SMHS IntroEpi - Revision history

Glenbrau: /* Bias */

2015-04-27T13:11:06Z

‎Bias

Glenbrau: /* Other Risk Estimates */

2015-04-27T13:06:58Z

‎Other Risk Estimates

Glenbrau: /* Ecologic Studies */

2015-04-27T13:04:57Z

‎Ecologic Studies

Glenbrau: /* Strengths and Limitations of Cross-Sectional Studies */

2015-04-27T12:59:49Z

‎Strengths and Limitations of Cross-Sectional Studies

Glenbrau: /* Interpretation */

2015-04-27T12:58:58Z

‎Interpretation

Glenbrau: /* Cohort Study */

2015-04-27T12:55:52Z

‎Cohort Study

Glenbrau: /* External and internal validity */

2015-04-27T12:53:21Z

‎External and internal validity

Glenbrau: /* Reliability(repeatability) of tests */

2015-04-27T12:44:48Z

‎Reliability(repeatability) of tests

Glenbrau: /* Case Control Study */

2015-04-16T19:36:57Z

‎Case Control Study

Glenbrau: /* Cohort Study */

2015-04-16T19:36:33Z

‎Cohort Study

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 ===Bias===
 Bias is a barrier to internal validity.
-*Causes of bias: Any systematic error in the design, conduct or analysis of a study that results in a distorted estimate of the relationship between an exposure and outcome; observed results different than true results.
+*''Causes of bias'': Any systematic error in the design, conduct or analysis of a study that results in a distorted estimate of the relationship between an exposure and outcome; observed results different than true results.
-*Impact of bias: makes it appear as if there is an association when there really is none (bias away form the null); mask an association when there really is one (bias toward the null).
+*''Impact of bias'': Makes it appear as if there is an association when there really is none (bias away form the null); masks an association when there really is one (bias toward the null).
-*Reasons we get wrong answer: Selection bias: who is selected or retained in a study distorts your estimates of the truth. Example may be selection bias due to different retention in the study.
+*''Reasons we get wrong answers'': Selection bias: who is selected or retained in a study distorts your estimates of the truth. Example may be selection bias due to different retention in the study.
 *Mechanisms to reduce bias:
 **Ensure proper selection of study subjects (chose groups from the same source population; try lists of people that are more inclusive; use methods that result in high recruitment rates).
 **Minimize loss-to-follow up: keep participants happy and in touch with study team; review non-respondents to understand characteristics.
-*Information bias: the quality of your information distorts your estimate of the true association. Examples include surveillance bias, non-differential misclassification of hypertension, reporting bias and differential misclassification. Sources of measurement error/misclassification: normal variability or imprecision in measure, error due to subconscious or conscious decisions by the participant or investigator.
+*''Information bias'': The quality of your information distorts your estimate of the true association. Examples include surveillance bias, non-differential misclassification of hypertension, reporting bias and differential misclassification. Sources of measurement error/misclassification: normal variability or imprecision in measure, error due to subconscious or conscious decisions by the participant or investigator.
-* Confounding bias: differences between cases and controls or exposed and unexposed distorts your estimates of the truth. A variable is a confounder if it is a known risk factor for the outcome, it is associated with the exposure but not a result of the exposure. These three conditions are necessary for a variable to be considered as a confounder.
+*''Confounding bias'': Differences between cases and controls or exposed and unexposed distorts your estimates of the truth. A variable is a confounder if it is a known risk factor for the outcome, it is associated with the exposure but not a result of the exposure. These three conditions are necessary for a variable to be considered as a confounder.
-* Chance: the luck of draw gets you a study sample that is not representative of the larger population.
+*''Chance'': The luck of draw gets you a study sample that is not representative of the larger population.
-*Strategies to handle confounding: (1) in study design – individual matching, group matching, randomization (experimental) studies; (2) in data analysis – stratification, adjustment. Matching in a case-control study:
+*''Strategies to handle confounding'': (1) In study design – individual matching, group matching, randomization (experimental) studies; (2) in data analysis – stratification, adjustment. Matching in a case-control study:
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-* Concordant pairs: both case and control exposed; neither case nor control exposed.
+*''Concordant pairs'': Both case and control exposed; neither case nor control exposed.
-*Discordant pairs: case exposed but control not exposed; control exposed but case not exposed.
+*''Discordant pairs'': Case exposed but control not exposed; control exposed but case not exposed.
-*Matched analysis: Odds ratio (only based on discordant pairs) $Odds\, Ratio =\frac {b} {c}.$
+*''Matched analysis'': Odds ratio (only based on discordant pairs) $Odds\, Ratio =\frac {b} {c}$
 ''Interpretation'': If there is an association between exposure and outcome, it is not due to any factors that were matched on; you cannot conduct analyses for matched variables and outcome.
-*Randomization: Random allocation of exposure/”treatment” by investigator, ensure that the two groups (exposed & unexposed) are the same except for exposure of interest, able to control for both known and unknown confounders because distribution of these “3rd variables” should be equally distributed between the groups.
+*''Randomization'': Random allocation of exposure/”treatment” by investigator, ensure that the two groups (exposed & unexposed) are the same except for exposure of interest, able to control for both known and unknown confounders because distribution of these “3rd variables” should be equally distributed between the groups.
-*Stratification: Examine the relationship between exposure and outcome within each stratum of a potential confounding variable; holding the confounding variable constant.
+*''Stratification'': Examine the relationship between exposure and outcome within each stratum of a potential confounding variable; holding the confounding variable constant.
-*Adjustment: A statistical technique that can be used to examine what the association between exposure and outcome would be IF the confounder was not associated with the exposure.
+*''Adjustment'': A statistical technique that can be used to examine what the association between exposure and outcome would be IF the confounder was not associated with the exposure.
-Example of age-adjustment.
+An example of age-adjustment:
 [[Image:MSHS_IntroEpi_Fig4.png]]

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 ===Other Risk Estimates===
-*Attributable Risk Estimates of Effect – if exposure causes increased risk of disease, then we can estimate how many cases of disease could be eliminated if we completely eliminate the exposure.
+*''Attributable Risk Estimates of Effect'': If exposure causes increased risk of disease, then we can estimate how many cases of disease could be eliminated if we completely eliminate the exposure.
-*Attributable Risk (AR): $AR=CI_{Exposed} - CI _{Not\,exposed}$. This is just the risk difference. Group of interest: exposed and aims to quantify the risk of disease in the “exposed” group attributable to the exposure.
+*''Attributable Risk'' ($AR$): $AR=CI_{Exposed} - CI _{Not\,exposed}$. This is just the risk difference. Group of interest: exposed and aims to quantify the risk of disease in the “exposed” group attributable to the exposure.
-*Attributable Risk Percent $(AR\%)$: $ AR\%$ = $\frac{(CI_{Exposed} - CI_{Not exposed})}{CI_{exposed}}$
+*''Attributable Risk Percent'' $(AR\%)$: $ AR\%$ = $\frac{(CI_{Exposed} - CI_{Not exposed})}{CI_{exposed}}$
-*Population Attributable Risk (PAR):    $PAR= CI_{Total} - CI_{Not exposed}$
+*''Population Attributable Risk'' ($PAR$):    $PAR= CI_{Total} - CI_{Not exposed}$
-*Population Attributable Risk Percent $(PAR\%)$: $PAR\%$ = $\frac{(CI_{Total}-CI_{Not exposed})} {CI_{total}}$.
+*''Population Attributable Risk Percent'' $(PAR\%)$: $PAR\%$ = $\frac{(CI_{Total}-CI_{Not exposed})} {CI_{total}}$.
 ===Bias===

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 ===Ecologic Studies===
-An ecological study is an observational study in which group-level data is used for the exposure and/or the outcome. Subjects can be grouped by place (multiple-group study); by time (time-trend study); by place & time (mixed study). An error that could occur when an association identified based on group level (ecologic) characteristics are ascribed to individuals when such association do not exist at the individual level.
+An ecological study is an observational study in which group-level data is used for the exposure and/or the outcome. Subjects can be grouped by place (multiple-group study), by time (time-trend study), or by place & time (mixed study). However, one error that could occur is when an association is identified based on group level (ecological) characteristics that are ascribed to individuals when such associations do not exist at the individual level.
 ====Strengths and Disadvantages of Ecologic Studies====
 *'''Strengths:'''
-# data is relatively easy and/or cheap to obtain;
+# Data is relatively easy and/or cheap to obtain.
-# good place to start;
+# Ecological studies are a good place to start.
-# many relevant social, occupational and environmental exposures cannot be ascribed to an individual.
+# Many relevant social, occupational and environmental exposures cannot be ascribed to an individual.
-*'''Weakness:''' Reliance on group-level data may not correctly represent individual-level associations.
+*'''Weaknesses:'''
+#Reliance on group-level data may not correctly represent individual-level associations.
-*Ecologic fallacy is when an association between variables based on group characteristics is used to make inferences about individuals when that association does not exist.
+#Ecologic fallacy is when an association between variables based on group characteristics is used to make inferences about individuals when that association does not exist.
+#Ecologic studies are useful for generation of new hypotheses because they are relatively easy and low-cost to conduct.
 ===Other Risk Estimates===

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 ====Strengths and Limitations of Cross-Sectional Studies====
 * '''Strengths:'''
-# good for generating hypotheses;
+# Good for generating hypotheses
-# easily sets up other analytic designs;
+# Easily sets up other analytic designs
-# temporality is not a problem for time invariant exposures (genetic markers);
+# Temporality is not a problem for time invariant exposures (genetic markers)
-# relatively low cost.
+# Relatively low cost
 *'''Weakness:'''
-# temporality – exposure or disease which happened first;
+# Temporality – exposure or disease which happened first
-# prevalent cases may not be the same as incident cases;
+# Prevalent cases may not be the same as incident cases
-# not useful for rare disease;
+# Not useful for rare disease
-# subject to selection bias.
+# Subject to selection bias
 ====Measures of Association in Cross Sectional Studies====

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 *Avoiding Recall / Reporting Bias. Ways to avoid recall and report bias include:
-# adjusting timing so that the time between the event/illness and the study is as short as possible; use standardized questionnaires that obtain complete information;
+# Adjusting timing so that the time between the event/illness and the study is as short as possible; use standardized questionnaires that obtain complete information
-# using existing information if/when possible (e.g. medical record);
+# Using existing information if/when possible (e.g. medical record)
-# masking participants to study hypothesis
+# Masking participants to study hypothesis
 *Conditions when an OR from a Case-Control Study can approximate a RR OR≈RR:
-# when the cases are representative, with respect to their exposure status, of all people with the disease in the population from which the cases were drawn;
+# When the cases are representative, with respect to their exposure status, of all people with the disease in the population from which the cases were drawn
-# when the controls are representative, with respect to their exposure status, of all people without the disease in the population from which the cases are drawn;
+# When the controls are representative, with respect to their exposure status, of all people without the disease in the population from which the cases are drawn
-# when the disease being studied does not occur frequently.
+# When the disease being studied does not occur frequently
 ===Cross-Sectional Studies===

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 [[Image:MSHS_IntroEpi_Fig2_C.png |500px|]]
 *Types:
-: Prospective (concurrent) and Retrospective Cohort Studies (non-concurrent) based on when is the data collected
+**Prospective (concurrent) and Retrospective Cohort Studies (non-concurrent) based on when is the data collected
-: Retrospective has benefits: more cost effective; good for disease of long latency
+**Retrospective has benefits: more cost effective; good for disease of long latency
-: Prospective has benefits: data quality presumably higher
+**Prospective has benefits: data quality presumably higher
 Both designs need to be cautious of ascertainment biases if outcomes or exposure is known.
 *Measures of Association in Cohort Study:
-: Ratio of two measures of disease incidence (relative measures): Risk Ratio (Relative Risk), Rate Ratio
+**Ratio of two measures of disease incidence (relative measures): Risk Ratio (Relative Risk), Rate Ratio
-: Difference between two measures of disease incidence: Risk Difference, Rate Difference
+**Difference between two measures of disease incidence: Risk Difference, Rate Difference
 *Strengths and weakness of Cohort Design:

@@ Line 189: / Line 189: @@
 ====External and internal validity====
-*''External validity'': Generalization of study to larger source population. Influenced by factors like:
+*''External validity'': Generalization of study to larger source population, which is influenced by factors like:
 :*Demographic differences between eligible and ineligible subgroups
 :*Intervention mirror what will happen in the community or source population
-*''Internal validity'': Ability to reach correct conclusion in study. Influenced by factors like:
+*''Internal validity'': Ability to reach correct conclusion in study, which is influenced by factors like:
 :*Ability of subjects to provide valid and reliable data
 :*Expected compliance with a regimen

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-=====Reliability(repeatability) of tests=====
+=====Reliability (repeatability) of tests=====
 Can the results be replicated if the test is redone? The results may be influenced by three factors:

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 ===Case Control Study===
 A case control study compares cases and controls to see which group has greater exposure to the disease.
-*Measures of Association: Odds Ratio.
+*Measures of Association: Odds Ratio
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-$Odds\, Ratio=\frac{odds\, of\, a\, case\, being\, exposed}{odds\, of\, a\, control\, being\, exposed}=\frac{(a/c)} {(b/d)}=\frac {ad}{bc}.$
+$Odds\, Ratio=\frac{odds\, of\, a\, case\, being\, exposed}{odds\, of\, a\, control\, being\, exposed}=\frac{(a/c)} {(b/d)}=\frac {ad}{bc}$
 ====Interpretation====

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 [[Image:MSHS_IntroEpi_Fig2_C.png |500px|]]
 *Types:
-: Prospective (concurrent) and Retrospective Cohort Studies (non-concurrent) based on when is the data collected.
+: Prospective (concurrent) and Retrospective Cohort Studies (non-concurrent) based on when is the data collected
-: Retrospective has benefits: more cost effective; good for disease of long latency.
+: Retrospective has benefits: more cost effective; good for disease of long latency
-: Prospective has benefits: data quality presumably higher.
+: Prospective has benefits: data quality presumably higher
 Both designs need to be cautious of ascertainment biases if outcomes or exposure is known.
 *Measures of Association in Cohort Study:
-: Ratio of two measures of disease incidence (relative measures): Risk Ratio (Relative Risk), Rate Ratio.
+: Ratio of two measures of disease incidence (relative measures): Risk Ratio (Relative Risk), Rate Ratio
-: Difference between two measures of disease incidence: Risk Difference, Rate Difference.
+: Difference between two measures of disease incidence: Risk Difference, Rate Difference
 *Strengths and weakness of Cohort Design:
 : Strengths:
-# Maintain temporal sequence – can estimate incidence of disease; exposure precedes development of disease; also explore time-varying information.
+# Maintain temporal sequence – can estimate incidence of disease; exposure precedes development of disease; also explore time-varying information
-# Excellent for studying known adverse exposures or those that cannot practically be randomized.
+# Excellent for studying known adverse exposures or those that cannot practically be randomized
-# Like RCT, excellent for studying rare exposures.
+# Like RCT, excellent for studying rare exposures
-# Multiple outcomes and sometimes multiple exposures can be studied.
+# Multiple outcomes and sometimes multiple exposures can be studied
 : Disadvantages:
-# Long-term follow-up required and expensive;
+# Long-term follow-up required and expensive
-# Not effective at capturing rare outcomes and can be challenging to study disease that take a long time to develop;
+# Not effective at capturing rare outcomes and can be challenging to study disease that take a long time to develop
-# Loss to follow-up can be a problem;
+# Loss to follow-up can be a problem
-# Changes over time in criteria and methods can lead to problems with inferences;
+# Changes over time in criteria and methods can lead to problems with inferences
-# People self-select exposures so exposed and unexposed may differ with respect to important characteristics.
+# People self-select exposures so exposed and unexposed may differ with respect to important characteristics
 *Situations favor a Cohort Study:
-# When there is evidence of an association between the exposure and the disease from other studies;
+# When there is evidence of an association between the exposure and the disease from other studies
-# When the exposure is rare but incidence of disease among the exposure is high;|
+# When the exposure is rare but incidence of disease among the exposure is high
-# When time between exposure and development of the disease is relatively short or historical data is available;
+# When time between exposure and development of the disease is relatively short or historical data is available
-# When good follow-up can be ensured.
+# When good follow-up can be ensured
 ===Case Control Study===