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Evidence Based Medicine

Steps in the EBM process Ask, Acquire, Appraise, Apply
Framing the question: PICO: Patient, Intervention, Comparison, Outcome (& Type of question/study)
Hierarchy of Evidence Systematic Rvw/meta; RCT; Cohort/Case-Ctrl; Case Series, case report; Letters, etc
Where to search the lit: PubMed; Cochrane Collabn; Medline (OVID); ACP Journal Club; Practice Guidelines
Appraise: questions Are the results of study valid? What are the results? Will the results help in caring for my pt?
the study of the frequency, distribution and determinants of health related states or events in specified populations Epidemiology
2 assumptions of epidemiology 1: Human disease does not occur at random; 2: Factors that cause & prevent dz can be identified
Defn Frequency: how many cases of the dz exist or occurred
Defn distribution: who is getting the dz, where & when is it occurring (person, place & time)
Defn Factors/Determinants: what causes the disease, what influences or modifies the disease (physical, biological, social, cultural, behavioral factors)
Measures of dz frequency Prevalence; incidence
A fraction where the numerator is contained in the denominator (e.g., percentage); falls between 0 and 1 Proportion
Expression of the relationship btw a numerator & a denominator where the 2 usually are separate & distinct quantities, neither being included in the other; No restrictions on ranges or dimensions Ratio
Total number of individuals with the outcome of interest (i.e., disease, injury, health benefit) within a defined population at a particular point or period in time Prevalence
The number of persons with the disease or attribute at a specific point in time Point Prevalence
Number of persons with the dz at any time during defined period Period Prevalence
Number of new events or cases of an outcome of interest (i.e., dz, injury, health benefit) that develop in a population of individuals at risk during a specified interval of time Incidence
Cumulative Incidence (AKA: Relative Risk) Proportion: outcome occurring during a certain period of time
Incidence Rate (AKA: Incidence Density) Ratio: outcome occurring during person-time at risk
Assumptions for cumulative incidence: All study participants included in the calc for CI = observed (at risk) for the entire period; CI for specified time period infers* that the risk of dz is constant throughout that time period
Considered a more precise estimate of the impact of exposure in a population because it utilizes all the information about time at risk Incidence density
Able to determine changes and fluctuations in risk of disease over time Incidence density
Defn population at risk Free from dz, but susceptible
Prevalence pool influenced by: Incidence rate of dz; Dz Detection; recovery rate (dz duration); fatality rate
Methods & procedures for collecting, classifying, summarizing, & analyzing data & for making scientific inferences from such data Statistics
Descriptive Statistics Use of graphic, tabular, or numerical techniques to describe the properties of the data
2 types of categorical variable nominal; ordinal
Categorical variable: no intrinsic order: Nominal (e.g., race, gender, sub-types of leukemia)
Categorical variable: have some intrinsic order Ordinal variable (e.g., pain severity: none, mild, moderate, severe; Education level)
Measures of central tendency Means, medians, modes
Measures of variability Range, standard deviation (& variance)
Median vs mode median: middle value in list; mode = most frequent value
If variable is normally distributed: mean, mode, median all approx the same value
Problems with the mean 1: outliers skew mean; 2: bimodal dist: mean not a good summary stat
Measure of how spread out distribution is variance
Positive sq root of variance std deviation
Units for std deviation same units as the observations
In normal dist, there is a defined relationship between: std deviation & percentile distribution of the observations
Empirical rule: Std deviation (1,2,3): percentages 68, 95, 99.7%
A very large group of units about which scientific inferences are to be made = Population
Subset of units in the underlying population = Sample
Inferential Statistics Use of procedures to make inferences from the data (i.e., what are the implications of or what conclusions can be drawn from the data?)
Provides the actual numerical information used in making inferences about the population Sample
Sample mean vs popn mean Not nec same as popn; usu close to popn mean; some samples have means very diff from popn means
P-value = probability that … See a difference at least this large if samples were drawn from popn with same mean
Defn Study Power Probability that a study will conclude there is an association btw exposure & outcome when one truly exists
Defn Study Power = ability of a study to: to detect a statistically sig diff btw groups if one truly exists; 1 - beta
Confidence intervals can be calculated around: Diffs in means; diffs in proportions; odds ratios, relative risks, hazard ratios
Confidence interval is presented as: 1: point estimate; 2: plausible range of diffs btw which the true diff might actually lie (with 95% confidence)
Mann-Whitney U-Test / Wilcoxon Rank Sum test: used when? Used to compare continuous variables; Unpaired data; Nonparametric; Tests similar hypothesis as t-test
Wilcoxon Signed Rank Test used for what data? Used for paired data (e.g., before and after measurements on same individual)
Wilcoxon Signed Rank Test = Non-parametric test; Tests similar hypothesis as paired t-test
Statistical test used to determine if there is an association between the row and column classifications Chi squared test
Assumptions of t test Normally distribd variable (bell curve); independent observations (diff people, not multiple measurements on same people); reasonably large sample size
t-test used to compare means of >2 groups? No (2 groups only; otherwise: other tests)
Purpose of t test to detn how likely observed diffs in means btw groups could be due to chance
Type I error False positive (alpha error)
Type II error False negative (beta error)
t-statistic corresponds to: a given p-value
The bigger the t-statistic: the smaller the p value (more stat sig)
Causes of larger t-statistic Larger diff btw means; smaller variance; larger sample sizes
Used to compare observations that are not independent paired t-test
Alternatives to t-test or paired t-test Nonparametric tests
Nonparametric tests are used when: assumptions of t-test, particularly normal dist, are not met
Nonparametric tests most often used when: with small sample sizes
Power: Nonparametric tests vs parametric tests Nonparametric less powerful than parametric
Categorical data are often presented in: frequency tables
Test: compares observed frequencies to expected frequencies Chi squared test
Larger chi-square statistic correlates to: smaller p-value
Variations on chi square test Fisher exact; Mantel-Haenszel
Fishers exact test used when: expected frequencies in cells are small (<5)
Mantel-Haenszel chi-square tests for: linear association btw the row & column variables
When interpreting results of a study, consider: both the p-value and the size of the effect
Alternative to p-values as a way of presenting and interpreting results confidence intervals
CI: single value most likely to represent the true diff btw groups = point estimate
CI provides info on: precision of the estimate; statistical significance
If 95% CI excludes the null value, then: the findings are significant at p<0.05
CI: Greater variance results in: wider confidence interval
CI: Smaller sample size results in: wider confidence interval
Type I error: Concluding that a difference exists btw groups when no difference exists
Type II error: Concluding that no difference exists btw groups when a significant difference does exist
Alpha = p-value
Study Power is intimately related to: the sample size used in a study
Most studies designed with what power? 80 or 90% power
1-tailed vs 2-tailed test: which more conservative? 2-tailed
1-tailed vs 2-tailed test: which is the std in most research? 2-tailed
Chi squared used on what data type? Categorical
AKA Prevalence study Cross-sectional study
A study in which conditions are under the direct control of the investigator (e.g., randomized clinical trial, intervention study) Experimental
A study that does not involve an intervention or clinical trial. Investigators observe without intervening other than to record, classify, count and statistically analyze what is observed. Observational
______ studies examine the relationship between exposure and outcome with regard to the exposure preceding the outcome (“causation”) Etiologic studies
______ studies describe either the exposure, the outcome, or both, but do not infer a causal relationship between the two (association) Descriptive studies
A careful & detailed report of a single pt by one or more HCPs; documents unusual medical occurrences; possibly first clues in identification of new dz Case Report
An expansion of a case-report which usually describes the characteristics of a number of pts; may occur within a short period of time Case series
Study in which the units of analysis are populations or groups (not individuals); a study of group characteristics Ecologic study
A study that examines the relationship between diseases and other variables of interest as they exist in a defined population at one particular time. Cross-sectional study
Snap shot of disease prevalence in a population at one point in time Cross-sectional study
Can a cross-sectional study determine a temporal relationship (i.e., exposure preceded outcome)? No
Observational study: subjects selected on basis of whether they have (cases) or do not have (controls) a particular dz. Groups are compared w/respect to the proportion having a history of the exposure of interest. Case-control study
Source populations for cases in case-control studies Hospitals (Inpatient; Outpatient/Clinics); Communities (Registries; Dz Surveillance Registries)
Case-control studies: 2 types of matching of cases to controls Frequency matching; individual matching
Case-control studies: Number of controls selected per case can be influenced by: Cost; recruitment; small number of cases
Case-control studies: There is only a small increase in statistical power when the (controls:cases) ratio increases beyond: 3:1
Differences in the way exposure information is remembered or reported by cases (who have experienced an adverse health outcome) and controls (who have not). Recall bias
2 types of odds ratio Prevalence odds ratio; incidence odds ratio
Case-control studies: diagnostic criteria should minimize the likelihood that: 1: true cases are missed (sensitive); 2: false cases are included (specific)
A hybrid design in which a case-control study is nested in a cohort study. Nested case-control study
Observational study: subjects are sampled based on presence (exposed) or absence (non-exposed) of a risk factor of interest; subjects followed over time, assess devt of dz/ outcome of interest Cohort studies
Type of cohort study: investigators use historical data to reconstruct a timeline of exposure Retrospective cohort
Type of cohort study: investigators identify the original population at the beginning of the study and follow them concurrently over time Prospective cohort
Cohort study: fixed cohort Exposure groups in a cohort study represent groups that are defined at the start of the follow-up or observation period. No new members are added
Cohort study: dynamic cohort Study cohort gains members over time through in-migration, or loses members through out-migration
Best observational design to establish cause and effect relationships = cohort study
The ratio of the risk of disease among the exposed to the risk among the unexposed Relative Risk (AKA: Cumulative Incidence Ratio, Risk Ratio)
The ratio of the rate of disease in the exposed population to the rate in the unexposed population Rate Ratio (AKA: Incidence Density Ratio)
Ecologic study: Advantages Quick; Inexpensive; Hypothesis generating; Useful for establishing associations at an environmental level
Ecologic study: Disadvantages Individual exposure information is not collected; so we don’t know if those who are exposed are actually developing the dz and vice-versa
Descriptive studies: Advantages Inexpensive; Quick; Hypothesis generating; Assessment of public health interventions
Descriptive studies: Disadvantages Temporal relationship cannot be determined; Incidence (risk) of disease cannot be determined; Causal link cannot be established
Study: subjects randomly allocated to receive new active treatment, or the std tx, or a placebo. Different tx groups are then compared to assess effectiveness of the treatment Randomized clinical trial
Types of RCT Parallel, Equivalency (parallel), Crossover, Factorial, Group allocation
RCT: ____-blind trial = good for trials on very sensitive topics or subjectively measured outcomes Triple-blind
Randomization in RCT Random allocation to a grp; group in which the subject is placed is random, not the subject himself; core of a RCT
Random Selection in RCT Selecting subjects, at random, to be part of a study population; can be conducted in RCTs, but primarily in intervention and observational studies
RCT: types of randomization Simple, blocked, stratified
RCT: blocked randomization requires: a double-blind RCT design to eliminate investigator bias
RCT: Only disadvantage of blocked design: analyses become more cumbersome
If blocked randomization is not double-blinded, then: Block size pattern can randomly vary within a study (blocksize = 2, 4, 6, or 8)
Efficacy vs Effectiveness Efficacy = can it work (under lab conditions)? Effectiveness = will it work (are results generalizable to larger population)?
RCTs vs Observational study: RCTs are good for: Establishing tx efficacy; understanding effectiveness of intervention when small sample size; understanding: 1- efficacy for tx for serious/ rare dz; 2-dz pathology in a unique population
RCTs vs Observational study: Observational study good for: Understanding associations in large pops; establishing need to conduct an RCT; understanding associations of prognostic factors with dz of interest prior to RCT
Sensitivity = True Positives / (True Pos + False Negs)
Sensitivity will be higher if there are: fewer false negatives; i.e., fewer Type II errors
Specificity = True Negatives / (True Negs + False Pos)
Specificity will be higher if there are: fewer false positives; i.e., fewer Type I errors
Positive Predictive Value (PPV) = True Pos / (True Pos + False Pos)
Negative Predictive Value (PPV) = True Negs / (True Neg + False Neg)
Created by: Adam Barnard Adam Barnard