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# EBM 1

### Evidence Based Medicine

Question | Answer |
---|---|

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