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TYPES OF EPIDEMOLOGICAL STUDIES

There are two main objectives for epidemiological studies; descriptive and analytic. Descriptive epidemiology deals with rates, ratios and distributions, it explains the determinants of the disease in the form of time place and person. Analytical epidemiological tests consist of observational studies and experimental studies. Observational studies include Case-Control, Cohort and cross-sectional studies.

CASE CONTROL STUDY

The movement is from the effect to the disease. The researcher begins with a population with a certain outcome, and subjects are classified

into either "cases" or "controls" based on the outcome status.

The cases and controls are assessed retrospectively to for the presence of risk factor (Information is collected about exposure to risk factors).

Is very popular in exploring an exposure - disease association.

Selection of control subjects based on exposure status (exposed diseased or even none exposed non diseased) is inappropriate because

comparing the frequency of exposure between the case and control groups is an important part of case-control study.

Optimal selection of control group is to provide an accurate estimation of exposure frequency among non-diseased general population (both

exposed and non-exposed).

Independent variables (age, sex) are often selected to be the same (matched) between the case and control groups to decrease the effect of

confounding. Subjects with the disease of interest (case group) are compared with an otherwise similar group that is disease free (control

group).

It is retrospective study aiming at determining the association between risk factors and disease occurrence.

The main measure of association is exposure Odds ratio can be calculated in the case control study but incidence of the disease can't. One of

the drawbacks of case control study is that the risk can’t be derived directly from its results. It is more cheap and easy than cohort study.

Incidence measures (e.g. relative risk or relative rate) can't be directly measured in case-control study, because the people being studied are

those who have already developed the disease.

Relative risk and Relative rate are calculated in cohort studies, where people are followed over time for the occurrence of the disease.

Prevalence odds ratio is calculated in cross-sectional studies to compare the prevalence of the disease in different populations.

COHORT STUDY (PROPSPPECTIVE OR LONGITUDINAL)

Divides the study group into "exposed" and "none exposed" to the risk factors. Each subject is then following prospectively till the presence of

the disease. It is a prospective observational study in which groups are chosen based upon the presence or absence of one or more risk factors. All

subjects are then observed over time for the development of the disease of interest. Thus allowing estimation of the incidence within the total population and comparison of incidences between subgroups.

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It is best for determining the incidence of the disease & comparing the incidence of the disease in 2 populations, (One with and one

without a given risk) allows for calculation of a relative risk. It is stronger than case-control study and cross sectional study. Loss to follow-up in a prospective studies creates a potential for selection bias (selective loss of high risk or low risk subjects). E.g. if a substantial number of subjects are lost to follow-up in exposed and/or unexposed groups, It is possible that the lost subjects differ in their risk of developing the outcome from the remaining, Such loss may result in either overestimation or underestimation of the association between exposure and the disease. Example: if 30% of subjects were lost to follow-up in a prospective study for the relation of alcohol and breast cancer, There is no information available on whether these subjects develop breast cancer or not. The number (30%) is substantial and will influence the outcome if heterogeneity in developing breast cancer exists between the lost

subjects and the remaining subjects. For example if the subjects lost in the exposed group experienced more breast cancer than those with follow-up (selective loss of high

risk subjects). As a result, the measure of association might be underestimated. To reduce the potential for selection bias in prospective studies, investigators try to achieve high rates of follow-up Median survival: used to compare the median survival times in two or more groups of patients (e.g. receiving new treatment or placebo). Median survival is calculated in cohort study or clinical studies. Prevalence odds ratio is calculated in cross-sectional studies to compare the prevalence of the disease between two different peoples.

PREVALENCE

IT is the measure of those with the disease in the population at a particular point in time.

The relation between them in a stable population (little migration) can be demonstrated by: Prevalence = (incidence) × (time).

So if the incidence is fixed in a stable population, the prevalence is increased if there are factors, that prolong survival (i.e. disease duration) e.g. improved quality of care.

Prevalence of disease in a population = incidence of the disease / population.

INCIDENCE:

It is the frequency of new cases of a disease arising in a population at risk over a specified time period. It is the measure of the appearance of new

cases.

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COHORT STUDY (Retrospective)

Cohort= a group of individuals.

Starts at some point between the exposure and the outcome.

The researcher reviews the past records and classify subjects into "exposed" and "non-exposed" and then follow them until the outcome.

In a cohort study, the study subjects are free of the outcome at the time a study begins.

CROSS SECTIONAL STUDY (Prevalence study)

Both the exposure and the outcome are studied at one point of time (at one cross section of time). Since both exposure and outcome are present for some time before the study, it is not possible to determine the temporal association

between the exposure and outcome from cross-sectional study. Takes a sample of individual from a population at one point in time. It allows determination of a disease prevalence (the total number of cases in a population at a given time). Disease incidence can't be determined

CASE SERIES

A study involving only patients already diagnosed with the condition of interest It is helpful in determining the natural history of uncommon conditions. But provides no information about the disease incidence.

CLINICAL TRIAL

Compare the therapeutic benefit of different interventions in patient already diagnosed with a particular disease. Usually subjects are randomly arranged into exposed (treatment group) & placebo and then followed to detect the development of the

outcome of interest. Can't be used to determine disease incidence.

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RANDOMIZED CONTROL TRIALS

Type of experimental study. It is considered as the gold standard for studying the efficacy of a treatment or a procedure. Compare two or more treatments. Subjects are randomly assigned to an experimental (experienced a specific exposure e.g. medication) and a control group (non-exposed i.e.

placebo). This type of study has the least bias and helps to show a strong causal relationship.

CROSS OVER STUDY

In which a group of participants is randomized to one treatment for a period of time and the other group is given an alternate treatment for the same period of time (interchanging the treatment), with a washout (no treatment) period in between the treatment intervals to limit the confounding effect of the prior treatment.

At the end of the time period, the two groups then switch treatment for another set period of time.

PARALLEL GROUP STUDY

Randomizes one treatment to one group and another treatment to the other group. Such as treatment drug to one group versus a placebo to the other group. There are usually no other variables are measured.

EFFECT MODIFICATION

Occurs when the effect a main exposure on an outcome is modified by another variable. It is not a bias. It is a natural phenomenon that should be described not corrected as it is not a bias or confoundation. Example: the effect of oral contraceptives on breast cancer is modified by the family history i.e. women with +ve family history have an

increased risk, while women without +ve family history don't have an increased risk. Other examples: studying the effect of estrogen on the risk of venous thrombosis (modified by smoking). Also studying of the risk of lung cancer in people exposed to asbestos (greatly depends on / modified by smoking). For example, the effect of a new estrogen receptors agonist drug on the incidence of DVT is modified by smoking status: Smokers taking the drug have an increased risk of developing DVT, while nonsmokers taking the drug don't. It may be confused with confounding; both can be differentiated by dividing the whole cohort into subgroups (stratified analysis). Imagine that smoking is a confounding that, by itself is associated with a higher risk of DVT, so if more smokers are taking the drug, it might

appear that the drug causes DVT, but when stratified analysis is performed by analyzing smokers and nonsmokers separately, it will appear that the drug is no longer associated with DVT.

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LATENT PERIOD

It is a time period required for an exposure to start the effect I.e. the time require from getting exposed to outcome. In infectious diseases it is relatively short, while in chronic diseases (e.g. cancer or CAD), It may be very long and extended period of exposure may be required to affect the outcome. Latent period also can be applied to the exposure to risk modifier, as it may need to be continuous over a certain period of time before

influencing the outcome. Latent period is a natural phenomenon not a bias.

OUTLIER (extreme observation)

It is defined as an extreme and unusual observation in a dataset. It may be the result of a recording error, a measurement error or a natural phenomenon. It affects the measures of central tendency as well as measures of dispersion for example: The mean: is extremely sensitive to the outliers and easily shifts towards them. The standard deviation is sensitive to outliers because it is the measure of dispersion within the data set, and outliers significantly increase the

dispersion (SD = deviation of values around the mean). The range = maximum value - minimal value (so it is definitely changed). The mode is not changed by outliers as they don’t change the most frequent value observed. The median is much more resistant to the outliers as is located in the middle of the dataset where the observations usually don’t differ much

from each other.

Absolute Risk Reduction (ARR)

RR = event rate for the drug or test i.e. = +ve cases/ total number examined by the test or drug In case of 2 drugs or interventions study one drug reduce the relative risk (RR) than the other. Absolute risk Reduction (ARR) = RR of first drug (placebo) - RR of second drug (under test). Number needed to treat (NNT): is the number of people that should receive a treatment to prevent one defined event. Is calculated by inverse the absolute risk reduction. NNT = 1/ARR. The power of a study is the ability to detect a difference between two groups (treated versus none treated, exposed versus none exposed). Increasing the sample size --> increases the power of the study and consequently makes the confidence interval of the point of estimate (e.g.

relative risk) tighter. If the sample size is small --> low power of study to detect the difference between exposed and non-exposed subjects & this makes the

confidence interval of the study wide (e.g. 0.8-3.1) and makes the study statistically insignificant. And if we increase the sample size --> the confidence interval will be tighter and the study will be statistically significant. Relative risk reduction (RRR) = ARR (control group) - ARR (treatment group)/ ARR (control group).

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RELATIVE RISK (RR):

Is used as a measure for association in a cohort studies. It is the ratio of the risk in an exposed group to that of the unexposed group. The NULL value of RR is 1.0. A RR of 1 means that there is no association between the risk factor and the disease. A relative risk > 1 means that there is a positive association between the risk factor and the outcome. A relative risk < 1 means that there is a negative association between the risk factor and the association. The further the value of the RR from 1, the stronger the association. Example: the RR of bronchogenic cancer in smokers is greater than 2 --> indicates, A strong association between smoking (risk factor) and bronchogenic carcinoma (outcome). When exposure is measured on a continuous scale (Number of smoked cigarettes per day or PPD), The classification into two or more ordinal categories enable the risk to be assessed as a function of exposure. And the DOSE RESPONCE EFFECT can be calculated from the exposure and the outcome. The present example illustrates a dose response relationship between smoking and bronchogenic cancer, (The RR of bronchogenic lung cancer increases as the number of smoked PPD increases). One weakness of the RR is that it gives no clue whether such finding can be explained by chance alone. The confidence interval and the "P" value can help strengthen the finding of the study. For the study to be statistically significant:

1- The confidence interval must not contain null value (1). 2- The "p" value should be less than 0.05 (i.e. < 5% chance the result obtained were due to chance alone). 3- The RR is not Null value (1).

The "p" value is used to strengthen the results of the study; it is defined as the probability of obtaining the result by chance alone. e.g. "P"

value is 0.01 means that (the probability of obtaining the result by chance alone is 1%). The commonly accepted upper limit (cut-off point) of the "P" value for the study to be considered statistically significant is 0.05 (i.e. less than

5%). The "P" value deals with random variability, not bias. If the "P" value less than 0.05 (i.e. the study is statistically significant), the 95% confidence interval doesn't contain 1.0 (the null value for RR).

A relative risk of 0.71 shows that the drug decreased the risk of mortality by 29% (the null value for RR is 1). e.g.: A case of RR 1.6 (greater than 1) & the confidence interval 1.02-2.15 (doesn't contain the null value 1), so for the study to be statistically significant the "P" value must be less than 0.05.

N.B: Very important to know how to calculate relative risk from the 2×2 table: Relative risk = {a/(a+b)}/{c/(c+d)}

Number needed to harm (NNH): It is the number of people that must be treated for one adverse event to occur (similar to number needed to treat). NNH = 1/ Attributable risk. Attributable risk = Adverse event rate (treatment group) - Adverse event rate (control group). Adverse event rate = Number of deaths / total number of the group. For example: drug X (deaths=60 & living=20) placebo drug (deaths=38 & living=38). Adverse event rate in treatment group = 60/80= 0.75. Adverse event rate in placebo group = 38/76= 0.50. Attributable risk = 0.75 - 0.50 = 0.25. NNH 1/0.25 = 4.

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TYPES OF BIAS SAMPLE DISTORTION BIAS Due to a nonrandom sampling of a population. It can lead to a study population having characteristics that differ from the target population. A common example; is that severely ill patients are most likely to enroll in cancer trials leading to, results that are not applicable to patients with less advanced cancer i.e. the study sample isn't representative of the target population with respect to the joint distribution of exposure and outcome.

BERKSONS BIAS It is a selection bias that can be created by selecting a hospitalized patients as the control group.

LATE LOOK BIAS: Individual with sever disease less likely to be uncovered in a survey cause die early.

SELECTION BIAS Results from the manner in which the subjects are selected for the study, from the selective losses from the follow-up.

INFORMATION BIAS Occurs due to imperfect assessment of the association between the exposure and outcome. As a result of errors in the measurements of exposure and outcome status. It can be minimized by using standardized techniques for surveillance and measurement of outcomes as well as trained observers to measure the exposure and outcome.

MEASUREMENT BIAS Occurs from poor data collection with inaccurate results.

LEAD-TIME BIAS Lead-time bias should be considered while evaluating any screening test. It happens when two interventions are compared to diagnose a disease and one of them diagnose the disease earlier than the other without an effect on the outcome (survival). What actually happens is that detection of the disease was made at an earlier point of time But the disease course itself or the prognosis did not change So the screened patients appeared to live longer from the time of diagnosis till the time of death. IN USMLE: Think of LEAD BIAS when you see “a new screening test" for poor prognosis diseases like lung cancer or pancreatic cancer.

OBSERVER'S BIAS, MEASUREMENT BIAS & ASCERTAIN BIAS: When the observer maybe influenced by prior knowledge or details of the study that can affect the results. Refers to misclassification of an outcome and /or exposure. e.g.: labeling diseased subjects as non-diseased and vice versa. Blinded studies usually avoid this bias by preventing the observer from knowing which treatment or intervention the participants are receiving. Blinding can involve patients exclusively or both patients and physicians (double blinding). And are related to the design of the study (the scenario will describe how the study was designed).

DEECTION BIAS: Refers to the fact that a risk factor itself may lead to extensive diagnostic investigations and increase the probability that a disease is identified. For example: patients who smoke may undergo increased imaging surveillance due to their smoking status, which would detect more cases of cancer in general.

RESPONDENT BIAS: Occurs when the outcome of the test is obtained by the patient's response not by objective diagnostic methods (e.g. migraine headache). SUSCEPTABILITY BIAS: Is a type of selection bias where a treatment regimen is selected for a patient based on the severity of their condition, without taking into account other possible confounding variables? Offline case 20.

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TYPES OF BIAS RECALL BIAS: Occurs when a study participant is affected by prior knowledge to answer a question. Result from inaccurate recall of past exposure by people in the study and applies mostly to retrospective studies as case-control study. People who have suffered an adverse event (such as having a child with congenital anomalies) are more likely to recall previous risk factors than people who have not experienced a poor outcome. This is more common in case-control studies than in randomized clinical trials.

REFERRAK BIAS or admission rate: Occur when the case and control populations differ due to admission or referral practices. For example: a study involving cancer risk factors performed at a hospital specialized in cancer research may enroll cases referred from all over the nation, however hospitalized control subjects without cancer may come from only the local area.

ALLOCATION BIAS: It may result from the way that treatment and control groups are assembled. It may occur if the subjects are assigned to the study groups of a clinical trial in a non-random fashion. For example in a study group comparing oral NSAIDs and intra-articular corticosteroid injections for the treatment of osteoarthritis, obese patients may be preferentially assigned to the corticosteroid group (affect the outcome).

CONFOUNDING: Occurs when at least part of the exposure-disease relationship can be explained by another variable (confounding). Due to presence of one or more variables associated independently with both the exposure and the outcome. For example: cigarette smoking can be a confounding factor in studying the association between maternal alcohol drinking and low birth

weight babies. As cigarette smoking is independently associated with alcohol consumption and low birth weight babies.

Beta error:

Refer to a conclusion that there is no difference between the groups studied when a difference truly existing. It is a random error not a systemic error (i.e. bias).

Hawthorne effect: It is the tendency of a study population to affect the outcome because these people are aware that they are being studied. This awareness leads to consequent change in behavior while under observation --> seriously affecting the validity of the study. It is usually seen in studies that concern behavioral outcomes or outcomes that can be influenced by behavioral changes. In order to minimize the Hawthorne effect, the studied subjects can be kept unaware that they are being studied.

Pygmalion Effect: It describes researcher's beliefs in the efficacy of treatment that can potentially affect the outcome. N.B. all bias are considered as a threat to the validity of a study.

HOW TO CONTROL BIAS: 1- Selection bias can be controlled by choosing a representative sample of the population for the study & achieving a high rate of follow up. 2- Observer's bias can be controlled by blinding technique. 3- Ascertainment bias can be controlled by selecting a strict protocol of case ascertainment. 4- Confounders: can be avoided by 3 methods in the design stage of the study; matching restriction and randomization. 5- Matching is used in case control study in which select variables that could be confounders (age, race,) then cases and controls are selected

based on the matching variables. 6- Randomization is commonly employed in clinical trials its purpose is to balance various factors (confounders) that can

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Influence the estimate of association between the treatment and placebo groups so that the uncompounded effect of the exposure can be isolated. A very important advantage of randomization when compared to other methods is the possibility to control the known risk factors (as; Age, severity of the disease) as well as unknown & difficult to measure confounders a (level of stress, socioeconomic status) and make all confounders evenly distributed between the treatment group and the placebo. In clinical trials, randomization is said to be successful, when there is similarity in the distribution of the baseline characteristics (age, race, prevalence...) between the treatment and placebo groups i.e. the confounders are evenly distributed between the treatment and the placebo groups.

HAZARD RATIO:

It is the ratio of the chance of an event occurring in the treatment arm (drug or group of interest), Compared to the chance of that event occurring in the control arm (the other drug or group) during a set period of time. Hazard ratio = event occurring in the test group / event occurring in the control group. So; the lower the hazard ratio, the less likely the event will occur in the treatment arm. The higher the ratio, the more likely the event will occur in the treatment arm. A ratio close to 1 indicates no significant difference between the 2 groups, Example: Hazard ratio of 2 drugs A & B in bleeding complications: Hazard ratio for major bleeding = 0.93 i.e. close to 1 means that both groups are similar to each other’s in this event. Hazard ratio for intracranial bleeding = 0.41 (indicates the lower chance of drug "A" to cause intracranial bleeding than drug "B"). Hazard ratio for GIT bleeding = 1.50 (indicates that drug "A" has a higher chance to cause GIT than drug "B"). Hazard ratio for life threatening bleeding = 0.80 (indicates the lower chance of drug "A" to cause intracranial bleeding than drug "B"). Hazard ratio for total bleeding = 0.91 (indicates the slight lower chance of drug "A" to cause intracranial bleeding than drug "B"). In case number (11 offline) you should focus on the baseline value in the case in take the corresponding hazard ratio in the study then Decide which one of them has the greater hazard of hyperkalemia (N.B. Ca channel blockers affects GFR). You should learn case 19 in offline 2013.

SUCCESSFUL RANDOMIZATION: In any randomized clinical study, the goal of successful randomization is:

1- To eliminate bias in treatment assignments. 2- Blind the investigators from the identity of the patients who receive the treatment arm. 3- Minimize the confounding variables.

Ideal randomization allows for adequate statistical power and should include: 1- Equal patient group sizes. 2- Low selection bias. 3- Low probability of confounding variables.

A listing of the base line characteristics of the patients in each arm would demonstrate, if the two arms had patients with similar characteristics and would insure the proper randomization occurred in the study

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Two SAMPLE "T test"

It is commonly used to compare two means not proportions. The basic requirements needed to perform this test are: The two mean values - the sample variances - the sample size. T test" is then done to obtain the "P" value. If the "P" value is less than 0.005 --> the null hypothesis (that there is no difference between the two groups) is rejected, and the two means

are assumed to be statistically different. If the "P" value is large --> the Null hypothesis is retained.

TWO SAMPLE "Z test"

Also can be used to compare two means, but Population (not sample) variances are employed in the calculations. Because the population variances are not usually known --> this test has limited applicability.

ANOVA test (Analysis of variances)

Used to compare two or more means (determine whether there are significant differences between the means of 2 or more independent groups. E.g. ANOVA can be used to assess for difference in mean blood pressure among three samples of populations, grouped by exercise status (never exercise, exercise occasionally and exercise frequently).

Chi Square test:

Used to test the association between two categorical variables. By compare proportions (of categorized outcome, e.g. high or low) then presented with the exposure (present or not present). A 2×2 table may be used (high or low outcome) and (exposed & non-exposed) to compare the observed values to the expected values. If the difference between the observed and expected values is large, this means there is association between the exposure and the outcome. E.g. it is used to determine if the distribution of gender and smoking status is random or if there is difference between the sexes regarding smoking status.

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META-ANALYSIS: It is an epidemiologic method for pooling of the data from several studies to do an analysis having a relatively big statistical power. e.g.: individual studies assessing the effects of aspirin on certain cardiovascular events may be inconclusive, However analysis of data compiled from multiple clinical trials may revealed a significant benefit.

Pearson correlation coefficient: It is a measure of the strength and direction of a linear relationship between 2 variables. For example, a study may report a correlation coefficient describing the association between hemoglobin A1C level and average blood glucose level.

Multiple linear regression: It is a method used to model the linear relationship between a dependent variable and 2 or more non-dependent variables. E.g. this test could be used to quantify the effects of alcohol use, tobacco smoking and charred food consumption on the incidence of gastric ulcer.

FACTORIAL DESIGN STUDY: Involves two or more experimental interventions, each with two or more variables that are studied independently. For example: A study uses 3 different interventions beta blocker (Metoprolol), calc. channel blocker (Amlodipine) or ACEIs (Ramipril) with two different variable BP. endpoints (102-107 mmHg or < 92 mmHg).

Patient Randomization: 1) ACEIs: - Higher BP goal - Lower BP goal. 2) Beta blocker: - Higher BP goal - Lower BP goal. 3) Ca channel blocker: - Higher BP goal - Lower BP goal.

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IN CASE OF NORMAL DISTRIBUTION

The normal distribution is symmetrical and bell shaped. All measures of central tendency are equal i.e. mean = median = mode. The degree of dispersion from the mean is determined by the standard deviation. 68% of data --> within 1 Standard deviation from the mean (mean +/- 1 SD). 95% of data --> within 2 standard deviation from the mean (mean +/- 2 SD). 99.7% of data --> within 3 standard deviation from the mean (mean +/- 3 SD). In contrary to normal distribution curve, most of data in real world statistical analysis have asymmetrical distributions:

Positive skewed curve Smaller numbers predominate in the dataset. The long slop of the curve "the tail" extends in the positive direction. The mean is the most shifted to the positive direction followed be the median then the mode. So the mean is greater than the median. In strongly skewed distributions, the median is a better measure for central tendency than the mean.

Negative skewed curve

Larger numbers predominate in the dataset. The long slop of the curve "the tail" extends in the negative direction. The mean is the most shifted to the negative direction followed by the median then the mode. So the mode > the median > the mean (i.e. the mean is the smallest). In strongly skewed distributions, the median is a better measure for central tendency than the mean.

SENSITIVITY

Sensitivity --> the proportion of true +ve cases among all diseased cases (Sensitivity = true +ve by the test/all patients that are actually

diseased). Indicates the ability of a test to detect those patient with disease. A higher sensitivity --> the higher the test detect patient with the disease --> decrease false negatives. Screening tests (especially for diseases with severe squally) should have a high sensitivity.

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SPECIFICITY

Specificity --> the proportion of true -ve cases among all non-diseased cases (Specificity = true -ve by the test/all patients that are actually

free). Is a measure of the true negative rate and indicates how will a test can rule out a given condition (exclude those without the disease). The higher the specificity the more likely that most healthy patients will have a -ve test results. The higher the specificity --> the less likely the false +ves. They are fixed values that are not vary with the pre-test probability of a disease or with the prevalence of the disease. The ideal diagnostic test should have high sensitivity and specificity.

N.B. Raising the cutoff point of a diagnostic test --> decrease it's sensitivity but increase its specificity. Lowering the cutoff point of a diagnostic test --> increase its sensitivity but decrease it's specificity.

Exposure Odds ratio:

Draw the 2×2 table (a,b,c,d) It is the measure of association in case control study. It compares the odds of exposure in cases to the odds of exposure in control. OR = (a×d)/(b×c). It is not the same as relative risk. RR can be calculated in follow up studies by comparing the risk of exposed individuals to the risk of unexposed individuals. RR =

[a/(a+b)]/[c/(c=d)]. Direct calculation of RR in case-control study is not possible, because the study design doesn't include following peoples overtime. But sometimes the RR can be approximately equal to the odd's ratio. If the prevalence of the disease is low --> the odds ratio approximates the Relative risk (RR). This is called (the rare disease assumption). Increasing the sample size will decrease the "P" value of the odds ratio and make the confidence interval tighter. Attributed risk percent (ARP): represents the excess risk in a population that can be attributed to the exposure to a particular risk factor. It can be calculated by subtracting the risk in the unexposed population (baseline risk) from the risk from the exposed population and dividing

the results by the risk in the exposed population. ARP = (Risk in exposed - Risk in none exposed)/Risk in exposed. Or ARP can be calculated from the relative risk as follow: ARP = (RR-1)/RR Pre and post-test Probabilities (+ve predictive value (PPV) & -ve predictive value:

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A. Positive predictive value (PPV) test:

Describes the probability of having the disease if the test result is +ve, (if the patient has a +ve test result, what is the likelihood that he actually has a disease).

The post-test probability of having the disease is directly related to the PPV. If the PPV is 25% i.e. low, consequently if the test result is positive, then the post-test probability of having the disease is low. The post-test probability is also dependent on the sensitivity, specificity and pre-test probability of having the disease.

B. Negative predictive value (NPV) test: Describes the probability of not having the disease if the test result is -ve. NPV will vary with the pre-test probability of a disease (important) i.e.: A patient with high probability of having a disease will have a low NPV. And a patient with a low probability of having a disease will have a high NPV. If the NPV is 96 % this means that if the test result is -ve, the chances of the patient to not have the disease is high (96%). And the chances of the patient to have the disease is low (100 - 96 = 4%). Example: BREAST CANCER & FNA test results: A patient of a high pre-test probability for having the disease (1st degree relative having breast cancer or age > 40 ys), has a low NPV. A patient of a low pre-test probability for having breast cancer (less than 40 ys old), has a high NPV. HIV & ELISA test results: A patient who belongs to a high risk group e.g. (multiple sexual partners, use no condoms, IV drug abuse) --> has a high pre-test probability of having AIDS --> so he will have a low NPV. On the other hand a patient who belongs to a low risk group (one sexual partner, using condom and no IV drug abuse) --> has a low pre-test probability of having AIDS --> so has a high NPV.

NOTE

The prevalence of the disease is directly related to the pre-test probability of having the disease (PPV) & inversely related to the pre-test probability of not having the disease (NPV), so increased prevalence --> low NPV but high PPV and vice versa. Sensitivity and specificity are not affected by the prevalence of the disease and so the likelihood ratio positive i.e. sensitivity (1-specificity), as it depends on sensitivity and specificity.

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N.B NOTE

The prevalence of the disease is directly related to the pre-test probability of having the disease (PPV) & inversely related to the pre-test probability of not having the disease (NPV), so increased prevalence --> low NPV but high PPV and vice versa. Sensitivity and specificity are not affected by the prevalence of the disease and so the likelihood ratio positive i.e. sensitivity (1-specificity), as it depends on sensitivity and specificity.

If the test result is -ve, the probability of the patient to have the disease = 1 - NPV. Cases and diagnostic tests that are high yield USMLE questions in probabilities: Coronary artery disease and ECG stress test. Pulmonary embolism and ventilation-perfusion scanning. Prostate cancer and serum PSA level.

VALIDITY OF TEST = Accuracy

Represents the appropriateness of the test (i.e. the test ability to measures what is supposed to be measured). In order to determine the validity of a test, the results are compared to those obtained from the gold standard test. It doesn't depend on the pre-test probability of the disease.

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Receiver Operating Characteristic (ROC) curve: It emphasizes the importance of choosing the appropriate cutoff value, although overlapping of normal & abnormal results makes it difficult. Any cutoff point demonstrates a trade-off between SENSITIVITY and 1-SPECIFICITY. Sensitivity (positivity in disease) --> is the proportion of subjects who have the target condition and gives positive results. Sensitivity = TP/ (TP + FN).CLINICALLY Specificity (Negativity in health) --> is the proportion of subjects without the target condition and gives negative results. Specificity = TN/ (TN + FP).CLINICALLY ++ Sensitivity --> ++ true +ve & -- false -ve (diagnosed as normal but he is diseased). ++ Sensitivity --> allow not to miss any diseased patient (not to miss any true +ve). ++ Specificity --> ++ true -ve & -- false +ve (diagnosed as diseased but he is normal). ROC --> Aiming at decrease false -ve and false +ve results (i.e. increase sensitivity and specificity). N.B.: In ROC curve: sensitivity = true positive while (1-specificity) = false positive. Positive predictive value (PPV) --> is the probability of having the disease if the test results are +ve. PPV = TP/(TP + FP). Negative predictive value (NPV) --> is the probability of not having the disease if the test result is -ve. NPV = TN/(TN + FN). Positive likelihood ratio (LR+) = sensitivity/(1-specificity). (LR+) --> is the ratio of the proportion of patients who have the target condition & test positive to, The proportion of patients without the target condition & who also test positive. Negative likelihood ratio (LR-) = (1-specificity)/sensitivity. (LR-) --> is the ratio of the proportion of patients who have the target condition who test negative to the proportion of patients without the target condition who also test negative. ROC curve has 2 lines; vertical line (Y) for sensitivity and horizontal line (X) for specificity Large Y values --> Indicates High sensitivity. Small X values --> Indicates High specificity. Low cutoff --> Increase sensitivity (better ability to identify patients with the disease i.e. increase true positive), Although this causes decrease specificity (the test falsely identifies more subjects as diseased also they are not) and vice versa. High cutoff --> Decrease sensitivity and Increase specificity. Low cutoff --> High Sensitivity --> higher negative predictive value (NPV) --> decrease false -ve results (Ruling out probability). High cutoff --> Higher Specificity --> higher positive predictive value (PPV) --> decrease false +ve results (Ruling in probability).

RELIABILITY: Test-retest reliability. A reliable test is reproducible; gives similar or very close results on repeat measurements. Reliability is quantified in terms of Coefficient of variation (CV). Coefficient of variation; is the standard deviation of the set of repeated measurements divided by their mean & expressed as a percentage. Reliability is maximal when random error is minimal.

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N.B Draw the overlap curve: A shift of the ROC curve upwards for a given cutoff indicates increased sensitivity and vice versa. A shift of the curve to the right for a given cutoff (higher value) indicates decreased sensitivity and vice versa. The curve usually shows that an increase in sensitivity is offset by decrease in specificity. As mentioned before sensitivity= TP/ (TP+FN) & specificity= TN/ (TN+FP), so decreased overlap between the healthy and diseased population curves --> --> Decrease both the number of FP & FN (i.e. decreases the dominator) --> thus increase both sensitivity and specificity (i.e. allow for a test with both higher sensitivity and specificity. In overlap curve: moving the cutoff value to the right (higher value) would increase specificity at the expense of sensitivity, while moving the cutoff to the left (lower value) would increase sensitivity at the expense of specificity. A cutoff value just outside the overlapping portion would maximize the sensitivity (if to the left) or specificity (if to the right) at 100%. Both sensitivity and specificity depend on the cutoff value of a given test for example: Raising the cutoff value makes it more difficult to diagnose the condition i.e. it makes it harder to obtain +ve results and easier to obtain -ve results --> this will increase specificity but decrease sensitivity. Lowering the cutoff value makes it easier to obtain +ve results and harder to obtain -ve results, i.e. increase sensitivity and decrease specificity. Increase sensitivity --> increase -ve predictive value (NPV) due to (decrease false -ve results). Increase specificity --> increase +ve predictive value (PPV) due to (decrease false +ve results).

ACCURACY Is the proportion of the true results (true +ve and true -ve) out of all results that are predicted by the test. The closer the plotted curve approaches the left and top borders of the ROC curve, the more accurate the test. Accuracy can also be measured by the total area under the plotted curve on ROC curve. Increase of the total area under the curve --> increases the accuracy of the test.

PRECISION

Is the proportion of the true +ve results out of the total number of the true results of the test (-ve results are not taken into account). Precision is equivalent to +ve predictive value i.e. true +ve/all true. It is the measure of the random error in the study. The study is precise if the results are not scattered widely, this is reflected by a tight confidence interval. So, if the first study has a wider confidence interval than the second study --> the second study is more précised.

N.B

Both accuracy and precision depend upon sensitivity and specificity of the test as well as the prevalence of the condition in the population tested. Validity and accuracy are measures of systematic errors (bias). Accuracy is reduced if the sample doesn't reflect the true value of the parameter measured. Increasing the sample size --> increases the precision of the study, but doesn't affect the accuracy.

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CORRELATION COEFFEICIENT (r)

It assesses a linear relationship between two variables. The null value for the correlation coefficient is 0 (no association). And the range of plausible values is from -1 to 1. The sign (mark) of correlation coefficient indicates a positive or negative association. The closer the value to its margins (-1 or 1), the stronger the association. The correlation coefficient shows the strength of association but does not necessarily imply causality (cause of it). The association is statistically significant if P value is low.

RISKS: It measures the incidence of the disease. It is calculated by divide the number of diseased subjects by the number of people at risk or of interest. No of diseased/people at risk. Prevalence of disease in a population = incidence of the disease / population.

MEASURES OF CENTERAL TENDENCY: Mean --> is the sum of observations divided by the number of observations. Mean (X') = E X/N. i.e. = sum of obs./ N. of obs. Median --> is the middle observation in a series of observations after arranging them in an ascending or descending manner. If number of observations is odd --> Median = (n+1)/2. If the number of observations is even --> Median = n/2 Mode --> is the most frequent occurring value in the data. EXAMPLE: 5,6,7,5,10,3 Mean = (5+6+7+5+10+3)/5 = 36/6 = 6. Mode --> 5. EX2: 5,6,8,9,11. Median = (5+1)/2 = 3. So Median is the 3rd observation --> median = 8. EX3: 5,6,8,9 Median = 4/2 = 2. So median is the 2nd observation. Median will be the mean of observations 2&3 --> (6+8)/2 = 7. N.B Range: is a measure of variation (dispersion). Range: is the difference between the largest and the smallest values Range = largest value - smallest value e.g. Range = 9-5 = 4.

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N.B.:

Average: it is the summation of the total number of observations divided by the sample size. E.g. In random sample of children the number of episodes of UTIs are as follow (50 child (0), 30 child (1), 10 child (2), 10 child (3)). The average number of UTIs episodes per year in a child is; The number of UTIs episodes per years is: (50×0) + (30×1) + (10×2) + (10×3) = 80 UTIs episode per year. The average number of UTIs episodes per year in a child = 80/100 = 0.8 (between 0 and 1) i.e. the child experiences less than one attack of UTIs per/Yr.

Confidence interval (CI): A 95% confidence interval is the range of values in which we can be 95% confident that the true mean of the underlying population falls in. In order to calculate the confidence interval we need to know the (mean, SD, Z- score and sample size). Standard error of the mean (SEM): is calculated using the formula SEM = SD//n. Notice that the sample size (n) is a part of the calculation. Thus the confidence interval (CI) will tighten as the sample size increases. The next step is to multiply the SEM with the corresponding z-score. For 95% CI, the Z-score is 1.96 (for 99% CI the Z-score is 2.58). The final step is to obtain the confidence limit as shown: Mean +_ 1.96*SD//n.

SCATTER PLOTS: They are useful for crude analysis of data. They can demonstrate the type of association (linear or nonlinear). If a linear association is present, the correlation coefficient can be calculated. The association is positive (if the outcome increases with the increase in the exposure) +ve correlation coefficient while the association is negative (if outcome decreases with the increase in exposure) -> -ve correlation coefficient. The correlation coefficient in an almost perfect linear association is close to 1. Crude analysis of association using the scatter plots doesn't account for possible confounders.

N.B

1- It is very important to consider the natural history of a disease when evaluating the effectiveness of a drugs in a trial e.g. common cold --> natural resolution within one week should be taken in consideration while evaluating, an anti-viral drug used in treatment of common cold. 2- It is difficult to comment on a drug’s effectiveness, unless a comparison is made with the control group and statistical significance is made to know the power of the study.

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NULL HYPOTHESIS AND ALTERNATIVE HYPOTHESIS:

NULL HYPOTHESIS: It is always the statement of NO relationship between the exposure and the outcome. To state the null hypothesis correctly you should recognize the study design first. In cross-sectional study: the 2 variables (CRP & cancer colon) are studied at the same point of time so the temporal relationship between the 2 variable can't be evaluated. So you can't measure the relationship between the 2 variables --> Null hypothesis is better considered.

ALTERNATIVE HTPOTHESIS: It Opposes the Null hypothesis. It States that there is a relationship between the exposure and the outcome. It is better for studies in which a relationship between the 2 variables is existing to consider the Alternative hypothesis.

GENERALIZABILITY or EXTERNAL VALIDITY OF A STUDY: It is the applicability of the obtained results beyond the cohort that was studied. External validity answer the question "how the generalize are the results of a study to other populations. For example: if the cohort is restricted to middle aged women, the results of the study are applicable only to middle aged women & not applicable to elderly men.

Very high yield:

1- Smoking cessation the single most effective preventive intervention in almost every patient or (most effective modifier of mortality including aspirin and tight glucose control) in nearly every disease. 2- How to calculate: Sensitivity = true +ve by the test / (true +ve + false -ve) all patients that are actually diseased. True positive = sensitivity × (true +ve + false -ve) i.e. (N. of patients actually with the disease). True negative = (1- sensitivity) × (true +ve + false -ve) i.e. (N. of patients actually with the disease). Specificity = true -ve by the test/ (true -ve + false +ve) all patients that are actually free. True negative = specificity × (true -ve + false +ve) i.e. all patients that are actually free.

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STATESTICAL POWER Type I error: α error It is s the probability of rejecting the null hypothesis when it is truly false i.e. it is the probability of finding a true relationship (the probability of seeing difference when there is one truly existing). So if the researchers need to find a difference between a tested drug and the standard of care if exists, they need to maximize the power (1-B). Power depends on sample size and the difference in outcome between the 2 groups being tested. So it occurs when the researchers reject the null hypothesis when the null hypothesis is really true, (they say there is difference when actually there is no difference i.e. the study finds a statistically significant difference between 2 groups when it is actually not existing. An example: If a study concluded that hard candy improves heart failure mortality, when it doesn't. Alpha (a): is the maximum probability of making type I error a researcher is willing to accept. It corresponds with the 'P" value or the probability of making a type I error. The (a) is typically set at P= 0.05, meaning that the researchers accept a 5% possibility that the difference perceived as true is actually due to chance. N.B.: in a,b,c,d table: type I erorr = b/(b+d). type II erorr = c (a+c). Type II error: β error

Occurs when the researchers fail to reject the null hypothesis when the null hypothesis is really false, (they say there is no difference when actually there is (one) difference). It causes the investigators to miss true relationships. An example: a study finding that doesn't affect platelet function when, in fact it does. Beta (β): is the probability of committing a type II error. If (β) is set at 0.2 (20%) i.e. there will be a 20% chance to accept the null hypothesis when it is false --> the power (1-B) will be 0.8 (80 %) i.e. there will be an 80% chance of rejecting the null hypothesis when it is truly false.

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BASIC 4 PAYMENT METHODS BETWEEN PHYSICIANS AND INSURANCE COMPANIES 1) CAPICITATION: Physicians are paid fixed amount of money per enrollee, not per service (i.e. paid by capitation). So they have incentives to contain (decrease) costs per enrollee due to the fixed budget allocated for them. If many enrollees seek care or there are enrollees need extensive care, physician’s costs may be greater than their payments. So physicians are motivated to provide more preventive care to catch illness early so patients stay healthier and need fewer tests and procedures as they age. 2) FREE FOR SERVICE (FFS): Physicians are paid fixed amount of money for every service and diagnostic test they provide. They face little financial risk and they enticed (tend to) increase the number of service they provide on each visit as well as the number of visit per each patient. There is no incentive to avoid costly tests or procedures. 3) DISCOUNT FREE FOR SERVICE: Discounted FFS works similarly to FFS except that physicians are reimbursed (repay) a discounted amount. So physicians paid under this model may be more conservative when ordering tests and providing services compared to those paid by FFS especially if expensive tests or services are greatly discounted. 4) SALARY: Physicians are paid a fixed amount and their pay is not tied to number of enrollees or services rendered (provided). Unless their contracts include withholds or bonuses, salaried physicians face no financial risk. So they have no financial incentive to change their treatment patterns, either in service provided or number of follow up visits. Capitation is often used in health maintenance organization insurance plans. FFS and discount FFS are commonly used in preferred provider organization insurance plans.

A 20 year old boy is arrested for setting fire at his college. His parents report that their son is “mentally weak” and demand that he should not be punished. His colleagues have seen him setting fire at other occasions, and loving it. One friend reports that he killed a wild cat while they were on a trip, while his teachers often find him offensive. From the way, this person talks, you noticed that the he does not regret what he did. What is the most likely diagnosis? A. Conduct disorder B. Antisocial personality disorder C. Bipolar disorder D. Schizophrenia E. Pyromania Answer: The correct answer is E Pyromania, characterized by deliberate fire setting on more than one occasion. There is anxiety before the act and release of anxiety after it. It is more common in people who are moderately retarded mentally. They may have a history of cruelty to animals and lack remorse for the consequences of their actions. They also often show resentment towards authority figures e.g. teachers. Antisocial personality disorder and conduct disorder are the differentials but the characteristic fire setting behavior make pyromania more likely. For more clinical cases for USMLE step 2 CK, click here to visit our page.

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N.B

A state with a population of 4,000,000 contains 20,000 people who have disease A, a fatal neurodegenerative condition. There are 7,000 new cases of the disease a year and 1000 deaths attributable to disease A. there are 40,000 deaths per year from all causes, what is the ....?? 1- Incidence of the disease: is the number of new cases of a disease per year divided by population at risk. Incidence = 7000 / (4,000,000 - 20,000). 2- The disease specific mortality: is the number of deaths attributable to the disease per year divided by the total population. The disease specific mortality = 1000/4,000,000. 3- The rate of increase of a disease: is the number of new cases per year minus the number of deaths (or cures) per year divided by the total population. The rate of increase of a disease = (7000-1000)/4,000,000. 4- The prevalence of a disease: is the number of persons with the disease divided by the total population at a specific point of time. The prevalence of a disease = 20,000 / 4,000,000. 5- The mortality rate: is the number of deaths per year divided by the total population. The mortality rate = 40,000 / 4,000,000.

When you see it as a graph 1- An increase in lung cancer incidence and mortality has been observed in women over the last four decades due to increased cigarette smoking. 2- Breast cancer is the most common non skin cancer among women in USA, but breast cancer mortality is comparatively low, 3- Mortality from breast cancer has stayed relatively stable overtime, whereas colon cancer mortality decreased somewhat over the last decades. 4- Stomach cancer is now uncommon, so its incidence and mortality have been drastically decreased in the last decades. 5- Mortality of ovarian cancer is stable over time. 6- A part from skin cancer, the most common women cancer are ordered in descending according to incidence: Breast cancer, Lung cancer then colon cancer. 7- In order of mortality: Lung cancer followed by Breast cancer then colon cancer.

Case-Fatality rate: is calculated by dividing the fatal cases by the total number of people with the disease. Case-fatality = Number of fatal cases/total number of people with the disease. If events are independent, the probability that all events will turn out the same (e.g. -ve) is the product of the separate probabilities for each

event. The probability of at least 1 event turning out differently is given as: 1- (the probability of all events being the same).

Example

A new serological test for detecting prostate cancer is negative in 95% of patients who don’t have the disease, if the test is used on 8 blood

samples taken from patients without prostate cancer, what is the probability of getting at least 1 positive test. In this case a 0.95 (95%) probability of giving a true negative result and 0.05 (5%) probability of giving false positive result. To calculate the chance of all 8 tests being negative: probability (all negative) = (0.95). You have to know that the total probability is always equal to 1.0 (100%).so The probability that at least 1 test turns out positive is: Probability (at least 1 positive) = 1-probility (all negative) = 1- (0.95)

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