epidemiology in medicine sandra rodriguez internal medicine ttuhsc

Epidemiology in MedicineEpidemiology in Medicine

Sandra RodriguezSandra Rodriguez

Internal MedicineInternal Medicine

TTUHSCTTUHSC

Epidemiology and ResearchEpidemiology and Research

Study of the frequency and cause of Study of the frequency and cause of disease in human populations. disease in human populations. Research allows understanding of risk Research allows understanding of risk factors, progression of diseases, treatment factors, progression of diseases, treatment effectiveness, outcomes and highlights effectiveness, outcomes and highlights research needs.research needs.After critical evaluation of data, physicians After critical evaluation of data, physicians can make clinical decisions and improve can make clinical decisions and improve populations health (EBM). populations health (EBM).

Types of Statistical StudiesTypes of Statistical StudiesDescriptiveDescriptive– Take data, arrange them, and present them to Take data, arrange them, and present them to

demonstrate associations or to generate a demonstrate associations or to generate a research hypothesis for further studiesresearch hypothesis for further studies

AnalyticAnalytic– To compare exposures to risk factors with To compare exposures to risk factors with

disease states, allow hypothesis testing and disease states, allow hypothesis testing and statistical analysisstatistical analysis

– Could be without or with interventionCould be without or with intervention

Types of Statistical StudiesTypes of Statistical Studies

DescriptiveDescriptive– Case Reports and SeriesCase Reports and Series– Correlation studiesCorrelation studies

Large sample size to identify associations between Large sample size to identify associations between disease and variablesdisease and variables

Best to generate research ideasBest to generate research ideas

– Cross-Sectional StudiesCross-Sectional StudiesEvaluates a group at one point in timeEvaluates a group at one point in time

Causal links can be speculated but no conclusions Causal links can be speculated but no conclusions can be drawn.can be drawn.


AnalyticAnalytic– Case controlCase control

Study group analyzed to identify associationsStudy group analyzed to identify associations

Begins with a population and it’s disease and can Begins with a population and it’s disease and can be evaluated retrospective to determine exposurebe evaluated retrospective to determine exposure

– Prospective Cohort StudyProspective Cohort StudyDisease-free subjects are followed overtime to Disease-free subjects are followed overtime to identify onset of disease, or incidenceidentify onset of disease, or incidence

Used to establish relative riskUsed to establish relative risk


AnalyticAnalytic– Meta-AnalysisMeta-Analysis

Quantitative analysis of two or more independent studies into Quantitative analysis of two or more independent studies into a large one for analysis of variables and results. Gives a a large one for analysis of variables and results. Gives a statistic summary and is used to increase knowledge beyond statistic summary and is used to increase knowledge beyond one study, guide diagnosis and treatments and point toward one study, guide diagnosis and treatments and point toward research.research.

– Interventional studyInterventional studyClinical trialClinical trial

Ideally randomized, blind, designed to minimize impact of Ideally randomized, blind, designed to minimize impact of bias and confounding factors.bias and confounding factors.

Crossover study design.Crossover study design.

Reviewing literatureReviewing literatureStudy designStudy design– Hypothesis: Research and NullHypothesis: Research and Null– Theory/natural lawTheory/natural law– SampleSample

Inclusion/Exclusion criteriaInclusion/Exclusion criteriaRandomizationRandomizationMatching controlsMatching controls

– Test: Threshold for normal, sensitivity, specificity, use depending Test: Threshold for normal, sensitivity, specificity, use depending prevalence of disease in your population.prevalence of disease in your population.

– Measures for assessment resultsMeasures for assessment resultsAccuracyAccuracyPrecisionPrecisionStatistical analysis.Statistical analysis.Intention to treat. Intention to treat. ValidityValidityReliabilityReliability

– Bias: Selection, ObservationalBias: Selection, Observational– Confounding factorsConfounding factors– EfficacyEfficacy– EffectivenessEffectiveness

Reviewing literatureReviewing literature

Statistical powerStatistical power– Accepting or rejecting the null hypothesis is Accepting or rejecting the null hypothesis is

the basic thrust of any studythe basic thrust of any study– Power: Likelihood that a statistically Power: Likelihood that a statistically

significant difference would be found between significant difference would be found between two groups given that a difference truly exists.two groups given that a difference truly exists.

Sample size: Rule of 3, if a condition occurs 1 in Sample size: Rule of 3, if a condition occurs 1 in to 10, then the population needed is three times to 10, then the population needed is three times this number (30) for a statistically significant study.this number (30) for a statistically significant study.

Experiment designExperiment design


Type I errorType I error– Rejecting a true null hypothesis/accepting a false Rejecting a true null hypothesis/accepting a false

positive research hypothesis; hence a false positive positive research hypothesis; hence a false positive resultresult

– Alpha is the frequency of occurrence of a type I errorAlpha is the frequency of occurrence of a type I error– The probability of committing a a type I error is the P-The probability of committing a a type I error is the P-

valuevalue– P-value is the probability that the null hypothesis is P-value is the probability that the null hypothesis is

true, and the lower the more significant, <0.05 means true, and the lower the more significant, <0.05 means that less than 5% possibility that result is by chance, that less than 5% possibility that result is by chance, <0.01 means that less than 1% possibility that result <0.01 means that less than 1% possibility that result is by chance.is by chance.


Type II errorType II error– Accepting a false null hypothesis/rejecting a Accepting a false null hypothesis/rejecting a

true positive research hypothesis; hence true positive research hypothesis; hence false-negative resultfalse-negative result

– Beta signifies the frequency of a type II error Beta signifies the frequency of a type II error occurringoccurring

– Protect with statistical powerProtect with statistical power

Statistical AnalysisStatistical Analysis

NoncontinuousNoncontinuous– P-valuesP-values– The chi-square which concerns the frequency The chi-square which concerns the frequency

of event occurrenceof event occurrence– The Fisher’s exact test which estimate the P-The Fisher’s exact test which estimate the P-

value when small samples are usedvalue when small samples are used


ContinuousContinuous– One sample T-TestOne sample T-Test

Compares the sample mean value to a known Compares the sample mean value to a known mean of a standard variablemean of a standard variable

– Two sample T-Test or paired T-TestTwo sample T-Test or paired T-TestCompares the mean values with two independent Compares the mean values with two independent groupsgroups

– ANOVAANOVACompares values in more than two independent Compares values in more than two independent groups, the variation is between and within groupgroups, the variation is between and within group


The normal distributionThe normal distribution– Mean: Sum of all values divided by number of Mean: Sum of all values divided by number of

themthem– Median: The middle most observationMedian: The middle most observation– Mode: The most frequent observationMode: The most frequent observation– Standard deviation: A measure of spreadStandard deviation: A measure of spread

1 SD: About two thirds of data1 SD: About two thirds of data

2 SD: About 95% of date will fall within2 SD: About 95% of date will fall within

3 SD: Virtually comprises 100% of the data3 SD: Virtually comprises 100% of the data


Sensitivity refers to ability of the test to correctly Sensitivity refers to ability of the test to correctly identify patients who have disease.identify patients who have disease.

s= s= All positive results in disease (TP) All positive results in disease (TP) x 100x 100

All specimens with disease (TP+FN)All specimens with disease (TP+FN)

Specificity refers to the ability of a test to Specificity refers to the ability of a test to correctly identify patients who do not have a correctly identify patients who do not have a disease.disease.

sp= sp= All negative results without disease (TN) All negative results without disease (TN) x 100 x 100

All specimens without disease (TN+FP)All specimens without disease (TN+FP)


PrevalencePrevalence– Proportion of persons with the disease among a Proportion of persons with the disease among a

group to whom test is appliedgroup to whom test is applied

P= P= Disease present (TP+FN)Disease present (TP+FN) All group (TP+FP+TN+FN)All group (TP+FP+TN+FN)

Pretest probabilityPretest probability– % of patients who have the target disorder as % of patients who have the target disorder as

determined before test is performeddetermined before test is performed


Likelihood Ratio summarizes in a single number Likelihood Ratio summarizes in a single number the clinical utility of a test, and it is added to the the clinical utility of a test, and it is added to the pretest probability to increase certainty:pretest probability to increase certainty:– LR of a positive test: s/(1-sp)LR of a positive test: s/(1-sp)

2 increases probability of disease by 15%2 increases probability of disease by 15%

5 increases probability of disease by 30%5 increases probability of disease by 30%

10 increases probability of disease by 45%.10 increases probability of disease by 45%.

– LR of a negative test: (1-s)/spLR of a negative test: (1-s)/sp0.5 decreases probability of disease by 15%0.5 decreases probability of disease by 15%

0.2 decreases probability of disease by 30%0.2 decreases probability of disease by 30%

0.1 decreases probability of disease by 45%0.1 decreases probability of disease by 45%


Odds RatioOdds Ratio– Compare a portion of the affected population with the Compare a portion of the affected population with the

unaffected population and is expressed as a ratiounaffected population and is expressed as a ratio

– The OR gives the odds of having a risk factor if the The OR gives the odds of having a risk factor if the condition is present as compared to having a risk factor if condition is present as compared to having a risk factor if the condition is not presentthe condition is not present

– The higher, the stronger association.The higher, the stronger association.

Odds ratio= Odds ratio= risk factor with disease presentrisk factor with disease present A/CA/C

risk factor without disease B/Drisk factor without disease B/D

Predictive valuesPredictive values

Positive predictive valuePositive predictive value– Describes the probability that a patient who Describes the probability that a patient who

has an abnormal test actually has the diseasehas an abnormal test actually has the disease– Directly proportional to the prevalence of the Directly proportional to the prevalence of the

diseasedisease

PPV= PPV= Number of true positive results (TPNumber of true positive results (TP) x 100) x 100

All positive test results (TP+FP)All positive test results (TP+FP)

Predictive ValuesPredictive Values

Negative predictive valueNegative predictive value– Describes the probability that a patient who Describes the probability that a patient who

has a normal test is actually free of diseasehas a normal test is actually free of disease– Inversely proportional to the prevalence of the Inversely proportional to the prevalence of the

diseasedisease

NPV= NPV= Number of true negative results (TN)Number of true negative results (TN) x100 x100

All negative test results (TN+FN)All negative test results (TN+FN)

Number necessary to treatNumber necessary to treat

NNT is how many patients must receive a NNT is how many patients must receive a treatment to produce one additional treatment to produce one additional improved outcome compared to control. improved outcome compared to control. The lower the NNT, the more effective the The lower the NNT, the more effective the treatment.treatment.– NNT: 1/ARRNNT: 1/ARR– ARR: Event rate w/o tx-event rate w tx.ARR: Event rate w/o tx-event rate w tx.– RRR: Event rate w/o tx/event rate w tx.RRR: Event rate w/o tx/event rate w tx.

Confidence IntervalsConfidence Intervals

Provides an interval that is likely to capture the Provides an interval that is likely to capture the population mean with a level of confidence.population mean with a level of confidence.

95% CI: Indicates that if a test were repeated 95% CI: Indicates that if a test were repeated 100 times, a result within the specified range of 100 times, a result within the specified range of values would be expected 95% of the time. In values would be expected 95% of the time. In studies is presented as mean minus and plus studies is presented as mean minus and plus two standard deviations: “4.5 ( 95% CI, 3.8 to two standard deviations: “4.5 ( 95% CI, 3.8 to 5.4).5.4).

Larger studies typically have narrower CI’s.Larger studies typically have narrower CI’s.

Board-type QuestionsBoard-type Questions

PSA has a sensitivity of 75% and PSA has a sensitivity of 75% and specificity of 80%. Prevalence of prostatic specificity of 80%. Prevalence of prostatic carcinoma in your referral male population carcinoma in your referral male population is 10%. If your patient has positive result is 10%. If your patient has positive result on the blood test, what is the chance that on the blood test, what is the chance that he has prostate carcinoma?he has prostate carcinoma?– What they are asking for?What they are asking for?– Calculate TP, FP, FN, TN then PPV, NPV.Calculate TP, FP, FN, TN then PPV, NPV.


A 40 years old woman wants to have a A 40 years old woman wants to have a mammogram to make sure that she does mammogram to make sure that she does not have breast carcinoma because the not have breast carcinoma because the prevalence of breast cancer in her prevalence of breast cancer in her population is known to be 20 in 1000. population is known to be 20 in 1000. Sensitivity of mammogram test is 70% and Sensitivity of mammogram test is 70% and specificity is 90%. If her mammogram is specificity is 90%. If her mammogram is positive, what is the likelihood that she has positive, what is the likelihood that she has breast cancer?breast cancer?


An HIV pregnant patient has 2% risk of An HIV pregnant patient has 2% risk of transmitting infection to the baby if transmitting infection to the baby if delivered with CS compared to 7% risk if delivered with CS compared to 7% risk if delivered vaginally. How many c-sections delivered vaginally. How many c-sections you would have to do to prevent one HIV you would have to do to prevent one HIV infection?infection?

Board-type QuestionBoard-type Question

Rates of re-admission for HF diminished Rates of re-admission for HF diminished from 46% to 41% after close from 46% to 41% after close multidisciplinary monitoring program multidisciplinary monitoring program initiated upon discharge.initiated upon discharge.– What is the number necessary to monitor to What is the number necessary to monitor to

prevent one re-admission?prevent one re-admission?– What is the sample size required to give What is the sample size required to give

power to a study? (Rule of 3).power to a study? (Rule of 3).– What could be the number to start with? Rule What could be the number to start with? Rule

of 3.of 3.

epidemiology in medicine sandra rodriguez internal medicine ttuhsc

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