53

USMLE Step 2 (Fach) / Epidemiology & Stats (Lektion)

Diese Lektion wurde von estoffel erstellt.

Lektion lernen

zurück  |  weiter 1 / 2

• Prevention Primary prevention:- Prevent the onset of specific diseases via risk reduction by altering behaviors or exposures that can lead to disease, or by enhancing resistance to the effects of exposure to a disease agent.- Primarily decrease the incidence, and in turn, decrease the prevalence of disease.- Examples: Immunization, lifestyle modification, fortification of salt with iodine to prevent iodine deficiency, fluoridation of toothpaste, water, and salt to reduce the risk of dental caries, fortification of food with folic acid to reduce the prevalence of neural tube defects, health legislation Secondary prevention:- Early detection of disease in asymptomatic patients to promote early intervention, and to prevent further progression and complications from the disease.- Consists of a 2-step process: screening test to identify disease and a follow-up for management of the disease.- Examples: General medical examination (“checkup”), laboratory testing, other screening tests Tertiary prevention:- Optimize care of patients with an established disease to improve functionality and prevent complications.- Lower the risk of relapse.- Examples: Blood pressure management, diabetes management, lifestyle modification
• Randomized controlled trials Aim: determines the possible effect of a specific intervention on a population of interest Study method: patients are randomly allocated as either treatment or control subjects, after which they are monitored and evaluated for the outcome of interest Blinding: the practice of not informing an individual or group about which individuals are a control or treatment candidate; used to minimize bias- Single-blind study: Only researchers know who is a control or treatment candidate.- Double-blind study: Neither the researcher nor the study participants know who is a control or treatment candidate.- Triple-blind study: The researcher, the study participants, and the person who analyzes the data do not know who is a control or treatment candidate. Cluster randomized controlled trials:- Different participants are grouped together into clusters and then randomly assigned to the control or intervention groups.- A cluster RCT is easier to perform than a classical RCT, but may have less validity than a classical RCT.
• Clinical drug trials Studies involving human subjects to assess new health interventions to provide safe and effective medical care- Compares the benefits of a single treatment vs. a placebo or between 2 or more drugs Preclinical studies: - Animals- Determine the effect, dose, and side effects (teratogenic/carcinogenic potential) of the drug Phase 0:- Small number of healthy individuals- Test subtherapeutic doses of a new drug to determine preliminary pharmacokinetic and/or pharmacodynamic properties of the drug Phase I:- Small number of healthy individuals- Determine the side effects, toxicity, pharmacokinetics, and pharmacodynamics of the drug Phase II:- Small number of patients with a specific disease- Determine the efficacy, effective dosing, and side effects of the drug Phase III:- Randomized control trial with a large number of patients with a specific disease- Compare the new drug with current treatment options or placebo Phase IV:- Large number of patients with a specific disease after drug approval- Ascertain the effects of long-term therapy and effects on special patient groups (e.g., patients with chronic renal failure); can lead to withdrawal of a drug from the market
• Case-control study Aim: to study if an exposure is associated with an outcome (e.g., disease) Study method:1. Researchers begin by selecting patients with the disease (cases) and without the disease (controls) with matching baseline characteristics from the same source population.2. The observer compares the presence of risk factors between these two groups.3. The odds ratio is then determined between these groups. Example: determining the link between cervical cancer and human papillomavirus (HPV) exposure by comparing otherwise similar (e.g., same age) patients with and without histologically confirmed cervical cancer A case-control study compares a small population group over a short period of time (less cost-intensive) and determines how multiple exposures lead to one outcome; a cohort study compares a large population over a long period of time (more cost-intensive) and determines how one exposure leads to multiple outcomes!
• Selection biases Ascertainment (sampling) bias: Study population differs from target population due to nonrandom selection methods Nonresponse bias: High nonresponse rate to surveys/questionnaires can cause errors if nonreponders differ in some way from responders Berkson bias: Disease studied using only hospital-based patients may lead to results not applicable to target population Survival (a.k.a prevalence-incidence or Neyman) bias: A type of selection bias in which those observed as having a disease have either more/less severe disease than is true for all those who truly have the disease. Attrition bias: Significant loss of study participants may cause bias if those lost to follow-up differ significantly from remaining subjects
• Observational biases Recall bias: Common in retrospective studies, subjects with negative outcomes are more likely to report certain exposures than control subjects Observer bias: Observers misclassify data due to individual differences in interpretation or preconceived expectations regarding study Reporting bias: Subjects over- or under-report exposure history due to perceived social stigmatization Surveillance (detection) bias: Risk factor itself causes increased monitoring in exposed group relative to unexposed group, which increases probability of identifying a disease
• Analysis of variance (ANOVA) Calculates the statistically significant difference between ≥ 3 independent groups by comparing their means (an extension of the t-test). One-way analysis of variance:- Assesses 1 variable (e.g., the mean height of women in clinics A, B, and C at a given point in time; the variable is height)- The aim is to determine whether there is an effect of different independent variables on a dependent variable. Two-way analysis of variance: assesses 2 variables (e.g., the mean height of women and the mean height of men in clinics A, B, and C at a point in time; the variables are gender and height)
• Chi-square test Categorical test (test used to evaluate the statistically significant difference between groups with categorical variables)Categorical variable (nominal variable): A variable that has two or more categories without having any kind of order or numeric value. Examples include gender and eye color. There is no set distance (interval) between data. - Calculates the difference between the frequencies in a sample- Aims to determine how likely outcomes are to occur due to chance (used in cross-sectional studies)- E.g., the number of patients with lung disease below 20 years of age in clinics A, B, and C at a certain point in time - E.g., comparing the number of members of four different ethnicities with diabetes.
• Ecological study Aim: to identify an exposure with an outcome (eg, disease), especially if the outcome is rare Study method: assess aggregated data where at least one variable (eg, an outcome) is at a population level and not an individual level- Each unit of observation is an entire country or a large population of people. Eg, the mean, prevalence, incidence, or rate of disease. Example: determining the incidence of cholera deaths based on specific locations (eg, different parts of the city) to identify the exposure (eg, water from a single contaminated pump)
• Ascertainment bias Sampling bias Study population differs from target population due to nonrandom selection methods. Occurs when the results from an atypical population are extrapolated into the entire population.
• Anzeige
• Berkson bias Disease studied using only hospital-based patients may lead to results not applicable to target population.
• Survival (prevalence-incidence or Neyman) bias Exposures that happen long before disease assessment can cause study to miss diseased patients that die early or recover. Eg, a hospital-based study on snow shoveling and myocardial infarction will miss patients who die in their driveways before reaching the hospital.
• Attrition bias Significant loss of study participants may cause bias if those lost to follow-up differ significantly from remaining subjects.
• Reporting bias Subjects over- or under-report exposure history due to perceived social stigmatization.
• Surveillance (detection) bias Risk factor itself causes increased monitoring in exposed group relative to unexposed group, which increases probability of identifying a disease.An outcome (e.g., disease) is diagnosed more frequently in a sample group than in the general population because of increased testing and monitoring.Leads to falsely high incidence and prevalence rates Example: Subjects who receive the trial treatment are monitored more frequently- E.g., frequent detection of endometrial cancer in post-menopausal patients exposed to estrogen therapy compared to post-menopausal patients not exposed to estrogen therapy. Estrogen therapy increases the risk of bleeding, leading to more frequent screening. Solution: - Comparing to an unexposed control group with a similar likelihood of screening- Selecting an outcome that is possible in both the exposed and unexposed group
• Types of epidemiological studies Descriptive studies: Studies that try to identify individual characteristics (age, sex, occupation), place (e.g., residence, hospital), or time of events (e.g., during diagnosis, reporting) in relation to an outcome (e.g., disease).- Case report- Case series- Ecological study- Cross-sectional study Analytical studies: Studies that determine the relationship between an exposure and outcome. Always involve a comparison group.- Experimental studies→ Studies that include an intervention→ The independent variable is manipulated to determine the effect on the dependent variable.→ Randomized control trial- Observational studies→ Studies without an intervention→ The independent variable is not manipulated.→ Cohort study; Case-control study, Cross-sectional study
• Descriptive studies Case report: a report of a disease presentation, treatment, and outcome in a single subject or event- Example: report of a single case of cervical cancer in a 25-year-old female subject Case series report: a report of a disease course/response to treatment compiled by aggregating several similar patient cases- Example: collecting and reporting several cases of pericarditis at a local hospital Ecological study- Aim: to identify an exposure associated with an outcome (e.g., disease), especially if the outcome is rare- Study method: assesses aggregated data where at least one variable (e.g., an outcome) is at a population level and not an individual level- Example: determining the incidence of cholera deaths based on specific locations (e.g., different parts of a city) to identify the exposure (e.g., water from a single contaminated pump)
• Factorial study Aim: to test the effect and interactions of two or more factors (e.g., treatments) Study method: Individuals are randomly assigned to groups receiving different doses and combinations of drugs. Example: In order to study 5 dose levels of a drug X and 2 dose levels of drug Y, 10 different intervention combinations should be examined.
• Crossover study Aim: to obtain a more efficient comparison of treatments with fewer patients Study method: each patient switches from one treatment to another during the trial period and serves as their own control Example: each patient receives both drug X and drug Y, but at different time periods during the study
• Cross-sectional study (prevalence study) Aim: to determine the prevalence of exposure and disease Study method: the prevalence of disease and other variables (e.g., risk factors) are measured simultaneously at a particular point in time (i.e., a snapshot of the population) Example: investigating the number of patients with both coronary heart disease as well as hypertension in the year 1998
• Cohort study Aim: to study the incidence rate and whether the exposure is associated with the outcome of interest (e.g., a disease) Study method and examples:- Retrospective cohort study→ Starts with individuals who are either exposed or not exposed to a particular risk factor (e.g., smoking)→ A review of medical records of patients in both groups is then conducted to determine if the disease of interest (e.g., lung cancer) has developed.- Prospective cohort study→ Starts with individuals who are either exposed or not exposed to a particular risk factor (e.g., smoking)→ These two groups are then followed for a period of time to see if the disease of interest (e.g, lung cancer) develops. In cohort studies, researchers select individuals based on exposure first and then determine if these individuals develop a disease. This is in contrast to case-control studies, in which patients with disease (cases) and those without disease (controls) are selected first to determine if they were exposed or not!
• Absolute risk ∼ Incidence rate Measures the probability of acquiring disease/injury in a given study population Used in cohort studies Formula: (number of new cases) / (total individuals at risk of developing disease) = (a + c)/(a + b + c + d)
• Relative risk (RR; risk ratio) The risk of an outcome (e.g., disease) among one group compared to the risk among another group Measures how strongly a risk factor (e.g., death/injury/disease) in exposed individuals is associated with an outcome Used in cohort studies Considered statistically significant if the corresponding p-value is < 0.05 Formula: (incidence of disease in exposed group) / (incidence of disease in unexposed group) = (a/(a + b))/(c/(c + d))
• Attributable risk (AR) The proportion of cases in exposed individuals that can be attributed to the exposure Used in cohort studies Formula- Population AR: (incidence rate entire study population) - (incidence rate in unexposed group) = ((a + c)/(a + b + c + d)) - (c/(c + d))- Exposure AR: (incidence rate in exposed group) - (incidence rate in unexposed group) = a/(a + b) - c/(c + d)
• Odds ratio (OR) Compares the odds of exposure in individuals with disease/injury to those without disease/injury Used in case-control studies Rare disease assumption- Since case control studies do not track patients over time, relative risk cannot be calculated.- However, the assumption can be made that if an outcome is rare (e.g., the prevalence of a disease), the incidence of that outcome is low, and the odds ratio (OR) approximates the relative risk (RR) Formula- Odds = The probability of an event occurring divided by the probability of this event not occurring→ Odds of disease in exposed group = cases exposed/cases not exposed→ Odds of disease in unexposed group = controls exposed/controls not exposed- OR = (odds of disease in exposed group)/(odds of disease in unexposed group) = (a/c) / (b/d)→ OR = 1 means the event is equally likely in both groups.→ OR > 1 means the event is more likely to occur in the group exposed to the risk factor.→ OR < 1 means the event is less likely to occur in the group exposed to the risk factor.
• Absolute risk reduction (ARR) The difference in risk as a result of an intervention compared to the control group (e.g., risk of death) Formula: risk in intervention group – risk in control group = (c/(c + d)) – (a/(a + b))
• Number needed to treat (NNT) The number of individuals that must be treated, in a particular time period, for one person to benefit from treatment (i.e., not develop disease/injury) Formula: 1/absolute risk reduction (ARR)
• Number needed to harm (NNH) The number of individuals who need to be exposed to a certain risk factor before one person develops disease/injury Formula: 1/attributable risk (AR)
• Hazard ratio The measure of an effect of an intervention on an outcome (death/cure) over a period of timeWhile similar to the risk ratio, the risk ratio only takes into account the occurrence of the event, not the timing (like the hazard ratio). The risk ratio can only be measured at the end of the study (since it is cumulative), while the hazard ratio can be calculated at any point in time. Used in survival analysisTo determine how long it takes for an event to occur Formula (incidence of disease in exposed group)/ (incidence of disease in unexposed group) = (a/(a + b))/(c/(c + d))HR = 1: no relationshipHR > 1: the event (the outcome of interest e.g., death, cure) is more likely to occur in the exposed groupHR < 1: the event is less likely to occur in the exposed group
• Allocation bias A systematic difference in the way that participants are assigned to treatment or intervention groups E.g., assigning all female patients to one group and all male patients to another group
• Anzeige
• Recall bias Awareness of condition by subjects changes their recall of related risk factors; common in retrospective studies Example: Subjects recall a certain exposure after finding out about others with the same conditionE.g., mothers who are aware that their child suffers from fetal alcohol syndrome may be inclined to underreport a history of alcohol consumption Solution: ↓ Time to follow-up in retrospective studies (e.g., retrospective cohort studies or case-control studies)
• Response bias Study participants do not respond truthfully or accurately because of the manner in which questions are phrased (e.g., leading questions) and/or the possibility of more socially acceptable answer options; especially common in surveys
• Observer bias (Experimenter-expectancy effect or Pygmalion effect) Measurement of a variable or classification of subjects is influenced by the experimenter's knowledge or expectations
• Confirmation bias The tendency of the investigator to include only those results which support his/her hypothesis and ignore the rest
• Hawthorne effect Subject's change their behavior once they are aware that they are being observed; especially relevant for psychiatric research; difficult bias to eliminate
• Lead-time bias Lead time: the average length of time between detection of a disease and the predetermined outcomeEarly detection of disease is misinterpreted as increased survival Example:- Often discussed in the context of cancer screening- E.g., the use of CT scans over conventional x-rays results in earlier detection of a malignant tumor. However, early treatment of this tumor does not improve survival. Therefore, any apparent improvement in 5-year survival rates in patients diagnosed with CT in comparison to patients diagnosed with x-rays is the result of lead-time bias.- Lead-time bias occurs when survival times are chosen as an endpoint of screening tests Solution:- Measure the back-end survival (That means adjusting the survival according to the severity of the disease at the time of diagnosis.)- Gold standard of measuring screening test effectiveness is to use mortality rates instead of survival times
• Length-time bias A phenomenon whereby a screening test preferentially detects less aggressive forms of a disease and therefore increases the apparent survival time.Length-time bias occurs because the duration of observation is randomly selected. Example: Often discussed in the context of cancer screeningE.g.,: A slow-growing tumor which remains asymptomatic for a long period would be picked up more frequently by a screening test than a rapidly growing tumor which presents early with symptoms and death. Since slow-growing tumors also tend to be less aggressive, they are associated with improved survival. This may lead to the false conclusion that patients who are screened for the tumor survive longer than those who have not been screened when the actual duration of survival has not improved with screening. Solution: Arrange patients according to severity of disease
• Confounding Definition: any third variable that has not been considered in the study but that correlates with the exposure and the outcome Example: A confounder can be responsible for the observed relationship between the dependent and independent variables. For instance, while exposure to coal can result in lung cancer in individuals at a mining company, many miners smoke cigarettes, which acts as a third variable that can lead to lung cancer. Solution:- Perform multiple studies with different populations.- Randomization- Crossover study- Restriction (epidemiology): The researcher only studies a part of the population that meet certain criteria (e.g., only males with a particular disease are included in a study to avoid the influence of gender on exposure and outcome)- Matching (epidemiology): Cases and controls are grouped into pairs with similar attributes to avoid confounding- Standardization of data (see Z-score)- Stratified analysis: → Study groups are divided into subgroups according to the third variable. → Measures of association (e.g., the odds ratio) can be calculated for each subgroup (e.g., stratum-specific odds ratios)→ In confounding: Stratifying participants into subgroups according to the third variable will eliminate the confounder→ In effect modification: Stratifying participants into subgroups according to the third variable will result in a stronger relationship in one subgroup
• Effect modification Definition: a third variable that positively or negatively influences a study outcome; occurs when the exposure has a different effect between groups; not considered a type of bias in itself Example: a certain drug works in children, but does not have any effect on adults Solution: stratified analysis- Effect modification can be differentiated from confounding by performing a stratified analysis: When the population is stratified according to a factor, different results will be seen if it is a confounder or if it is an effect modifier.
• Sensitivity and specificity Sensitivity (true positive rate):- The proportion of individuals that correctly register as positive in a clinical test designed to identify a disease- A test with a high sensitivity will yield a low false negative rate.- A test with a high sensitivity (i.e., few false negatives) but low specificity for a disease with low prevalence will yield a high false positive rate.- Can be used for screening purposesA high sensitivity is usually achieved at the expense of specificity. If a highly sensitive test yields a negative result, the disease can most definitely be ruled out because individuals with disease will very rarely have a false negative result. Therefore, tests with a high sensitivity are used as screening tests (e.g., ELISA for HIV antibodies). A screening test does not have 100% specificity, which means a positive result could be falsely positive and a confirmatory test is necessary. Specificity (true negative rate):- The proportion of individuals without a disease that correctly test negative in a clinical test designed to identify that disease: i.e., true negative results divided by total true negative and false positive results (typically expressed as a percentage)- A highly specific test will yield a low false positive rate- Can be used to confirm the diagnosis following a positive screening testIf a highly specific test yields a positive result, the disease is most likely present because individuals without disease will almost always have a negative result in highly specific tests. Therefore, tests with a high specificity are used as confirmatory tests (e.g., Western blot assay for HIVantibodies)
• Verifying the presence or absence of a disease Screening test:- Used to identify disease in asymptomatic individuals. E.g., mammogram for breast cancer or a Pap smear for cervical cancer- Should have a high sensitivity Confirmatory test:- Confirms disease in individuals with signs or symptoms of disease. E.g., biopsy for breast cancer or cervical cancer- Usually performed after a screening test to confirm a diagnosis- Should have a high specificity
• Receiving operating characteristic curve (ROC curve) A graph that compares the sensitivity and specificity of a diagnostic test Used to show the trade-off between clinical sensitivity and specificity for every possible cutoff value to evaluate the ability of the test to adequately diagnose subjects (e.g., diseased vs. nondiseased) The y-axis represents the sensitivity (i.e., true positive rate) and the x-axis corresponds to 1 - specificity (i.e., false positive rate). - A test is considered more accurate if the curve follows the y-axis.- A test is considered less accurate if the curve is closer to the diagonal. The area under the curve also allows the usefulness of tests to be compared: The larger the area under the ROC curve, the higher the validity of the test.
• T-test Calculates the difference between the means of two samples (E.g., comparing the mean heart rate between men and women) Two sample t-test: Calculates whether the means of two groups differ from one another Unpaired t-test (independent samples t-test):- Two different groups are sampled at the same time- The difference between the means of a continuous outcome variable of these 2 groups is compared- The null hypothesis is that the mean of these two groups is equal; a statistically significant difference rejects the null hypothesis- E.g. determining the mean weight between 50 men and 50 women Paired t-test (dependent samples t-test):- The same group is sampled at two different times- The difference between the means of a continuous outcome variable of this group is compared- The null hypothesis is that the group mean is equal at these two different times; a statistically significant difference rejects the null hypothesis- E.g. measuring students' test scores before and after the completion of a module
• Normal distribution (Bell curve, Gaussian distribution) Normal distributions differ according to their mean and variance, but share the following characteristics: The same basic shape; the following assumptions about the data distribution can be made:- 68% of the data falls within 1 SD of the mean.- 95% of the data falls within 2 SD of the mean.- 99.7% of the data falls within 3 SD of the mean. Symmetry (i.e., a symmetrical bell curve) Total area under the curve = 1 All measures of central tendency are equal (mean = median = mode)
• Nonnormal distributions Bimodal distribution:- Data distribution with two peaks (a peak = mode (epidemiology)- Two subgroups within the study population Positively skewed distribution- Data set that is skewed to the right- Mean > median > mode Negatively skewed distribution- Data set that is skewed to the left- Mean < median < mode
• Non-parametric tests Definition: tests used to evaluate the statistically significant difference between groups when the sample has non-normal distribution and the sample size is small. Spearman correlation coefficient:- Calculates the relationship between two variables according to their rank- Compares ordinal level variables- Interpretation: Extreme values have a minimal effect on Spearman's coefficient. Mann-Whitney U test:- Compares ordinal, interval, or ratio scales- Calculates whether two independently chosen samples originate from the same population and have identical distributions and/or medians- E.g., compares two groups of high school students – one with an average GPA of 4.2 and the other an average GPA of 3.2 – to determine if both came from the same larger group. Wilcoxon test (rank sum and signed rank):- Rank sum test: compares the means between groups of different sizes- Signed rank test: compares the means between pairs of scores that can be matched; substitute for the one-sample t-test when a pre-intervention measure is compared with a post-treatment measure and the null hypothesis is that the treatment has no effect
• Screening HIV test: at least once in life between 13-64 years of age High blood pressure: annually in patients > 40 years Pap smear: every 3 years starting at 21 years or every 5 years at 30 years, if combined with HPV testing. Blood glucose levels: in obese individuals aged 40-70 years Lipid profile: Individuals at increased risk of cardiovascular disease aged 45-75 years Mammography: every 2 years in women between 50-74 years of age Colorectal cancer: colonoscopy every 10 years, an annual fecal occult blood test, or sigmoidoscopy every 5 years in every adult ≥ 50 years of age DEXA scan: women ≥ 65 years or postmenopausal women younger than 65 years who have at least one risk factor for osteoporotic fractures. Low-dose CT imaging of the chest: patients 55-80 years of age with a history of smoking (≥ 30 pack years) who still smoke or who have stopped in the past 15 years Pneumococcal conjugate vaccine (PCV13): immunocompetent individuals ≥ 65 years of age Shingles vaccine (administered in 2 doses, 2-6 months apart): immunocompetent adults ≥ 50 years of age, regardless of a history of shingles. Abdominal aneurysm: abdominal ultrasound one-time screening in men aged 65 to 75 years with a history of smoking
• Attributable risk percent (ARP) The percentage of incidence of disease among exposed individuals that can be attributed to the exposure Formula: ARP = (RR - 1)/RR Alternatively, ARP = AR/(incidence of disease in exposed group) * 100
• Likelihood ratio Determines the utility of a diagnostic test in clinical practice; likelihood ratio is not influenced by disease prevalence Reflects how much more likely the disease is in a person with a positive (positive likelihood ratio) or negative (negative likelihood ratio) test result compared to the pretest probability. If the likelihood ratio is > 1, then it is associated with the disease.If the ratio is < 1, then it is associated with absence of the disease.If the likelihood ratio is 1, then the post-test probability is similar to the pretest probability, and therefore the test has poor clinical utility. Positive likelihood ratio: Ratio of the sensitivity rate (true positive rate) to the false positive rateSensitivity/(1 - specificity) Negative likelihood ratio: Ratio between the false negative rate and the specificity (true negative rate)(1-sensitivity)/specificity
• Intention-to-treat analysis All patients who initially enrolled in the study (including drop-outs) are included in the analysis of study data; helps to reduce selection bias; preserves randomization

zurück  |  weiter 1 / 2