Ghassan Fraij EBM Board Review 6/8/2009

Epidemiologic Measures ► Measures of disease occurrence:  Risk (the likelihood that and individual will contract a disease)  Prevalence (the amount of disease already present in a population)  Incidence Rate (how fast new occurrences of disease arise) In addition, these measures can be used to assess the prognosis and mortality of patients with disease.

Risk ► Is the proportion of unaffected individuals who on average will contract the disease of interest over a specific period of time. ► Is estimated by observing a particular population for a defined period of time-the risk period  R = New Cases/Persons at Risk= A/N

Prevalence ► Is the proportion of a population that has the disease of interest at a particular time, i.e., on a given day. ► Is calculated by dividing the number of existing affected individuals or cases (C) by the number of persons in the population (N):  P = C/N

Incidence Rate (IR) ► Measures the rapidity with which newly diagnosed disease develops ► To estimate the IR, one observes a population, counts the number of new cases of disease in that population (A), and measures the net time called person-time (PT), that individuals in the population at risk for developing disease are observed.  IR = A/PT

Epidemiologic Measures ► Risks are most useful if interest centers on the probability that an individual will become ill over a specific period time ► Incidence rates are presented if interest centers on the rapidity with which new cases arise ► Prevalence is preferred if interest centers on the number of existing cases or the proportion of cases of a given type

Diagnostic Testing

Sensitivity ► Defined as the percentage of persons with the disease of interest who have positive test results = True-post/(True-post + False-neg) x 100 The greater the sensitivity of a test, the more likely that the test will detect persons with the disease of interest Tests with great sensitivity are useful clinically to rule out a disease Not Affected by the prevalence of disease

Specificity ► Is defined as the percentage of persons without the disease of interest who have negative test results = True-neg/(True-neg + False-post) X 100 The greater the specificity the more likely persons without the disease of interest will be excluded by the test If the test is highly specific, a positive test result would strongly implicate the disease of interest.

Diagnostic Testing ► Sensitivity and Specificity are descriptors of the ACCURACY of a test. They are not affected by the prevalence of the disease

Diagnostic Testing ► Two measures that directly address the estimation of PROBABILITY of disease are the positive predictive value (PPV) and the negative predictive value (NPV)

Positive Predictive Value ► Is defined as the percentage of persons with positive test results who actually have the disease of interest ► It allows one to estimate how likely it is that the disease of interest is present if the test is positive = True-post/(True-post + False-post) X 100 Hence it is the percentage of persons with positive test results who have the disease

Negative Predictive Value ► Is defined as the percentage of persons with negative results who do not have the disease of interest = True-neg / (True-neg + False-neg) X 100

Receiver operating characteristic curves ► ROC curves allow one to identify the cut-off value that minimizes both false positives and false negatives. ► Plots sensitivity on the y axis and 1 - specificity on the x axis (True positive rate vs. False positive rate) ► Applying a variety of cutoff values to the same reference population allows one to generate the curve. ► For the vast majority of cases, as one moves from left to right on the ROC curve the sensitivity increases while the specificity decreases

Cutoff Point ► For multilevel or continuous outcome test results, a dividing line or cutoff point can be chosen to separate findings considered to be positive or negative ► The choice of a cutoff value affects the sensitivity and specificity of a test, and consequently the PPV and NPV as well.

Cutoff Point ► Raising the threshold for considering a result to be positive typically will lead to gain in specificity (fewer false-positives) but a loss of sensitivity (more false-negatives or missed cases) ► On the other hand, lowering the threshold for considering a result to be positive typically will reduce the level of false-negatives (raise sensitivity) and increase the likelihood of false- positives (lower specificity)

Diagnostic Testing ► Is often used for screening of disease, interpretation of a test however is subject to bias. ► Lead-time bias is an increase in survival as measured from disease detection until death, without lengthening of life. ► Length-biased sampling occurs when disease is detected by a screening program is less aggressive than disease detected without screening ► To assess the true benefit of a screening program, it is useful to measure disease-specific mortality rates within the entire population

Likelihood Ratios (LR) ► For any given test, each specific result value has its own LR. It can be argued that there is no such thing as a positive or negative test result; tests only make you either more or less certain of disease than you were before the test ► The LR is the numerical value for how much more or less certain you are of disease after obtaining a specific test result

Likelihood Ratio (LR) ► If the LR is > 1.0, then you are more certain of disease than you were before the test was done. If the LR is 1.0, then you are more certain of disease than you were before the test was done. If the LR is < 1.0m then you are less certain of diseased than you were before the test. ► LR represents a measure of the odds of having a disease relative to the prior probability of the disease. The estimate is independent of the disease prevalence, which often is unknown to us.

Likelihood Ratio ► A LR of 1 is the only test result which is completely unhelpful to you; all other values will either make more or less certain of disease than you were before you testes for it ► A strong positive LR is typically 10 or greater ► A strong negative LR is typically 0.10 or lower

LR Calculation ► A positive likelihood ratio is calculated by dividing sensitivity by 1 minus the specificity  Sensitivity/(1-Specificity) NOT AFFECTED BY PREVALENCE

LR Calculation ► A negative likelihood ratio is calculated by dividing 1 minus sensitivity by specificity = (1-sensitivity)/specificty NOT AFFECTED BY PREVALENCE

LR Calculation ► So how can we utilize these ratios in clinical practice ► Remember the LR is specific to a certain value testing for a certain disease ► Hence for a particular patient in time with a particular affliction, you must assess that patients pre-test probability of having the disease ► This is done by using the prevalence of the disease as the pre-test probability

LR Calculation ► You take the prevalence of disease and use it as your pre-test probability. ► You then convert the pre-test probability to odds (Don’t confuse with the odds ratio) and multiply that by the positive LR or negative LR depending on what you are looking for ► This gives you the post-test odd which you then convert to a post-test probability ► The post-test probability is also the PPV or NPV of the test

Clinical Trials ► The first step in performing a clinical trial is to formulate the major research question. This question is usually referred to as an hypothesis. ► The parameter that is measured to answer the most important question of the clinical trial is the primary end point

Clinical Trials ► Hypothesis are started in the null form (Ho)- i.e. there is no difference between the treatment groups regarding the specific end point ► If the observed date are not consistent with the null hypothesis, then the null hypothesis is rejected in favor of the alternate hypothesis (Ha)

Sample Size Determination ► The number of subjects to be enrolled in a clinical trial must be determined at the same time as the primary research end point ► A clinical trial can be considered a sample of the “truth” ► Using a sample of a population, one hopes to make a valid inferences about the entire population, but since one can evaluate only a sample, the risk of mistaken conclusions exists

Type I and Type II errors Study Results TreatmentsDifferTreatment Do Not Differ TreatmentsDifferCorrect(True-Positive) Type I Error (False-Positive) Treatments Do Not Differ Type II error (False-Negative)Correct(True-Negative)

Type I Error ► If a study finds a difference in treatment when actually there is no difference, then type I error has occurred ► Under this circumstance, the study results are falsely positive

Type I and Type II errors Study Results Treatments Differ Treatment Do Not Differ Treatments Differ Correct (True-Positive) Type I Error (False-Positive) Treatments Do Not Differ Type II error (False-Negative) Correct (True-Negative)

Type II Error ► If a study fails to find a difference in treatments when in actuality there is a difference, a Type II error is said to have occurred ► Under this circumstance, the study results are falsely negative.

Type I and Type II errors Study Results TreatmentsDifferTreatment Do Not Differ TreatmentsDifferCorrect(True-Positive) Type I Error (False-Positive) Treatments Do Not Differ Type II error (False-Negative)Correct(True-Negative)

Errors ► Falsely positive or falsely negative studies can occur because of faulty methodology, chance occurrences, or both ► While methodological error can be minimized by careful attention to study design, errors due to chance can never be completely eliminated

Alpha Level ► The notation used to denote the likelihood of a Type I error-that the observed difference between groups is not a true difference but is due instead to chance ► The Alpha Level is specified commonly as 0.05, which means that the investigator is willing to accept a 5% risk of committing a type I error (falsely concluding that the groups differ when in reality they do not)

P-Value ► Measured probability of a finding occurring by chance alone given that the null hypothesis is actually true. ► For a study to be valid, P-value would have to be less than the pre-specified Alpha Value

Beta Level ► The notation used to describe the likelihood of a Type II error- that the study did not find a difference when there actually is one ► Investigators often specify beforehand the Beta Level of their trial ► Often a level of 0.20 for Beta is considered adequate- in other words, a 1 in 5 chance of missing a true difference between the groups is allowed

Statistical Power ► The ability of a study to detect a true difference between groups is measured by subtracting Beta from 1 (1-Beta) ► Statistical power for a study with a beta level of 0.20 would be 0.80, or 80% ► Such a study would have 80% chance of detecting a specified difference in outcome between the treatment groups

Statistical Power ► A power calculation may be performed prior to conducting a study to ensure that there are a significant number of observations to detect a desired degree of difference ► The larger the difference, the fewer the number of observations that will be required to achieve a certain level of power

Analysis of Results ► Expressing outcomes of clinical trials can be done by comparing the incidence rate (IR) of a defined outcome between the standard and therapy group ► This can be done by expressing the outcome as a percentage rate reduction, or relative risk

Percentage Rate Reduction (PRR) ► PRR = IR (stand)-IR (experim) /IR (stand) ► If the percentage rate reduction is 0, there is no reduction in the incidence rate attributable to the new therapy, and the treatments are judged to be equivalent ► The further the percentage rate reduction is from zero, the greater the difference is between the two groups

Point Estimate/Confidence Intervals ► A specific percentage rate reduction calculated is called a point estimate. ► Because it is the single value along the scale from 0 to 100% that is most consistent with the results of the trial ► A useful method to gauge the precision of any point estimate is to calculate the 95% confidence intervals for the estimate

Confidence Intervals ► Give an idea of how likely the sample mean represents the population mean ► The calculation of a confidence interval considers the standard deviation of the data and the number of observations ► Thus, a confidence interval narrows as the number of observation increase, or its variance (dispersion) decreases

Confidence Intervals ► If a clinical trial was to be repeated many times, the values falling between the upper and lower bounds of the 95% confidence interval would include the true point estimate 95% of the time ► If the 95% CI of the percentage risk reduction includes ZERO, the data are consistent with null hypothesis and the difference between the groups is not statistically significant at the alpha level of 0.05 ► If the 95% CI does not include 0, the difference is statistically significant at the alpha level of 0.05

Relative Risk ► Another method of comparing two rates in a study is to form a ratio of the perspective incidence rates, the so-called rate ratio ► Rate Ratio = IR(experimental)/IR(standard) ► If the RR = 1.0, the rate (or risk) of the outcome of interest in the two treatments groups is exactly equal

Relative Risk ► The further the ratio is from 1.0, the greater the difference in rate (or risk) between the two groups ► If the 95% CI includes the null values of 1.0, there is no statistical difference between the rates in the two groups, and the null hypothesis would be accepted

Therapy Papers ► Terms Involved:  EER  CER  RR  RRR  ARR  NNT

Event Rate ► Experimental Event Rate (EER)  Event rate in treated group  a/n1 or a/(a+b) ► Control Event Rate (CER)  Event rate in control group  c/n0 or c/(c+d)

Absolute risk ► Risk of having a disease ► If the incidence of a disease is 1 in 1000, then the absolute risk is 1 in 1000 or 0.1%.

Relative risk ► Event rate in treatment group divided by the event rate in the control group. ► RR (aka Risk Ratio) is used in randomized trials and cohort studies. ► RR = EER/CER ► RR can be calculated from studies in which the proportion of patients exposed and unexposed to a risk is known.  i.e. a cohort study, in which a group of patients who have variable exposure to a risk factor of interest are followed over time for an outcome

Absolute Risk Reduction ► ARR = CER-EER ► Difference in the event rate between the control group (CER) and treatment group (EER). ► It reflects the additional incidence of disease related to an exposure taking into account the background rate of the disease.

Relative Risk Reduction ► RRR=CER -EER / CER ► Percent reduction in events from treated group compared to control group

Number Needed to Treat ► NNT is the reciprocal of the absolute risk reduction ► The number of patients who need to be treated to prevent one bad outcome

Note on Harm ► In harm papers similar calculations apply  Absolute Risk Increase (ARI)  Relative Risk Increase (RRI)  Number Needed to Harm (NNH)

Randomized Controlled Trial (RCT) ► A true experiment, in which the researcher randomly assigns some patients to at least one maneuver (treatment) and other patients to a placebo, or usual treatment ► Key feature = the classic way to evaluate effectiveness of drugs ► Prospective

RCT - ITT ► In an intent-to-treat analysis patients would be analyzed according to the groups for which they were originally assigned. ► Done to avoid the effects of crossover and dropout, which may break the randomization ► Intention to treat analysis provides information about the potential effects of treatment policy rather than on the potential effects of specific treatment

Cohort Study ► A longitudinal study that begins with the gathering of two groups of patients (the cohorts), one which received the exposure of interest, and one which did not, and then following this group over time (prospective) to measure the development of different outcomes (diseases) ► Comparison of risk (smokers vs. nonsmokers and risk of lung cancer)

Cohort Study ► Good way to determine risk relating to exposure to a harmful substance. ► Cannot exclude unknown confounders, blinding is difficult, and identifying a matched control group may also be difficult

Case-control study ► This type of research begins by identifying patients with the outcome (disease) of interest and looks backward (retrospective) to see if they had the exposure of interests ► Cases, people who have the outcome (disease) in question, are linked with controls, people from the same population without the outcome (disease) ► Pts with lung cancer versus those without it assessing for tobacco exposure

Case Control Study ► The classic study design for the initial investigation of cause-effect relationships ► Odds ratios (but not of absolute risks). ► A case-control study cannot be used to prove cause-effect relationships ► The case-control study is ideal for rare diseases or disease that takes many years to develop

Cross-sectional study ► Prevalence study. Survey of an entire population for the presence or absence of a disease and/or other variable in every member (or a representative sample) and the potential risk factors at a particular point in time or time interval ► Exposure and outcome are determined simultaneously ► Cannot establish causation, subject to bias

Community Trial ► An entire community receives a treatment or preventative measure to determine if it works in the "real world".

Crossover Study Design ► The administration of two or more experimental therapies one after the other to the same group of patients

Case Series ► A collection of anecdotes (patients with an outcome of interest). ► No control group is involved.

Case Report ► An anecdote. To get into the medical literature, a case report typically must convey the message "man bites dog."

References ► Greenberg, Raymond, et al. Medical Epidemiology. Second Edition, Lange Medical Book, 1996 ► Sackett, David, et al. Clinical Epidemiology: A Basic Science For Clinical Medicine. First Edition, Little Brown and Company, 1985.