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74 Cards in this Set

  • Front
  • Back

ANCOVA

Parametric




Reject Null if F value above critical value




Analyzing covariates (independent variables) effect on dependent variables through and F test (see ANOVA)

Chi Squared

Parametric




"Goodness of Fit"




Reject Null if Chi Squared is greater than critical vlaue

Kruskal-Wallis

Non-Parametric




Reject null if KW value > Chi Squared value




Compare medians of groups within independent variable, similar to ANOVA but nonparametric

Mann Whitney U Test

Non-Parametric




Reject null if U value lower or equal to critical value




Determines differnces in median (same shape) or distribution (different shape) in data of Ivs

T Test

Parametric




Reject null if test value is greater than t-value




Compare means of groups for difference based off variability though a T-value(variation between groups/variation within groups) - similar to ANOVA but 2 groups

Wilcoxin Signed Rank Test

Non-Parametric




Reject null if test value is less that critical value




Compare sample medians - similar to T-test but nonparametric

ANOVA

Parametric




Reject null if F value above critical value




Compares means of 3+ groups for a significant difference in order to reject the null hypothesis (Ho)Use F ratio (variation between groups/variation within groups) to determine

Correlations

Parametric or Non-Parametric




Reject null if test value greater than critical value (spearman's)Magnitude and direction are used as values




Determines strength of relationship between variables

MANOVA

Parametric




Reject null if F value above critical value




Same as ANOVA but for multpile Dependent Variables

Regression

Linear and small variance from best fit




Magnitude and direction are used as values




Similar to Correlation, determines how well a variable predicts an outcome

We use a T-test to compare the data of ankle pain before intervention and ankle pain after intervention in the same subjects. What kind of T-test would we use?

Dependent Sample

What are the three main types of T-tests?

Independent Samples


Dependent Samples


One Sample

When are logistic regressions used?

When making predictions of dichotomous outcomes(dependent variable) from single or multiple variables (independentvariable).

What does the R^2 value tell us in a regression analysis?

It tells us how strongly a variable predicts an outcome

If we are testing (ANCOVA) the effectiveness of medication A on reducing headaches, what would a possible covariate be?

Stress level

The results from your Kruskal- Wallis test are similarly distributed. You can use median data. True or false?

True

Homoscedasticity

Same variance across a range of values for an independent variable

Which critical values are used for the Wilcoxon signed rank test?

W and Z

Covariate

variable that is possibly predictive of the outcome under study




Confounding Variable or interacting

Types of plaigarism

Copy and Paste


Purchased "Paper Mills"


Word Switching


Style


Metaphors/Ideas


Self Plaigarism

Don't Need to Cite...

Myths/Folklore


Historical Events


Generally Accepted Facts

Freely available EBP resources

PEDro


PubMed


Cochrane


Turning Research into Practice (TRIP)


National Guideline Clearinghouse


APTA

Elements of Prognosis

Possible Outcomes


Likelihood that outcomes will occur


Time frame for change

Research Designs for Prognosis

Cohort Designs (Outcome)


Case Control Designs (Exposure)


Cross Sectional

5 and 20 rule

Lose less than 5% is very strong


Lose more than 20% threatens validity

Crude Case Analysis

Do not take into account those that are lost to follow up




Count the 4 deaths out of 84 instead of 100 when 16 lost




4.8%

Worst Case Analysis

Assume that the patients lost to follow up got worse / died




16 lost to follow up are counted with the 4 dead out of 100 total




20%

Best Case Analysis

Assume those lost to follow up got better / Alive




Only count the 4 who died as negative outcomes




4%

Kaplan Meier Curves

Survival curves for the proportion of the sample who has NOT had a specific outcome

Only valid if they fall within the confidence interval




If they include 1, they are no better than chance

Odds Ratio


Relative Risks


Hazard Ratio

Confidence Interval shouldn't cross...

Zero

Outcome based Intervention is to...

Evidence Based Practice

Outcome Measurements Establish...

What works


How well it works


Who it works for


Economic efficiency

Outcome Measurement Research Designs

Prospective Cross-sectional or longitudinal


pro: over time


con: difficult, long, need to find baseline patients




Retrospective **Preferred


Establishes temporal sequence

Types of Secondary Data for Retrospective Outcomes Measurements

-Institutional: hospitals, clinics, heath systems (forinternal use)


-Commercial: uniform data systems, FOTO, insurance claims


-Government: National Center for Health Statistics, Agency for Health CareResearch Quality, Medicare/Medicaidh

Test-Retest Reliability

Consistencyof repeated measures separated in time, indicates stability over time

Cross Sectional Validity

Thereis a difference in scores of the measurement tool between 2 or more groups ofresponses on the reference tool during a simultaneous administration

Longitudinal Validity

Thereis a difference in scores of the measurement tool between 2 or more groups ofresponses on the reference tool administered in the future

Person Level Outcomes

Activity limitations, Quality of Life, participation restrictions




Body Function


Activity


Participation

Minimal Detectable Change (MDC)

Smallest amount of change that can be reliably detected outside of measurement Error

Minimal Clinically Important Difference (MCID)

Minimal amount of change that patients Perceive as beneficial and would justify a change in care

Health Related Quality of Life (HRQL)

Multidimensional assessment of life satisfaction as it relates to health

Reliability Statistics




Cronbach's Alpha

Internal consistency/reliability




Expected correlation of two test that measure the same construct

Reliability Statistics



Intra-class correlation (ICC) Coefficient

Reproducibility

Reliability Statistics



Kappa (K)

Agreement among repeated Scores

Reliability Statistics




Effect Size and Standardized Response Mean (SRM)

Responsiveness

Floor and Ceiling Effects

Failure to fully characterize a group of patients




Ceiling = all healthy patients score maximally


Floor = cannot differentiate between different elderly populations

Content Validity

Full content of a concepts definition

Face Validity

Appears to measure what it intends to measure

Concurrent Validity

Outcome has a high correlation with gold standard at the same time

Predictive Validity

Outcome has high correlation with future Gold Standard measure

Criterion Standard Validity

Compare to gold standard

Reference Standard Validity

Not gold standard but reasonable

Construct Validity

Ability of a tool to measure an abstract concept

Convergent Validity

Outcome correlates with another thought to measure the same construct

Discriminant Validity

Outcome does Not correlate with with a measure thought to assess a different characteristic

Clinical Prediction Rules are a cluster of symptoms that can be used to...

Improve diagnosis


Refer patients for additional testing


Establish Prognosis


Create Subgroups

Clinical Prediction Rule




Step 1

Prospective Cohort




Derivation of rule




list all possible factors 10-20


analyze which factors are most reliable

Clinical Prediction Rule




Step 2

Prospective Cohort




Validation of rule




Narrow = one clinic


Broad = multiple clinics

Clinical Prediction Rule




Step 3

Randomized Control Trial




Impact Analysis




Rule changes clinician behavior


Improves outcomes and reduces cost

Clinical Prediction Rule




Level 1

Highest Level




At least 1 prospective validation and Impact Analysis




Randomized Control Trial

Clinical Prediction Rule




Level 2

Broad Prospective Validation




Wide Geographic region




Varying confidence but no certainty

Clinical Prediction Rule




Level 3

Narrow Prospective study to validate




Add to practice with caution

Clinical Prediction Rule




Level 4

Derived but not validated




Needs further investigation

Systematic Review




Selection Bias

Cherry Picking




No good exclusion/inclusion criteria

Systematic Review




Language Bias

Neglecting none English papers

Systematic Review




Including Published and unpublished data

Should only be published




Unpublished is cheating papers in

4 types of threats to Systematic Review

Subject allocation


Groups managed differently


Outcome measures blinded


Loss of Subjects

Narrative Research

Focuses on stories of individuals

Grounded Theory Research

theory is "grounded" in participant data




Generate a theory from participant views until you saturate the model

Case Study Research

Bounded by context or setting




Non-Reproducable

Participation Action Research (PAR) Research

Photovoice




Private troubles that participants have in common

Content Analysis Research

Examination of artifacts of social communication




may count phrases or discuss symbolism

Theoretical Saturation

Data starts to repeat itself


Not hearing new information