Techniques in Clinical Epidemiology

Dr. M.Senthil Murugan Research Scholar

Dr. BR Singh, Head Division of Epidemiology Indian Veterinary Research Institute, Izatnagar & Director

CCS NIAH, Baghpat

Clinical Epidemiology ?•The research discipline concerned with applying

epidemiologic methods to questions relevant to the practice of medicine at the individual or herd/ flock level.

•Epidemiology – study of diseases in natural habitat, away form controlled environment

•Clinical Epidemiology - the study focusing on the sorts of questions asked in practice of medicine

Field / Clinical Epidemiology

Velez et al., 2013

Where it is used?Issue Question

Normality / Abnormality

•What are the limits of normality?•What abnormalities are associated with having the disease?

Diagnosis •How accurate are the diagnostic tests and strategies used to find the disease?

Frequency / Occurrence

•Case definition for a disease, how common are each of the findings•Host, spatial and temporal distribution of disease

Risk / Prevention

•What factors are associated with the likelihood of contracting disease?

Prognosis •What are the consequences of having a disease?•What factors are associated with an increased or decreased likelihood of recovering form disease?

Where it is used?Treatment / Control

•How effective the therapeutic strategy and how does it change the future course of the disease?

•How risk and rate of spread can be reduced – useful tools for diagnosis, treatment, control and prevention?

Cause •What is the etiologic agent? Its life cycle? Its pathogenicity and virulence?

•Factors determine susceptibility and resistance of individuals to the disease?

•Predisposing population conditions to outbreaks?

Source / Transmission

•Source and reservoir mechanism and period of communicability for causative agent?

•Spread and route of infection to susceptible?

Cost •What is the impact of a disease in personal and economic terms?

Interactions of Clinical Epidemiology

Evidence Based Medicine

•The relationship between epidemiology and clinical medicine has been formalized in the practice of evidence based medicine (EBM)

•The process of systematically finding, appraising, and using contemporaneous research findings as the basis for clinical decisions

Steps in EBMIdentify one or more clinically important information needs

and convert them in to answerable

questions.

Track down, with max efficiency the best evidence with which to answer

the above questions

Summarize and critically appraise

the evidence – Scientific validity and applicability

Apply the results of this appraise to patient care

Evaluate your performance at answering the questions

Attributes for Defining the limits of normality

• Medical decision making – SOAP (Subjective data, objective data, assessment and plan)

• Clinical data are nominal (Categorical), ordinal (ranking) or interval (Continuous)

• Validity (Accuracy) – degree to which the a measurement reflects the true status of what is being measured

• Reliability ( Precision) – measure of repeatability and reproducibility of clinical measurement

• Coefficient of Variation (CV) – express the precision of Clinical measurements

▫ S.D / Mean = Percent variation of set of measurements around their mean

Normal or Gaussian Distribution

Defining normality• First Data is expressed as frequency distribution.

• Set the Measures of Central Tendency – mean, median, mode.

• Determine Dispersion – range, SD, Percentile, Deciles, Quartile.

Normal or Gaussian Distribution

• Naturally occurring distributions – Two tailed tests of significance.

• Assessed by Skewedness and Chi Square goodness-of-fit test.

• Critical values (Cut offs) are set: Mean ± 1.96 SD : 95% normal range & 5 % outside normal

range.

• Test of Significance (one tailed) is applied : Mean ± 1.645 SD.

Limitations

• Outside of range includes False positive and false negatives.

• Only denotes the random variations, not others variations.

Techniques for evaluation of

Diagnostic Test• Medical decision making is critical.

• Diagnostic testing – diseased are differentiated from non-diseased and/ or those with other

diseases with similar symptoms.

• Screening–in healthy subjects- identification of unrecognized diseases in apparently healthy

ones.

• Gold standard test - definitive test – determines whether disease is truly present or not. The

best example is Post Mortem Examination.

• Sensitivity – proportion of true positives detected .

• Specificity - proportion of true negatives detected .

• False Positive Rate – likelihood of a positive test result in a patient known to be free of

disease.

• False Negative rate - likelihood of a negative test result in a patient known to have disease.

2x2 table– uni-variate analyses

Evaluation of Diagnostic Test• Predictive Value of the test: probability of the test result that reflects the true

disease status of an individual.

▫ Positive predictive value: Probability of disease in an animal with a positive

(abnormal) test result.

▫ Negative predictive value: Probability that an animal does not have the disease when

the test result is negative(normal).

• Likelihood ratio: compares the proportion of animals with or without disease, in

relation to their test results.

▫ LR + = proportion of affected individuals test positive / proportion healthy individuals

test positive.

▫ LR - = proportion of affected individuals test negative / proportion healthy individuals

test negative.

Techniques for evaluation of Diagnostic testsTest Parameter being evaluated How measured How expressed

Validity 2 x 2 Contingency table

1. Sensitivity2. Specificity3. Positive and negative predictive

values4. Accuracy

Optimum Cutoff Response Operating Characteristic (ROC) Curve

1. Positive and negative Cutoff value

Comparison of tests

1. Fixed Cutoff : Pretest/ Post test curve

2. Continuous Variable : (ROC) Curve

1. Posterior probability / prior probability

2. Likelihood ratio at different levels of test, Area under the curve

Clinical utility

1. True positive rate/ false positive rate

2. False negative rate/ true negative rate

3. Decision analysis

1. Likelihood ratio for a positive test2. Likelihood ratio for a negative test3. Testing and treatment thresholds

Techniques in Clinical Study designs

Experimental studies •allocation of individuals is under control of investigator•Randomization – Statistical procedures•symmetry of potential unknown confounders•strongest empirical evidence

Randomized Controlled Clinical Trial (RCT)

•prospective, analytical, experimental study•randomly allocated to two or more treatment groups•outcomes the groups are compared after follow-up time•clinical efficacy of preventive and therapeutic procedures

Randomized Cross-Over Clinical Trial

•prospective, analytical, experimental study•Individuals - chronic condition – 2 or more treatment groups•washout period - switched to the other treatment for the same period•Susceptible to bias

Randomized Controlled Laboratory Study

•laboratory environment•powerful tools -all extraneous factors other than those of interest can be controlled•Interaction of factor – clinical setting – not applicable

Clinical Study designs contd…

Observational Studies: •allocation of factors is not under control of investigator•self-selected or are "experiments of nature”•provide weaker empirical evidence•large confounding biases - unknown association between a factor and an outcome•preliminary evidence - hypotheses - stronger experimental studies

Cohort Study (Incidence, Longitudinal Study)

•prospective, analytical, observational study•follow-up period - association between that exposure and an outcome•lack of control over risk assignment•zero time bias• more expensive

Case-Control Study • retrospective, analytical, observational study• Cases compared with controls• Association measured by ODDS RATIO• initial, inexpensive evaluation of risk factors• rare condition - long induction periods• Potential for bias

Clinical Study designs contd…

Ecologic (Aggregate)

Study

•Aggregate data on risk factors –

• compare different populations groups- identify associations

•inferred from the group level

•likelihood of an ecologic fallacy

Cross-Sectional

(Prevalence Study) Study

•descriptive study - at one point in time

•relationship between diseases and other factors

•lack -information on timing of exposure and outcome

relationships

Case Series: •descriptive, observational study of a series of cases

•manifestations, clinical course, and prognosis of a condition

•used as a source of hypotheses - further studies

•most common study type - clinical literature.

Case Report •description of a single case

•manifestations, clinical course, and prognosis

•little empirical evidence to the clinician – single case

Relative Merits of Clinical research Designs

Study design Limitations Best application

Uncontrolled clinical trial •Time•Ethical consideration•No comparison group

•Prognosis with or without treatment

Non randomized controlled clinical trial

•Time•Ethical consideration•Bias in selection of comparison group

•Prognosis with or without treatment•Evaluation of new treatments

Randomized controlled clinical trial

•Time•Ethical consideration

•Prognosis with or without treatment•Evaluation of new treatments

Experimental disease •Time•Availability of animal or other animal models•Cost

•Proving relationship between risk and causal factors of disease•Pathogenic mechanisms

Relative Merits of Clinical Research Designs

Study design Limitations Best application

Case Report •Temporal relation ship•Bias in case selection•Statistical validity

•Detailed description of uncommon diseases•Surveillance

Case series •Temporal relation ship•Bias in case selection

•Frequency of findings in disease

Prevalence survey •Temporal relationship•Measures prevalence not incidence

•Evaluation of diagnostic tests•Incrimination of risk factors•Outbreak investigation

Case Control •Temporal relation ship•Bias in selection of comparison group

•Evaluation of diagnostic tests•Incrimination of risk factors•Outbreak investigation•Rare diseases or diseases of long latency

Bias in clinical Observation• Selection bias

– Occurs when selection and/or follow-up procedures lead to study group differences in determinants of outcome other than the one under study

• Measurement bias

– Occurs when measurements are imprecise and/or the methods of measurement are dissimilar among study groups

• Confounding bias

– Occurs when two factors are associated (“travel together”) and the effect of one is confused with or distorted by the effect of another)

– Due to selection or by chance

Bias in Clinical observation• Ecological (Aggregation) Bias (Fallacy):

• association observed between variables representing group averages is mistakenly taken

to represent the actual association that exists between these variables for individuals.

• Reader Bias -errors of interpretation made during inference by the user or reader of clinical

information.

• tendency is to accept information that supports pre-conceived opinions.

• Zero Time Bias: individuals are found and enrolled in such a fashion that unintended

systematic differences occur between groups at the beginning of the study.

• individuals are found and enrolled in such a fashion that unintended systematic differences

occur between groups at the beginning of the study.

• stage of disease, confounder distribution - Cohort studies.

Placebo Effect• Any medical intervention – non specific, psychological, psycho-physiologic

therapeutic effect.

• Used for presumed specific therapeutic effect – but is without specific activity for

the condition treated.

• May be favorable or unfavorable.

• Used as control in clinical trials and understanding the mechanism of work.

• In animal context – effect of human contact ( visual & tactile) on animal health.

• Forms a investigator bias rather than biologically mediated effect in animals.

• Clinical trials

▫ placebos as controls

▫ second control without placebos

Techniques to Control Bias• Randomization:

▫ Control groups are randomly allocated

▫ Balance the distribution of other variables may be outcome related

▫ Guarantees the validity of the statistical tests

▫ Block randomization and stratification

• Blinding or masking

▫ Procedures that prevent the study participants, caregivers, outcome assessors from knowing the intervention received

▫ Single, double, triple and quadrupled blinded RCT (Stanley, 2007)

• Allocation concealment (Viera, et al., 2007)

▫ Procedure for protecting randomization process so that the treatment can be allocated is not known before the patient is entered in to the study

▫ Sequentially numbered opaque sealed envelopes SNOSE method is used

BLINDING

Techniques to control confounding

• Adjusting – using adjusted rates in confounder

• Matching

▫ Frequency matching – groups are divided so that they contain same

proportion of same confounding variable

▫ Individual matching – each case in group matched with a control –

respect to variable

• Multivariate Methods:

▫ The Mantel-Haenszel procedure

▫ Summary of odds ratio is used

▫ stratifying data with respect to potential confounders.

The Mantel-Haenszel procedure• calculate the crude odds ratio.

• calculate the adjusted odds ratio.

• decide whether the difference between the crude and the adjusted values

is 'big' (this is somewhat arbitrary); if the answer is 'yes', then there is

confounding and the adjusted odds ratio should be used, if there is no

interaction.

• assess if the stratum-specific odds ratios are homogeneously distributed

across the strata (e.g., using either Woolf's statistic or the Breslow-Day

statistic); if not, there is interaction, and the sub tables should not be

collapsed.

Techniques for validity

• Internal validity

– The degree to which the results of a study are correct for the sample of

patients being studied.

▫ Truth within the study.

▫ Results not likely due to bias or confounding.

▫ All studies are flawed some degree – but not crucial to study results.

• External validity (Probability of being Generalized)

▫ The degree to which the results of an observation hold true in other

settings -Truth beyond study (Demicheli V, et al., 2013).

▫ Study population similar to reader population.

Measures of commonness of disease

• Measuring the frequency of disease events▫ Assessing the risk of contracting a disease▫ Its cause ▫ Prognosis and response to treatment

• Frequency is expressed as proportions or rates▫ Cases in numerator and population at denominator▫ Rate = affected/ affected + unaffected▫ Ratio = Affected / unaffected▫ Vital statistics rates – indirect measure - commonness▫ Morbidity rate (Prevalence, Incidence, Attack rate) – direct

measure - commonness

Vital statistics in Veterinary Medicine

Rate Calculation Measure of

Crude live birth rate

(No of live birth/ Avg. population)* 10n Population increment due to natural causes

General Fertility Rate

(No of live birth/ Avg. of females of Rep age)* 10n

Index of overall reproductive performance

Crude Death rate (No of death/ Avg. population)* 10n Population loss due to natural loss

Morbidity / Mortality ratesRate Calculation Measure of

Attack Rate (No of individuals during an outbreak / population at risk at the beginning of the outbreak) )* 10n

Identifying risk factors for specific disease in outbreak investigation

Incidence rate (No of new cases of disease over a time interval / avg. population at risk during the time interval) )* 10n

•Monitoring the course of epidemic•Cohort study – measure of effect of suspected or known risk factors

Prevalence rate (No of existing cases of disease at a point of time / population at risk during same point of time)* 10n

•Static measure of risk of having a particular disease•Case control study – effect of suspected or known risk factors

Case Fatality rate

No of deaths from a specified cause/ Total no of cases of same disease

•Determine prognosis of disease•Severity of the disease

Techniques for Risk Assessment and Prevention

• Uses – Prediction, Diagnosis, Cause, Prevention

• Risk Factors – increased likelihood of an event occurring.

• Comparison of Risks – strength of association between risk factor and outcome

• Statistical significance of this association

• Univariate analysis – 2 x 2 table

▫ Relative risk and Odds ratio are calculated

• Multivariate analyses

▫ Number of potential risk factors and outcome of interest

▫ Multivariate logistic regression analyses

▫ Y=a + b1X1 + b2X2 + b3X3 + e

Measures of effect in studies of risk of disease

Expression Clinical Question Formula

Relative Risk (Risk Ratio)

How many times more likely are exposed individuals to become diseased relative to unexposed?

Incidence in exposed/ incidence in unexposed

Attributable Risk (Risk Difference)

What is the incidence of disease attributable to exposure?

Incidence in exposed – Incidence in unexposed

Population Attributable risk

What is the incidence of disease in a population associated with the occurrence of Risk factor?

ARP = AR * Prevalence

Population Attributable Fraction

What fraction of disease in a population is attributable to exposure to a risk factor?

AFP = ARP / Total incidence in population

Odds Ratio (Cross Product Ratio)

Odds of case exposed / odds of control exposed ( in case control Studies)

a * d / b * c

Smith R.D, 2006

Meta-analysis Technique

• Statistical analysis of data pooled from several studies to integrate

findings

• Evaluation of diagnostic tests

• Cost benefit analysis for diagnostic techniques and treatment

• Assessment of magnitude of health problems

• Strong evidence for efficacy of treatment

• Common approach for provide a weighted estimate – odds ratio, relative

risk are used

• Homogeneity of data from different studies using chi square test

• Fixed effect model and random effect model – based on effect of treatment

ConclusionClinical epidemiology competences includes

• Understanding of how bias and chance can affect the accuracy of observations in individual patients.

• Evaluation of the validity of original observations over diagnosis, prognosis, treatment and prevention.

• Knowing the strengths and weaknesses of randomized clinical trials, case-control studies, cohort

studies (prospective and retrospective) and meta-analysis.

• Using practical strategies to judge the validity of clinical evidence synthesis (i.e. reviews).

• Comprehension of the meaning, uses and limits of the statistic power, the values of ‘p’ and the

confidence interval, relative risk, attributable risk.

• Knowing how to measure the patients’ preferences.

• Comprehension and usage of the sensibility analysis and the cost-effectiveness analysis.