causal relationships, bias, and research designs professor anthony digirolamo

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Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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Page 1: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal relationships, bias, and research designs

Professor Anthony DiGirolamo

Page 2: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal Relationships

Page 3: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Page 4: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Page 5: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Necessary Cause

Page 6: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Necessary CauseFactor must be present for disease, but factor

can be present without developing disease

Page 7: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Necessary CauseFactor must be present for disease, but factor

can be present without developing diseaseRisk Factor

Page 8: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Causal RelationshipsSufficient Cause

Precedes diseases, factor always present with disease

Necessary CauseFactor must be present for disease, but factor

can be present without developing diseaseRisk Factor

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

Page 9: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Determining Cause and Effect

Page 10: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Determining Cause and EffectMill’s Cannons

Page 11: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Determining Cause and EffectMill’s Cannons

Strength Association shows a large difference

Page 12: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Determining Cause and EffectMill’s Cannons

Strength Association shows a large difference

Consistency Association is always present with the disease

Page 13: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 14: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 15: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

Page 16: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Page 17: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Common Types

Page 18: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Common TypesAssembly bias – characteristics of a group are

not evenly distributed

Page 19: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Common TypesAssembly bias – characteristics of a group are

not evenly distributedSelection bias – participants allowed to select

which part of the study they are in

Page 20: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Common TypesAssembly bias – characteristics of a group are

not evenly distributedSelection bias – participants allowed to select

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

disease

Page 21: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsWhat is bias?

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

Common TypesAssembly bias – characteristics of a group are

not evenly distributedSelection bias – participants allowed to select

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

diseaseMeasurement bias –variations due to

instrumentation or user error

Page 22: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsAdditional concerns when doing research…?

Page 23: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsAdditional concerns when doing research…?

Random error

Page 24: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsAdditional concerns when doing research…?

Random errorConfounding

Page 25: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Common PitfallsAdditional concerns when doing research…?

Random errorConfoundingSynergism

Page 26: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignAll research is descriptive, and results are

directly related to the data assembled

Page 27: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignAll research is descriptive, and results are

directly related to the data assembledWhat is a hypothesis ?

Page 28: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignAll research is descriptive, and results are

directly related to the data assembledWhat is a hypothesis ?

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

Page 29: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignAll research is descriptive, and results are

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

Page 30: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignAll research is descriptive, and results are

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 It’s called RE-SEARCH for a reason ! : )

Page 31: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research Design4 Key Factors of Research Designs

Page 32: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research Design4 Key Factors of Research Designs

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

Page 33: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 34: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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)

Page 35: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 36: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

Page 37: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

Cross-Sectional Surveys

Page 38: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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

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

Page 39: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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

Page 40: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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

Page 41: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

Cross-Sectional Ecological Studies

Page 42: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

Cross-Sectional Ecological Studies Relate the frequency of one characteristic

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

Page 43: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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

Page 44: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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

Page 45: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating Hypotheses

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 Long-term surveillance or frequent cross-sectional

surveys to measure trends in disease rates over many years

Page 46: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

Hypotheses

Page 47: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

HypothesesCohort Studies

Page 48: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

HypothesesCohort Studies

Compares two groups of clearly defined individuals, randomly selected

Page 49: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

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

Page 50: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

HypothesesProspective vs. Retrospective Cohort Studies

Page 51: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

HypothesesProspective vs. Retrospective Cohort Studies

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

Page 52: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Generating or Testing

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

Page 53: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 54: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

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

Page 55: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

Page 56: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

Randomized Controlled Clinical Trials (RCCT)

Page 57: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

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 ?

Page 58: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

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 ? 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

Page 59: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

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 ? 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)

Page 60: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Research DesignsDesigns for Testing Hypotheses

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 ? 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)

Page 61: Causal relationships, bias, and research designs Professor Anthony DiGirolamo

Well Done!!