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Why research:

An academic necessity?

Intention to answer a few questions to improve Patient care n management?Personal glory?

Competitive edge that it may provide?

Skeptic disagreement with so-called “established practices”?Common curiosity and natural tendency to challenge and be challenged?

Ultimately, it should add to existing body of knowledge, explain the unexplained and add to Patient care and management

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In other words,

We are attempting to unravel “truth” in the universe with the help of Truth in the study which is based on “findings in the study” which in turn depends on “plan” and “execution” of the study……

Hence, preparing properly and adequately at planning stage and executing right things in the right manner may eventually lead to truth in the study and may permit further extension to Truth in the universe!

So, now you know that Designing ,implementing, and inferring –all these stages are prone to “Errors” and the art and science of minimizing the same and increasing external and internal validity is Quantitative Methods in research!!8/4/2011 3RKB

Ideal study should be:Accurate: 1) validity (internal and external) 2)precisionFree from bias and confoundersNo sampling error (Type- I or alpha, Type-II

or beta)Confidence intervals (CI)“p” value significance.

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Anatomy of Research

  1. Define the problem

 2. Specify the objectives

 3.Select design or type of study

 4.Select study population

 5.Collect data

 6.Analyze data

 7. Determine conclusions

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Sequence and Cycles of Research

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Getting ready….

Relax, investigator needs to get acquainted onlyIt may be appreciated that it is a good idea to understand basic, underlying principlesWhat is required to be done and how to draw inference is to be learnt

Most computer software efficiently do the sample selection and statistical testing, but fortunately, they do not think for you as of now….

One does not have to know how to manufacture an automobile-just knowing how to drive a car is good enough.

Pl. do not underestimate those who go into the details of this science—some of the much smarter and younger brains have invested years which I am sure I can not justifiably pass on over a few minutes!!!

Pl. do not interpret or infer what is not there or not tested8/4/2011 7RKB

Hypothesis

Testing the same

Reject Null Hypothesis

Fail to reject Null Hypothesis

Much similar to Guilt, Guilty, Acquital, conviction!!!!

Truth in the populationResult in Sample Assoc. YES Assoc. NO

Reject Null H Correct Type I error

Fail to reject NullH Type II error correct

Where, the null hypothesis states that there is No association between Predictors and outcome 8/4/2011 8RKB

Type I error thus give false positive study result.

This can arise out of methodological faults or “pure chance” or both.

Hence it can not be eliminated completely, but can be brought down to a “measurable” level. This only chance measure ,your PSM friends call αAlpha.! This is the level of statistical significance-the level of reasonable doubt one is willing to accept based on study results i.e. you agree to err to the tune of 5%.Type II error gives false negative study result. Actuality not picked up by the study .Like alpha, it is not entirely avoidable, hence try to bring it to measurable minimum. This is called β.(beta). (1- beta ) is the power of the study. In simple terms, if beta is set at 0.20,it means, researcher is willing to accept a 20% chance of missing what is true in the population.Conventional alpha is 0.05 and beta 0.20.Ideal α and β should be 00- only conceptual and not concrete!!!!!

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TYPE OF STUDIES Observational

 1. Correlation study

 2. Case reports and case series

 3. Cross sectional survey

 4. Case-control study

 5. Cohort study

 Experimental or interventional

 1. Community trials

 2. Clinical trials – individuals

3. RCT 

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For the study to be valid

We need Precision and Accuracy

Precision is the degree to which a variable is reproducible, with nearly the same value each time it is measured.At times precision is also called reliability and consistencyPrecision is a function of Random error i.e. “chance error”Generally due to Observer variability, Instrument variability or Subject variabilityStandardizing, automation, training and repetition will help reduce these errors

Not entirely avoidable hence, learn to be vigorous, define in advance how much vigorous you intend to be and learn to measure the errors made!

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Accuracy:

It is the degree to which a variable represents what it is intended to representIt compares to a reference standardIt increases the validity&It is prone to Systematic errors(cf. chance errors and precision),hence accuracy is a function of systematic error or “Bias”Like with precision,Here also, it could be observer bias, instrument bias and subject bias.Comparing with “Gold standard’ will assess accuracy….which generally is known as specificity and sensitivity…..

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Approach Type of study

Examples

Observational

1. Descriptive - Institutional surveys - Community surveys

2. Analytic -cross-sectional

- Case-Control studies - Cohort studies

Experimental

Analytic- Lab experiments - Animal experiments - Clinical trials

STUDY DESIGNS INSTUDY DESIGNS INAPPLIED MEDICAL RESEARCH APPLIED MEDICAL RESEARCH

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Cross – Sectional Studies

Definition : In a cross-sectional study the information is collected from each subject at one point in time. This is in contrast to a cohort study which collects information on new events over a period of time. The main outcome measure obtained from a cross-sectional study is prevalence.

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Cross sectional study would permit simple analysis by categorizing data according to exposure status and outcome status.Thus, it will give Point prevalenceCrude prevalence ratePrevalence among exposed and among NOT exposed& there by difference of the 2 above and the ratio of the 2 above.A simple chi square test will get us the strength of association Further, CI for Prev. rate ratio also can be calculated.Most software do it for you !

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Cross sectional studies are onetime observations-a snap shot as they call it!Describes variables and its distributionGives point prevalenceAt times, in a relationship/association one is not too sure of what is a predictor and what is an outcome!

Risk factor;Disease

Risk factor;

No Disease

No Risk factor;

No Disease

No Risk factor;Disease

Sample

Population

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CASE - CONTROL STUDYCASE - CONTROL STUDY

Yes

No

Yes

No

Selectcases

Selectsuitablecontrols

Exposure to risk factor

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Without controls there can not be a case-control study, but with the wrong controls there can only be regrettable case –control studies.Controls should be comparable to case s except for the disease under study & frequency of the exposure under study..However potential for exposure should be same…Controls should come from same source population and should follow same selection criteria as casesHospital controls generally not representative of the source populationThough convenient, useful, better comparability, better recall they may be “inherently” different and may weaken internal validity.

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While selecting Controls ,pl. ask:

Do controls come from the same source population as cases?

Are they similar to cases as regards potential for past exposures?

Are potentialities of confounders similar?

Have similar exclusion criteria are applied to both cases and controls?

Have they come from the same time period?8/4/2011 19RKB

Analysis of Case –Control study gives OR or Odds Ratio.

As one would understand ,it can not give incidence, cumulative incidence or relative risk as we have not taken cases emerging prospectively ,BUT, we have cases selected from a population where the rate of occurrence would be different from the proportion which is “selected” for the study.

If incident cases are used, if selection of cases is Unbiased and (if) the outcome is Rare… OR can approximate RR.OR, chisquare and CI of OR can be calculated to refine the analysis.

As it is the most common design employed, and because it is vulnerable to Bias and confounders, we shall touch upon both Bias and confounders a little later..

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COHORT STUDYCOHORT STUDY

Screenpopulation

Diseaseabsent

Diseasepresent

Sample

Risk factorpresent

Risk factorabsent

Developdisease

Do notdevelop

Developdisease

Do notdevelop

Time

Time

/ /

/ /

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Cohort Study:

What matters is the sequencing of Exposure and outcome.

Measured at one time, simultaneously is Cross-sectional, Outcome decided before exposure is case-control while in Cohort, exposure is necessarily determined before outcome.

Even in a retrospective or historical or “Cohort –on –paper” ,in the time line, though outcome also has already occurred, examining exposure precedes the outcome. Hence, they are Longitudinal in nature.

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In establishing cause and effect relationship, an absolute must is “Temporal relation”Cohort study, best meets with this essentiality.

Permits calculation of Incidence among exposed and non-exposedPermits calculating RRPermits examining multiple outcomesSince exposure is measured before outcome ,a developing outcome will have no opportunity to influence exposure.

Some issues:

Large sample size requirementLong follow upMeasurement biasSelection biasLost to follow upmisclassification

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CASE-CONTROL Vs COHORT STUDY MERITS

CASE- CONTROL COHORT

Takes Less time Non – expensive Rare diseases can be studied

Practically no bias Cause- eff ect can be proved Results generalisable

DEMERI TS

Recall bias Cause- eff ect can’t be proved Results not generalisable Selecting suitable control

Takes more time Expensive Needs large sample Losses to follow- up

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Intervention studies

Randomized controlled trialsClinical trials

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Key steps in randomized controlled trial

Clear informationSpecific objectives of trailDefine the reference populationSelect the study populationSelect suitable subjectsObtain informed consentCollect baseline data

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Randomly allocate the subjects to the (new) intervention or to the standard or placebo treatment (controls)

Follow up all subjects in both groups (minimizing and monitoring defaulters and subjects lost to follow up).

Make an assessment of defined outcome(s) continuously, intermittently, or at the end of trial (“blind” if appropriate)

Analysis – comparison of outcomes between intervention and control groups.

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Interpretation – Magnitude of effect - Alternative explanations of effect e.g. bias in composition or follow-up of groups - Policy implications Feedback results to the participants

Communicate key results to the relevant official bodies (eg Ministry of Health, non-governmental organizations), and the general public

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For an incidental factor to be confounder:

It must be associated with the exposureIt must be an independent risk factor for the outcomeIt must NOT be intermediate in the chain of exposure to outcomeIt must be present in both study aswellas comparison group8/4/2011 30RKB

Bias Non random systematic error which occurs

during study design/conduct/analysis/interpretation is called BIAS.

The study must be designed and conducted in such a manner that that every possibility of introducing a bias is anticipated and steps are taken to minimize its occurrence

If indeed the study has elements of bias, it can not be rectified at the stage of analysis (unlike confounding)

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Types of bias Selection bias: mostly during study design

stage. A particular problem in case control and

retrospective cohort studies where both exposure and disease have occurred at the time of selection of individuals for the study.

Eg: 1)Berksons’ Bias, 2)prevalence incidence

bias, 3)healthy worker effect, 4)volunteer bias, 5)response bias, 6)loss to follow up bias.

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Bias: Measurement bias: mostly during

data collection phase. eg: 1) Recall bias, 2) interviewer bias,

3)Diagnostic suspicion bias, 4) Exposure suspicion bias.

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Confounding A confounder is a factor that is associated

with the exposure and independently affects the risk of developing the disease.

It distorts the estimate of true relationship

between the exposure and disease: it may result in association being observed when none in fact exists; or no association being observed when a true relationship does exist.

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Confounding: example An observed association between the

consumption of coffee and the risk of MI could be due, at least in part, to the effect of cigarette smoking, since coffee drinking is associated with smoking , and independent of coffee drinking, smoking is a risk factor for MI

The potential or true confounders are not

always as obvious as seen here in this example.8/4/2011 35RKB

Positive and negative confounding Tobacco smoking would be a positive

confounder in association between coffee drinking and CAD

The association between physical activity and CAD would be negatively confounded by gender, since women have lower risk of CAD and they also exercise less than men.

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Common confoundersAge and sex are almost universal

confounders for all exposure – disease associations

This is because they are markers for a whole lot of cumulative exposures. They may not be causally related to disease, but are markers for many other exposures which might be truly related to disease.

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Controlling confoundersRestriction of the study populationMatchingRandomization of exposureStratificationMultivariable analysis

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I am indeed thankful for getting access to Educational materials made available to E-course participants under Indo-US collaboration, under Fogarty Grant with Medical College, Baroda.

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Acknowledgements: Prof. SL KanthariaDr. NR GodaraDr. Deepak saxenaDr. RP Sridhar

Prof. VS MazumdarDr. Shobha MisraDr. Sangita PatelDr. Kedar Mehta

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