

The easiest way to remember this is using the following definitions.

True positive is the number of sick people correctly identified as sick False positives are the amount of healthy people incorrectly identified as sick The true negative is that healthy people correctly identified as healthy The false negative is that sick people are incorrectly identified as healthy High sensitivity means few false negatives Low sensitivity means many false negatives High specificity means few false positives Low specificity means many false positives.

Mnemonic: Sensitivity – Look at false Negatives Specificity – Look at false Positives.

Absolute risk by definition is the percentage of people within a particular group affected by a certain event. Therefore, for both sets of patients who got treatment and those who did not, the absolute risk of death (the event) would be: 10/100 = 1/10 = 10%.

Incidence in epidemiology is a measure of the probability of occurrence of a given medical condition in a population within a specified period. As the question specifically asked for annual incidence per million, the calculation should be as below:

1000 + 400 = 1400 250 000 + 250 000 = 500 000

There are 1400 people diagnosed with lung cancer in a population of 500,000 in 5 years.

1 000 000 / 500 000 = 2 Incidence per million in 5 years = 2 x 1400 = 2800 Incidence per million in 1 year = 2800 / 5 = 560.

Standard deviation is a measure of the spread or dispersion of a set of observations, calculated as the average difference from the mean value in the sample.

Risk ratios are an important topic in EXAM as it is one of the main epidemiologic questions asked. Absolute risk (AR) of a disease is your risk of developing the disease over some time. The same absolute risk can be expressed in different ways. For example, say you have a 1 in 10 risk of developing a certain disease in your life.

This can also be said to be a 10% risk or a 0.1 risk – depending on whether you use percentages or decimals.

Relative risk (RR) is used to compare the risk in two different groups of people.

For example, consider the risk for blindness in a patient with diabetes over 5 years. If the risk for blindness is 4 in 100 (4%) in a group of patients treated conventionally (control group) and 3 in 100 (3%) in patients treated with a new drug (experimental group),

the relative risk is the ratio of the two risks. Given the data above, the relative risk is: 3% ÷ 4% = 75% (or 0.75) Expressed as a relative difference, the new drug reduces the risk of blindness by a quarter.

An RR of < 1 means the event is less likely to occur in the experimental group than in the control group. An RR of > 1 means the event is more likely to occur in the experimental group than in the control group.

A good formula to remember is: RR = risk of disease in exposed / risk of disease in unexposed In this scenario,

it would be: RR = risk of blindness in diabetics taking new drug/risk of blindness in diabetics with no drug.

Sensitivity is a measure of a test’s ability to correctly detect people with the disease. It is the proportion of diseased cases that are correctly identified by the test. It is calculated as follows: Sensitivity = Number with disease who have a positive test/Number with disease.

The definition of incidence is the number of new cases of a disease occurring within a population during a specific time. Therefore, in this question, 950,000 people (number of new cases) developed myocardial infarction within a population during a specific time period.

Please see Q NO- 3

In this question, we must figure out the total amount of people affected with lung cancer within the 5-year period first:

1250+500=1750. Therefore, annually there are 1750/5 = 350 deaths from lung cancer.

This is based on the assumption that the 1 year survival rate is 0% therefore all cases should be accounted for.

Since 350 deaths/year is from a population of 500,000, to find out the annual prevalence per million would be

(350 x 1,000,000)/500,000 = 700 (cross multiply).

A falsely drawn negative conclusion. A conclusion that a person does not have the disease or condition being tested, when they actually do.

For some questions such as this, it is necessary to select an answer based on the process of elimination. Failure of the active drug is incorrect seeing as how a more significant percentage of the placebo group withdrew. Insufficient information to interpret data occurs when researchers do not have sufficient results to analyze the study.

Chance even in this context means that a specific cause cannot explain the people’s withdrawal

i.e. the dropout was by chance. The issue with this statement is the massive discrepancy in dropouts of the placebo group compared to the experimental group. An acceptable dropout rate between groups is considered to be less than 15%. Here we see a dropout rate difference of 22%. Furthermore, we would expect a dropout rate to be small given that the study is only for one year.

A study of 10 years may have a dropout rate difference of 20% to 25% which is acceptable because patients are often lost to follow-up as it is such a lengthy study. Dropouts not related to systemic bias could occur due to an unrelated illness, death, or job relocation. That being said, a study that only lasts for one year should have dropout rates that are near zero for it to be considered a chance event.

An initial randomization error is a possibility although the stem gives no information as to how the patients were selected. If methodologies were not appropriate, it could lead to bias and compromise the study.

The best answer is that there is a breakdown of the double-blind study. A breakdown in the study can occur if people from either group find out that they are in the experimental group or the control group. Patients in the placebo group may have found that they have been taking placebo medication and hence decided to leave.

This would result in a very high percentage of people in the placebo group leaving which is precisely what we see in this case.

Randomised control trials tend to introduce a treatment, in this case, drug A which is compared with a placebo in another group. Both groups are followed in time and their outcomes are recorded. The distractor here is a prospective cohort study. Prospective cohort studies are observational.

This means that the researchers would not interfere by giving drugs. They would only observe the risk factors of the two groups and look at the outcome.

Randomized control trials tend to introduce a treatment, in this case, it would be a water softener, which is compared with a placebo in another group. Both groups are followed in time,

and their outcomes are recorded. Cohort studies, cross-sectional studies, and case-controlled studies are purely observational as opposed to randomized control trials where experimental manipulation is involved.

Single-blinded trials mean either ONLY the researchers or ONLY the patients are blind to the allocation. In this case, it would only be the researchers since the patients would know that they are in the experimental or control group based on the handyman’s visit to the house to install the water softener.

It is first important to understand the meaning of absolute risk. Absolute risk (AR) is the number of events (in this case, developing a myocardial infarction) in the treated or control groups,

divided by the number of people in that group.

ARC = the AR of events in the control group = 100/1000 = 10%

ART = the AR of events in the treatment group = 50/1000 = 5% Absolute risk reduction

(ARR) = ARC-ART = 10%-5% = 5% or 0.05

Sensitivity and specificity calculations are very commonly asked. Below you will find a table and a formula to memorize and understand for the exam.

Disease present:

Test positive - A Test negative - C Disease absent: Test positive - B Test negative - D Sensitivity = a/(a+c) Sensitivity measures the proportion of actual positives that are correctly identified as such.

SPECIFICITY AND SENSITIVITY CALCULATIONS:

Disease present: Test positive - A Test negative - C Disease absent: Test positive - B Test negative - D Sensitivity = a/(a+c) Specificity = d/(b+d) Positive predictive value = a/(a+b) Negative predictive value = d/(c+d) Accuracy = a+d/(a+b+c+d)

Ans. The given key is D. relative risk = 2. [RR (relative risk)= Number of deaths in not treated group/number of deaths in the treated group].

The key is C. False negative

Ans. The key is C. Cohort study. [A cohort is a group of people who share a common characteristic or experience within a defined period (e.g., are born, are exposed to a drug or vaccine or pollutant, or undergo a certain medical procedure].

The key is D. 950000 in 250000 million