Causal relationships, bias, and research designs Professor Anthony DiGirolamo.

transcript

Causal relationships, bias, and research designs

Professor Anthony DiGirolamo

Causal Relationships

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Necessary Cause

Necessary CauseFactor must be present for disease, but factor

can be present without developing disease

can be present without developing diseaseRisk Factor

An exposure, behavior, or attribute that, if present, clearly influences the probability of disease

Determining Cause and Effect

Determining Cause and EffectMill’s Cannons

Strength Association shows a large difference

Consistency Association is always present with the disease

Specificity No disease if the factor isn’t present

Biological Plausibility Fits the natural history of the disease

Common PitfallsWhat is bias?

An event that produces deviations that shift data in a particular direction (skew your data)

Common Types

Common TypesAssembly bias – characteristics of a group are

not evenly distributed

not evenly distributedSelection bias – participants allowed to select

which part of the study they are in

which part of the study they are inDetection bias – failure to detect true cause of

disease

which part of the study they are inDetection bias – failure to detect true cause of

diseaseMeasurement bias –variations due to

instrumentation or user error

Common PitfallsAdditional concerns when doing research…?

Random error

Random errorConfounding

Random errorConfoundingSynergism

Research DesignAll research is descriptive, and results are

directly related to the data assembled

directly related to the data assembledWhat is a hypothesis ?

An “educated guess” about the relationship that exists in an observed phenomenon

Not always right; often need to re-test to truly discern relationship

Not always right; often need to re-test to truly discern relationship It’s called RE-SEARCH for a reason ! : )

Research Design4 Key Factors of Research Designs

Enable comparison of a variable for two or more groups at a specified time

Comparison must be quantified in absolute or relative terms

Allow determination of the temporal sequence (when and how the factor and disease occur)

Minimize bias, confounding, and other outside elements that may skew from true results

Research DesignsDesigns for Generating Hypotheses

Cross-Sectional Surveys

Cross-Sectional Surveys Quick, cost-effective studies done of a population at

a certain point in time (calls, interviews, appointments, etc)

Useful in determining prevalent risk factors in a population

Surveys sometimes have low response rates

Cross-Sectional Ecological Studies

Cross-Sectional Ecological Studies Relate the frequency of one characteristic

(smoking) and an outcome (lung cancer) occurring in a geographical area

Downside is too many other factors and temporal issues may be overlooked or incorrectly identified

Longitudinal Ecological Studies

Longitudinal Ecological Studies Long-term surveillance or frequent cross-sectional

surveys to measure trends in disease rates over many years

Research DesignsDesigns for Generating or Testing

Hypotheses

HypothesesCohort Studies

Compares two groups of clearly defined individuals, randomly selected

Observations made to examine if one group develops a condition with a risk factor, the other without

HypothesesProspective vs. Retrospective Cohort Studies

Prospective cohorts are done at present time, baseline data recorded and then observed over time

Retrospective cohorts look back in time to a risk group and follow members in a current day scenario to see what conditions are now prevalent

Research DesignsDesigns for Generating or Testing Hypotheses

Prospective vs. Retrospective Cohort Studies Prospective cohorts are done at present time,

baseline data recorded and then observed over time Advantages are control of standardized procedures

and diagnosis, estimates of risk generated are true, many different disease outcomes may be studied at once

Disadvantages are high costs, loss of follow-up, long wait for final results

Retrospective cohorts look back in time to a risk group and follow members in a current day scenario to see what conditions are now prevalent

Research DesignsDesigns for Generating or Testing Hypotheses

Prospective vs. Retrospective Cohort Studies Prospective cohorts are done at present time, baseline

data recorded and then observed over time Advantages are control of standardized procedures and

diagnosis, estimates of risk generated are true, many different disease outcomes may be studied at once

Disadvantages are high costs, loss of follow-up, long wait for final results

Retrospective cohorts look back in time to a risk group and follow members in a current day scenario to see what conditions are now prevalent Good to estimate absolute risk, but lacks ability to

control data collection and standardization

Research DesignsDesigns for Testing Hypotheses

Randomized Controlled Clinical Trials (RCCT)

Randomized Controlled Clinical Trials (RCCT) Patients placed into two groups, one receiving a

treatment the other a placebo (usually a therapeutic treatment)

Use of “blinding” in trials ?

Use of “blinding” in trials ? Patients are unaware of which group they are in

(single), those dosing the patients also do not know (double)

Key is that both groups must be treated equally

Use of “blinding” in trials ? Patients are unaware of which group they are in

(single), those dosing the patients also do not know (double)

Key is that both groups must be treated equally

Randomized Controlled Field Trials (RCFT)

Use of “blinding” in trials ? Patients are unaware of which group they are in (single),

those dosing the patients also do not know (double) Key is that both groups must be treated equally

Randomized Controlled Field Trials (RCFT) Focuses on using a preventative measure, rather than a

therapeutical one (RCCT)

Well Done!!

Causal relationships, bias, and research designs Professor Anthony DiGirolamo.

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