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Epi > Epi - Exam 3 > Flashcards

Epi - Exam 3 Flashcards

1
Q

What are the 3 statistical perspectives that can be taken by the researcher?

A

Statistical-Perspectives

  • Superiority
  • Noninferiority
  • Equivalency
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2
Q

What are the 3 primary levels or groupings of variables (data)?

A

Variables - Primary Levels

  • Levels:
    • Nominal
    • Ordinal
    • Interval or Ratio
  • ALL statistical tests are selected based on level of data being compared.
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3
Q

What are the 3 key attributes of data that define their level or grouping, and how are they assessed?

A

Variables - Key Attributes

  • Attributes:
    • Order/Magnitude
    • Consistency of scale / equal distances
    • Rational absolute zero
  • Each attribute is assessed with a “Yes” or “No,” ‘does the variable have it?’
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4
Q

What is a nominal variable?

A

Variables - Nominal

  • Dichotomous/binary; non-ranked; non-ordered; Named categories.
    • No - order or magnitude.
    • No - consistency of scale or equal distances (discrete).
    • Nominal variables are simply labeled-variables without quantitative characteristics (or dichotomous/binary).
  • ALL DICHOTOMOUS VARIABLES ARE NOMINAL.
  • INCLUDES CATEGORICAL VARIABLES DESPITE NUMBER OF CATEGORIES.
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5
Q

What is an ordinal variable?

A

Variables - Ordinal

  • Ordered, rank-able categories, non-equal distance.
    • Yes - order of magnitude.
    • No - consistency of scale or equal distances (discrete).
  • PAIN SCALE WILL ALWAYS BE ORDINAL ON EXAM.
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6
Q

What is an interval/ratio variable?

A

Variables - Interval/Ratio

  • Order, magnitude, and equal distances (units).
    • Interval = arbitrary zero value (0 doesn’t mean absence)
    • Ratio = Absolute (rational) zero value (0 means absence of measurement value), i.e. physiological parameters).
    • Yes - order or magnitude.
    • Yes - consistency of scale or equal distances (continuous).
      • Living siblings (number) & personal age (in years).
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7
Q

How do the levels of data vary in specificity/detail?

A

Variables - Specificity/Detail of Levels

  • After data is collected, we can appropriately go down in specificity/detail of data, but never up.
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8
Q

What are descriptive statistics?

A

Descriptive Statistics

  • Non-comparative, simple description of various elements of the study’s data.
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9
Q

What are measures of central tendency.

A

Measures of Central Tendency

  • Mode / Median / Mean
  • Min / Max / Range
  • Interquartile Range (IQR)
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10
Q

What is variance?

A

Measures of Central Tendency - Variance

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11
Q

What is standard deviation?

A

Measures of Central Tendency - Standard Deviation

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12
Q

What are the shapes of data distribution in graphical representations?

A

Shapes of Data Distribution

  • Normally distributed
  • Positively skewed
  • Negatively skewed
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13
Q

Describe the shape of normally distributed data, in graphical representations.

A

Shapes of Data Distribution - Normal

  • Symmetrical
  • Mean/median are equal/near equal.
  • Equal dispersion of curve (‘tails’) to both sides of mean.
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14
Q

What are parametric tests?

A

Stats Tests - Parametric

  • Stats tests useful for normally-distributed data.
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15
Q

Describe the shape of positively skewed data, in graphical representations.

A

Shapes of Data Distribution - Positively Skewed

  • Asymmetrical distribution with one ‘tail’ longer than the other.
  • Mean > median = ‘positive skew’.
    • “>” points right so tail points to right.
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16
Q

If the median differs from the mean, what does that mean for data distribution?

A

Shapes of Data Distribution

  • The distribution is skewed.
    • Mean > median = positive skew
    • Mean < median = negative skew
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17
Q

Describe the shape of negatively skewed data, in graphical representations.

A

Shapes of Data Distribution - Negatively Skewed

  • Asymmetrical distribution with one ‘tail’ longer than the other.
  • Mean < median = negatively skewed.
    • “<” points left so tail points to left.
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18
Q

What is skewness?

A

Shapes of Data Distribution - Skewness

  • A measure of the asymmetry of a distribution.
  • Perfectly-normal distribution = skewness of 0.
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19
Q

What is kurtosis?

A

Shapes of Data Distribution - Kurtosis

  • A measure of the extent to which observations cluster around the mean.
  • Kurtosis statistic:
    • 0 = normal distribution.
    • Positive = more cluster.
    • Negative = less cluster.
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20
Q

What percentages coincide with standard deviation ranges?

A

Standard Deviation - Percentages

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21
Q

For proper selection of a parametric test, what are the required assumptions?

A

Parametric Test - Selection

  • Required assumptions:
    1. Normally-distributed
    2. Equal variances
      • Multiple tests available to assess for equal variances between groups.
        • Levene’s test.
    3. Randomly-derived & independent.
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22
Q

How is interval data, that is not normally-distributed, handled?

A

Interval Data - Not Normally-Distributed

  1. Use a statistical test that does not require the data to be normally-distributed (non-parametric tests), or
  2. Transform data to a standardized value (z-score or log transformation), hoping it will allow the data to be normally distributed.
  • ALWAYS RUN DESCRIPTIVE STATISTICS & GRAPHS.
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23
Q

What is the null hypothesis?

A

Null Hypothesis (H0)

  • A research perspective which states there will be no true difference between the groups being compared.
    • Either accepted or not accepted, based on statistical analysis.
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24
Q

What is type 1 error?

A

Type 1 Error

  • A.K.A. - alpha
  • Not accepting H0 when it is actually true, and you should have accepted it!
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25
Q

What is type 2 error.

A

Type 2 Error

  • A.K.A. - beta
  • Accepting H0 when it is actually false, and you should have not accepted it!
26
Q

In reference to statistical significance, what is power?

A

Power

  • The statistical ability of a study to detect a true difference, IF one truly exists between comparison groups.
    • The level of accuracy in correctly accepting / not accepting H0.
  • Increases with a larger sample size.
27
Q

How is sample size determined?

A

Sample Size

  1. Minimum difference between groups deemed significant:
    • The smaller the difference necessary to be significant = the larger the N needed.
  2. Expected variation of measurement (known or estimated from past studies/population).
  3. Type 1 & type 2 error rates & confidence interval (usually 90%-99%)
  • ADD IN ANTICIPATED DROP-OUTS OR LOSS TO FOLLOW-UPS.
28
Q

What is a p value?

A

p Value

  • Statistical tests determine possible error-rate or liklihood of chance in comparing difference or relationship between variables.
    1. A statistical test critical value is calculated.
    2. Test statistic value is compared to the appropriate table of probabilities for that test.
    3. A probability (p) value is obtained; based on the probability of observing, due to chance alone, a test statistic value as extreme or more extreme than actually observed if groups were similar (not different)
      • The p-value is selected by investigators before the study starts (a priori).
29
Q

Describe the statistical significance of the p value.

A

p Value - Statistical Significance

  • If p value is lower than the pre-selected alpha value (customarily 5% (0.05)), then we say it is statistically significant.
    • This means that the risk of experiencing a type 1 error is acceptably low if we reject H0.
30
Q

How is a pre-set p value interpreted?

A

Pre-Set p Value - Interpretation

  • The probability of:
    • making a type 1 error if the H0 is rejected.
    • erroneously claiming a difference between groups when one does not really exist.
    • obtaining group differences as great or greater if the groups were actually the same/equal.
    • obtaining a test statistic as high/higher if the groups were actually the same/equal.
31
Q

How do you interpret and explain a p-value?

A

p Value - Interpretation and Explanation

  • Explain it as you would a risk ratio with type of change and magnitude between groups, adding whether or not the data is statitstically significant or not statistically significant.
32
Q

What is Levene’s test?

A

Levene’s Test for Equality of Variances

  • Used to assess the equality or homogeneity of variances.
  • Independent t-tests assume homogeneity of variances.
  • Interpretation:
    • >0.05 means variances are NOT significantly different, thus the variances are similar.
    • 0.05 or less means variances are significantly different and thus there is no equality or homogeneity of variances.
33
Q

What is a confidence interval?

A

Confidence Interval (CI)

  • Most common selections are 90%, 95%, or 99%.
  • Interpretation:
    • We are 95% confident that the “true” difference or relationship between the groups is contained within the confidence interval range.
    • Without p value:
      • If CI crosses 1.0 (for ratios) or 0.0 (for absolute differences) = NOT significant.
  • Calculated at an a priori percentage of confidence, including a high and low value, that statistically includes the real (yet unknown) difference or relationship being compared.
  • Based on V/SD and N.
  • Journals are moving away from solely reporting p values; or even showing them at all.
34
Q

What are the 4 key questions to ask when selecting the correct statistical test?

A

Statistical Test Selection - Questions for Selection

  1. What data level is being recorded?
    • Does data have order/magnitude?
    • Does data have equal, consistent distances along the entire scale?
  2. What type of comparison/assessment is desired?
    • Correlation -> correlation test
    • Event-occurrence / time-to-event -> survival test
    • Outcome prediction/association (OR) -> regression
  3. How many groups are being compared?
  4. Is the data independent or related (paired)?
    • Data from the same (paired) or different groups (independent).
  • Questions 2-4 get you around the other portions of each individual sheet.
35
Q

What is a correlation test?

A

Statistical Tests - Correlation Test

  • Correlation (r) - provides a quantitative measure of the strength & direction of a relationship between variables.
    • Range from -1.0 - +1.0.
    • -1.0 = strong negative correlation.
    • 0.0 = no correlation.
    • +1.0 = strong positive correlation.
  • Partial correlation - correlation that controls for confounding variables.
  • Types:
    • Nominal correlation test = Contingency Coefficient.
    • Ordinal correlation test = Spearman Correlation
    • Interval correlation test = Pearson Correlation
      • p>0.05 for a Pearson Correlation just means there is no linear correlation; may still be a non-linear correlations present.
  • All correlations can be run as a “partial correlation” to control for confounding.
36
Q

What is a survival test?

A

Statistical Tests - Survival Test

  • “Changes over time.”
  • Compares the proportion of events over time, or time-to-events, between groups.
  • Commonly represented by a Kaplan-Meier curve (all types can be represented by a Kaplan-Meier curve).
  • Types:
    • Nominal survival test = Log-Rank test
    • Ordinal survival test = Cox-Proportional Hazards test
    • Interval survival test = Kaplan-Meier curve
37
Q

What is a regression?

A

Statistical Test - Regression

  • “Predict likelihood of some outcome” = regression test.
  • Provide a measure of the relationship between variables by allowing the prediction about the dependent, or outcome, variable (DV) knowing the value/category of independent variables (IVs)
  • Basically, helps to determine how well variables can predict outcome.
  • Also able to calculate OR for a measure of association.
  • Types:
    • Nominal regression test = Logistic Regression
    • Ordinal regression test = Multinominal Logistic Regression
    • Interval regression test = Linear Regression
38
Q

What is a partial correlation, and how is it used to control for confounding?

A

Partial Correlation

  • Correlation that factors in the confounders, trying to keep them “mathematically quiet” to get a better idea of true correlation.
  • Can be used to validate correlation test result by controlling for possible confounders.
  • Example: Spearman’s returns small, but statistically significant, positive correlation. Partial returns even smaller postive correlation that is statistically not significant. Therefore initial correlation was not true.
39
Q

What is the Spearman Correlation test?

A

Correlation - Spearman Correlation Test

  • Correlation test for ordinal data.
40
Q

What is the Contingency Coefficient test?

A

Correlation - Contingency Coefficient Test

  • Correlation test for nominal data.
41
Q

If you want to compare frequencies, counts, or proportions between the groups, what determines which tests are used?

A

Statistical Tests - Comparing Frequencies/Counts/Proportions

  • Depend on 3 things:
    • Type of data.
    • Number of groups.
    • Relationship:
      • Independent data
      • Paired/related data
        • Pre- vs. Post, Before vs. After, Baseline vs. End
42
Q

What statistical tests are used to compare independent, nominal data and what are they comparing?

A

Independent Nominal Data - Statistical Tests

  • 2 groups:
    • (Pearson’s) Chi-square test
  • 3 groups:
    • Chi-square test of independence
      • Both tests compares group proportions and if they are different from that expected by chance.
      • Assumptions-Both test:
        • Usual chi-square (binomial; non-normal) distribution for nominal data.
        • No cell count < 5 observations.
    • For statistically significant findings (p<0.05) in 3 or more comparisons, one must perform subsequent analysis (post-hoc testing) to determine which groups are different:
      • Multiple chi-square tests are NEVER acceptable, risk of Type I error increases with each additional test (almost guaranteed after 4-5).
  • 2 or more groups with expected cell count of < 5:
    • Fisher’s Exact test
  • Tests compare frequencies/counts/proportions.
43
Q

What keywords indicate that data is paired or related?

A

Paired/Related - Keywords

  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
44
Q

If given both chi-square and Fisher’s Exact, what is the only situtation in which that can occur?

A

When the expected cell count is < 5. If you can confirm cell count < 5, then the chi-square is no longer valid and Fisher’s Exact is the right answer.

45
Q

What statistical tests are used to compare paired/related nominal data and what are they comparing?

A

Paired/Related Nominal Data - Statistical Tests

  • 2 groups of paired/related data:
    • McNemar test
  • 3 or more groups of paired/related data:
    • Cochran
      • Same as principle and assumptions as chi-square yet mathematically factors in concept of paired, or related, data.
      • Bonferroni test of inequality (Bonferroni correction)
        • Adjusts the p value for # of comparisons being made (very conservative).
  • Tests compare frequencies/counts/proportions.
  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
46
Q

What statistical tests are used to compare independent, ordinal data and what do they compare?

A

Independent Ordinal Data - Statistical Tests

  • 2 groups of independent data:
    • Mann-Whitney test
  • 3 or more groups of independent data:
    • Kruskal-Wallis test
  • The tests compare the median values between groups.
    • Both also used for interval data not meeting parametric requirements.
    • If 3+ group comparison significant, must perform a post-hoc test to determine where difference(s) is(are).
47
Q

What statistical tests are used to compare paired/related, ordinal data and what do they compare?

A

Paired/Related Ordinal Data - Statistical Tests

  • 2 groups of paired/related data:
    • Wilcoxon Signed Rank test.
  • 3 or more groups of paired/related data.
    • Friedman test
  • Tests compares the median values between groups.
    • Each effective for non-normally distributed interval data or don’t meet all parametric requirements.
    • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
48
Q

What are the post-hoc tests used for ordinal data?

A

Ordinal Data - Post-Hoc Tests

  • Student-Newman-Keul test
    • All groups MUST be equal in size.
    • Compares all pairwise comparisons possible.
  • Dunnett test
    • Compares all pairwise comparisons against a single control.
    • All groups MUST be equal in size.
  • Dunn test
    • Compares all pairwise comparisons possible.
    • Useful when all groups are NOT of equal size.
49
Q

What statistical tests are used to compare independent, interval data and what do they compare?

A

Interval Data - Statistical Tests

  • Independent Data:
    • 2 groups of independent data:
      • Student t-test.
    • 3 or more groups of independent data:
      • Analysis of variance (ANOVA)
    • Tests compare the means of all groups (along with intra- and inter-group variations) against a dependent variable.
      • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • 3 or more groups of independent data w/ confounders:
    • Analysis of Co-Variance (ANCOVA)
      • Compares the means of all groups (along with intra- and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders.
50
Q

What statistical tests are used to compare paired/related, interval data and what do they compare?

A

Paired/Related Interval Data - Statistical Tests

  • 2 groups of paired/related data:
    • Paired t-test.
      • Compares the mean values between groups that are related.
  • 3 or more groups of paired/related data:
    • Repeated Measures ANCOVA (1 DV)
      • Compares the means of all groups (along with intra- and inter-group variations) of related data against a dependent variable.
        • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • 3 or more groups of paired/related data w/ confounders:
    • Repeated Measures ANOVA:
      • Compares the means of all groups (along with intra- and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders.
51
Q

What are the post-hoc tests used for interval data?

A

Interval Data - Post-Hoc Tests

  • Student-Newman-Keul test
    • All groups MUST be equal in size.
    • Compares all pairwise comparisons possible.
  • Dunnett test
    • Compares all pairwise comparisons against a single control.
    • All groups MUST be equal in size.
  • Dunn test
    • Compares all pairwise comparisons possible.
    • Useful when all groups are NOT of equal size.
  • Tukey or Scheffe tests
    • Compares all pairwise comparisons possible
    • All groups must be equal in size.
      • Tukey test slightly more conservative than the Stu.N.K.
      • Scheffe test less affected by violations in normality and homogeneity of variances - most conservative.
  • Bonferroni correction
    • Adjusts the p value for # of comparisons being made (very conservative).
52
Q

What is the Kappa test?

A

Kappa Tests

  • Kappa statistic - a correlation test showing relationship or agreement between evaluators (consistency of “decisions”, “determinations”).
  • Kappa interpretation:
    • Kappa (K) value can be + or -; + = good agreement; - = poor agreement.​
    • +1 = the observers perfectly “classify” everyone exactly the same way.
    • 0 = There is no relationship at all between the observers’ “classifications,”above the agreement that would be expected by chance.
    • -1 = The observers “classify” everyone exactly the opposite of each other.
53
Q

What is another word for correlation?

A

Relationship

54
Q

How is research tracked before completion/publication?

A

Research Tracking

  • Must be regsitered on clinicaltrials.gov and is assigned a NCT#.
  • Reduces publication bias by helping to prevent failure to print due to nonfavorable outcomes.
55
Q

What is needed to accurately assess a study?

A

Study Assessment

  • To accurately assess a study, readers need complete, clear, and transparent information on the study’s methodology and findings.
  • Historically, unfortunately, adequate and thorough assessments (by readers) of published studies can be challenging because authors may neglect to provide lucid and complete descriptions of that critical, necessary information.
56
Q

Describe the CONSORT checklist.

A

Checklists - Interventional Studies

  • CONSORT (Consolidated Standards Of Reporting Trials) - randomized interventional trials.
  • May have extension documents for:
    • Non-inferiority trials.
    • Equivalence trials.
    • Cluster trials.
    • Pragmatic trials.
57
Q

What are the checklists for analyzing studies?

A

Study Analyses - Checklist

  • CONSORT (Consolidated Standards Of Reporting Trials)
    • Randomized interventional trials.
  • PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
    • Systematic reviews of multiple randomized trials.
  • STROBE (Strengthening the Reporting of Observational Studies in Epidemiology)
    • Observational studies (cohort, case-control, cross-sectional)
  • TREND (Transparent Reporting of Evaluations with Non-randomized Designs)
    • Reporting evaluations with non-randomized designs of behavioral and public health interventions.
  • REMARK (REporting Recommendations for Tumor MARKer Prognostic Studies)
    • Tumor marker prognostic studies.
  • GRIPS (Genetic RIsk Prediction Studies)
    • Genetic risk prediction studies.
  • STARD (STAndards for the Reporting of Diagnostic Accuracy Studies)
    • Diagnostic studies.
  • QUADAS-2 (QUality Assessment of Studies of Diagnostic Accuracy in Systematic Reviews (2nd ed.))
    • Systematic reviews of multiple diagnostic studies.
58
Q

Describe the STROBE checklist.

A

Study Analysis - STROBE Checklist

  • STROBE (Strengthening the Reporting of Observational Studies in Epidemiology)
  • For observational studies (cohort, case-control, cross-sectional).
  • May have extension documents:
    • Molecular epidemiology studies (STROBE-ME).
    • Genetic association studies (STREGA)
      • STrengthening the REporting of Genetic Association studies.
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