Research Evaluation Exam Flashcards by Andrew Damon

Physician Assistant Competencies:

Patient care, Professionalism, Systems-based practice.

Reasons for Research Evaluation

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Make decisions about diagnostic and therapeutic interventions based on patient information and preferences, current scientific evidence, and informed clinical judgment.

Patient Care

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Commitment to excellence and on-going professional development.

Professionalism

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Partner with supervising physicians, health care managers, and other health care providers to assess, coordinate, and improve the delivery and effectiveness of health care and patient outcomes.

Systems-based practice

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Analyze practice experience and perform activities using a systematic methodology.
Locate, appraise, and integrate evidence from scientific studies - Study designs and statistical methods
Utilize information technology to manage information, access medical information, and support their own education
Recognize and appropriately address personal biases,

Practice-based Learning and Improvement

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The practice of health care in which the practitioner systematically finds, appraises, and uses the most current and valid research findings as the basis for clinical decisions.

Evidence based practice

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Results in the best possible outcome for your patients.

Integration of research evidence and clinical experience

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Original study design

Primary Literature

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Databases Point of Care resources
Up To Date
MD Consult
Epocrates
Lexicomp

Secondary Literature

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Primary (Analytic) Studies
Experimental
Observational Secondary (Integrative) Studies

Medical Literature

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Those that report original research.

Experimental
Observational

Primary (Analytic) Studies

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An intervention is made or variables are manipulated.

Example: experiment, randomized controlled trial, non-randomized controlled trial

Primary (Analytic) Studies Experimental

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No intervention is made and no variables are manipulated.

Example: cohort, case-control, cross-sectional, descriptive, surveys, case reports, etc.

Primary (Analytic) Studies Observational

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Author’s peers and recognized researchers in the field read and evaluate a paper (article) submitted for publication.
Articles/scholarly journals accepted meet the discipline’s expected standards of expertise and passed through this review process.

Peer Review

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Authors ->
Authors submission ->
Editor ->
Peers ->
Peer comments ->
Editor roll-up comments ->
Author -> Repeat process

Peer Review Process

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A journal’s impact factor for a particular year

Impact score

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Total number of times its articles were cited during the two previous ➗ Total number of citable articles in the journal during those two years.

Calculation of Impact Score

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Sources for Literature (credible)

PubMed
Google Scholar
UpToDate
Medscape
DoD/Va Clinical Practice Guidelines

Finding Literary Sources

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Is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control health problems.

Epidemiology

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Reduce morbidity and mortality from disease
Extent of disease
Evaluate and develop preventative and therapeutic care
Develop policy

Objectives of Epidemiology

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Critical to public health and clinical practice to determine information

Clinical Practice & Epidemiology: Importance

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Multi-step process:

Determine whether an association exists between an exposure or an outcome.
If there is an association…is it causal?
Derive appropriate inferences about a possible causal relationship from the patterns are found.

Epidemiologic approach

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The branch of statistics that deals with data relating to living organisms.

Biostatistics

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Tools of statistics to help answer pressing research questions in medicine, biology, and public health

Importance of Biostatistics

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Methods, Materials & Patients: What methods are used to evaluate medical literature?

* Study design * Subject selection procedures * Method of measurement * Description of analytic techniques

* Read only what is interesting and useful * Scan the article to gain a quick overview * Concentrate on the methods section * Reserve the right for final judgment

Tips for Evaluating Medical Literature

* Observes associations * Show patterns of disease occurrence * Helps to generate hypotheses

Descriptive research

- Analyzes associations - Investigates relationships - Tests hypotheses

Analytic research

- Descriptive epidemiologic studies reveal the patterns of disease occurrence in human populations. - Provide general observations concerning the relationship of disease to basic characteristics. **IE**: Person, Place, Time

Descriptive Studies

Examples: Case Reports Clinical Series Populations (Ecologic Studies)

Descriptive Studies

Descriptive Study or Analytic Study? Alerting the medical community to what types of persons are at risk for a new…..or old disease?

Descriptive Studies

Descriptive Study or Analytic Study? Assisting in the rational planning of health and medical care needs: I.e.. How many PAs do we need in the field?

Descriptive Studies

Descriptive Study or Analytic Study? Provide clues to disease etiology and questions or hypotheses for further study.

Descriptive Studies

Attempt to provide insight into etiology or find/ determine better patient outcomes. * Experimental * Observational

Explanatory Studies

Has an active intervention from the investigator.

Explanatory Studies - Experimental

Examples of \_\_\_\_\_\_\_\_\_\_\_\_\_ * Controlled trial * Clinical trial * Educational intervention * Healthcare trial * Intervention trial

Explanatory Studies - Experimental

Investigator observes nature

Explanatory Studies - Observational

Examples of \_\_\_\_\_\_\_\_\_\_\_\_\_ * Case-control * Follow-up * Cross-sectional * Cohort or follow up

Explanatory Studies - Observational

Descriptive Study or Analytic Study? * Experimental * Clinical Trial * Community Trial * Educational Intervention

Analytic Study

Descriptive Study or Analytic Study? * Observational * Case-control * Cohort (follow up) * Cross-sectional

Analytic Study

* A narrative in the professional literature that identifies a single incident and discusses pertinent factors related to the patient * Brings a novel or unusual patient to the attention of colleagues * Information is preliminary and unrefined in terms of research methodology

Case Report

As the name implies, this type of study analyzes a number of individual cases that share a commonality Usually relatively low numbers of subjects

Case Series

- Examine adverse events or effects - Catalog new diseases or outbreaks - Determine the feasibility or safety of a new treatment or intervention - Discuss the potential efficacy of a new treatment

Case Series are used to:

Case reports and case series lack?

Sufficient methodological rigor

- Data does not necessarily extrapolate to larger populations - Evidence may be circumstantial - Confounding factors may be present - But – both typically indicate the need for further study

Case reports and case series lack...

- Examine the relationship between exposures and diseases as measured in a population r - Utilizing data from surveys or registries - Followed by an analytic study to see if the association holds true in individuals.

Descriptive study design - Ecologic Studies

Is a type of bias specific to ecological studies. Occurs when relationships that exist for groups are assumed to also be true for individuals.

Ecological Fallacy

Attempt to provide insight into etiology or find/ determine better patient outcomes: - Experimental - Observational

Explanatory Studies

Has an active intervention from the investigator. Examples: - Controlled trial - Clinical trial - Educational intervention - Healthcare trial - Intervention trial

Explanatory Studies - Experimental

Investigator observes nature. Examples: - Case-control - Follow-up - Cross-sectional - Cohort or follow up

Explanatory Studies - Observational

- Examines the relationship between outcomes and other variables of interest at one particular time. - Determines prevalence (% of population) not incidence (rate) - Enrolls a large number of individuals - Cannot show causality, does not separate cause/effect - Does not establish a temporal relationship between risk factors and disease because they are measured at the same time

Cross-sectional studies

Strengths of __________ - Can assess multiple outcomes and exposures simultaneously - Can be completed quickly - Data generated can lead to further studies - Can generate prevalence

Cross-sectional studies

Limitations of __________ - No time reference: “Snapshot In Time”- like looking at a photograph - Only useful for common conditions - Cannot calculate incidence, it is a prevalence study Results are dependent on the study population

Cross-sectional studies

- Studies specific condition (cases) and compares with people who do not have the condition (controls). - The researcher looks back to identify factors or exposures - Design may follow a case-series (as a retrospective look at causes). - Captures the cause and effect relationship by comparing the frequency of a risk factor among those how are exposed and not-exposed.

Case-control studies

Strengths of __________ - Good for studying rare outcomes - Can evaluate many exposures - Ideal for initial, explanatory idea - Simple & fast – we already know the outcomes - Efficient-no waiting for outcome to occur - Inexpensive

Case-control studies

Limitations of __________ - Single outcome - High risk for bias - High risk for confounding variables - Other factors may exist those influence outcomes - Can’t determine the prevalence - Temporality - Can’t make causal interpretations - Can’t determine the incidence - Can’t calculate Relative risk

Case-control studies

Selection Bias: the inappropriate selection of cases or controls.

Case-control studies bias

\_\_\_\_\_\_\_\_\_\_ bias: Can be selected from a variety of sources: Hospitals, Clinics, Registries. If cases are selected from a single source, and risk factors from that facility may not be generalizable to all patients with that disease.

Case-control studies Selection bias - Case

\_\_\_\_\_\_\_\_\_\_ bias: come from the same reference population that cases are derived from. An inappropriate control group can have the opposite effect and obscure an important link between disease and its cause

Case-control studies Selection bias - Control

\_\_\_\_\_\_\_\_\_\_ bias: Recall Bias (Subject Bias) is the main form of information bias in case-control studies. Occurs when there is a differential recall of exposure between cases and controls.

Case-control studies Information Bias

\_\_\_\_\_\_\_\_\_\_ bias: Occurs when the researcher/observer evaluates cases vs controls differentially.

Case-control studies Researcher/Observer Bias

\_\_\_\_\_\_\_\_\_\_ bias: Arises when case subjects who think they have been exposed to responds at a higher rate to controls.

Case-control studies Voluntary Reponses Bias

\_\_\_\_\_\_\_\_\_\_ is the process of selecting the controls so they are similar to the cases in certain characteristics, such as age, race, sex, socioeconomic status, and occupation. - Individual - Group-based

Control Biases: Matching

Individual or Group Matching? For each case selected for the study, a control is selected who is similar to the case in terms of the specific variable

Control Biases: Matching Individual

Individual or Group Matching? Select controls with a certain characteristic that is identical to the proportion of cases with same characteristic.

Control Biases: Matching Group-Based

- If you select too many matching characteristics it is difficulty to find an appropriate control. - You lose the ability to study a matched variable.

Control Bias: Problems

Offers independent estimates of exposure among different samples of non-cases. Increases strength of the study.

Control Bias: Mutliple Controls

Case-Control Study: __________ A variant of a case-control study Each case becomes their own individual control Used for transient exposures during a discrete occurrence

Case-Control Study: Case-crossover

Case-Control Study: __________ Large cohort, large enrollment studies Controls are a sample of individuals who are at risk for the disease/outcome at the time each case of the disease develops.

Case-Control Study: Nested

Case-Control Study: __________ Same as nested case-control design, expect controls are randomly chosen from the cohort at the beginning of the study

Case-Control Study: Case-Cohort

a group of people who share a common characteristic or experience and all remain in the group for a period of time

Cohort

a group of people who share a common characteristic or experience and all remain in the group for a period of time

Cohort Study

identify a group of patients who are already taking a particular treatment or have an exposure, follow them forward over time, and then compare their outcomes with a similar group that has not been affected by the treatment or exposure being studied

Cohort Study: Prospective

start with a cohort and go back in time to evaluate past exposures to risk factors

Cohort Study: Retrospective

Strengths of __________ - May study multiple effects of a single exposure - Can identify a temporal relationship between exposure and disease (outcome) - Help confirm cause and effect of disease and the magnitude of the effect - Can measure incidence (rate) of disease - Calculate Relative Risk - Highest validity of observational study design

Cohort studies

Limitations of __________ - Expensive and time consuming - Inefficient for studying rare diseases: Case-control more appropriate for rare diseases - Lose participants to follow-up - Risk of confounding variables - Retrospective studies require presence of records or recall

Cohort studies

Cohort or Case-Control Summary? Start with exposure, look for disease Prospective or retrospective Common diseases High risk for drop out $$$

Cohort Study Summary

Cohort or Case-Control Summary? Start with disease, look for exposure Retrospective Rare disease Recall and selection bias $

Case-Control Study Summary

- prevent any potential biases on the part of the investigators from the influencing the assignment of participants into different treatment groups. - generates random assignments - subject has an equal chance of being assigned to each group (control or intervention) - strives for comparability of the different treatment groups; however, its not guaranteed.

Randomized control studies

Randomized __________ implies: - Specified hypotheses - Primary and secondary endpoints to address hypotheses - Methods for enrollment and follow up - Eligibility/Exclusionary criteria - Rigorous monitoring - Analysis plans and stopping rules

Randomized control studies Controlled

- Criteria for determining selection - Ensure that participants actually have the disease of interest. - Carefully select sample based on a reference population

Enrollment

Stratifying our study population by each variable that we consider important, and then randomize participants to treatment groups within each stratum.

Allocation: How is randomization accomplished:

How is randomization accomplished?

- Computer programs - Envelope System

The treatment assignment that is designated by a Random number is written on a card, and this card is placed inside an envelope. Each envelope is labeled on the outside: Patient 1, Patient 2, Patient 3…..etc. When the first patient is enrolled and consented the investigator opens the envelope and the treatment assignment is determined

Envelope System

Comparable measurements in all study groups - Improvement - Side Effects or Adverse Reaction

Outcome

- concealment of group allocation from one or more individuals involved in a clinical research study. - compare two or more types of interventions.

Randomized control studies: Blinding

After being observed for a certain period of time on one therapy; any changes are measured; patients are switched to the other therapy. - Must have a washout period!

Crossover: Planned

- Occurs when subjects who are randomized cross-over to the other group. - Lost the benefits of randomization. - Current practice to perform the analysis by intention to treat-according to the original randomized assignment

Crossover: Unplanned

Blinding Type: Single, Double, Triple? - Allocation is concealed from only one group - Researchers OR Subjects

Single Blinding

Blinding Type: Single, Double, Triple? - Allocation is concealed from both groups - Researchers AND subjects)

Double Blinding

Blinding Type: Single, Double, Triple? Allocation is unknown to the subjects, the individuals who administer the treatment or intervention, and the individuals who assess the outcomes.

Triple Blinding

Subjects may agree to be randomized, but following randomization they may not comply with the assignment treatment

Noncompliance

study results will be to reduce any observed Differences, because the treatment group will include some who did not receive the therapy, and the no-treatment group may include some who received the treatme

Net Effect of non-compliance

What is the Gold-Standard

Double-blinded Randomized Control Trial

Limitations of \_\_\_\_\_\_\_\_\_\_ * Large trials (may affect statistical power) * Long term follow-up (possible losses) * Compliance - Expensive * Possible ethical questions * Primum Non Nocere / ‘First Do No Harm’

Randomized control studies

Strengths of __________ - Minimizes the chance for bias if randomization and blinding are done correctly

Randomized control studies

Attempted to learn if the drug, surgical procedure, or administrative program works under ideal circumstance

Efficacy Trial

Within the confines of the study, results appear to be accurate and the interpretation of the investigators I supported

Internal Validity

Ability to apply results obtained from a study population to a broader population

External Validity/ Genealizibiity

Quasi-experiments, know as __________ study?

Non-randomized control studies

The control group is predetermined (without random assignment) and compared to a control group. - Volunteer to join the study OR - Are geographically close to the study site OR - Conveniently turn up (at a clinic, school) while the study is being conducted - Like the studies recruiting with fliers posted in the restroom

Non-randomized control studies

Non-randomized control studies vs. RCS - inexpensive - not feasible to randomize all evaluation - Ethical problems may be perceived to occur in health services evaluation studies - By the time RCS are completed and data analyzed results may not be relevant anymore

Non-randomized control studies

Limitations of __________ - Study group characteristics may not be balanced at baseline - Baseline differences between groups may confound the study’s results - Typical confounding variables include age, educational level, motivation, severity of illness, socio-economic status, income, comorbidities...etc

Non-randomized control studies

Four major areas of __________ concern? Enrollment Allocation Follow Up Analysis

Ramdomized & Non-randomized Control Studies Methodological concern

How was the study population chosen? - Inclusion vs. Exclusion - Representation - Controls similar to case subject

Ramdomized & Non-randomized Control Studies Methodological concern - Enrollment

How were the subjects assigned to their study group? - Randomization and binding conducted - Consecutive or convenience

Ramdomized & Non-randomized Control Studies Methodological concern - Allocation

How long are patients followed for? Adherence: - How well did the study subjects adhere to the treatment protocol? - Was the intervention too difficult to continue to participate? Attrition - How many subjects were lost to follow-up? - Why were they lost? Did they quit or die?

Ramdomized & Non-randomized Control Studies Methodological concern - Follow Up

How was the data collected analyzed? - Are the effects of the intervention clearly defined? - Is there a clearly defined end point? - Did they provide an “intent to treat” analysis? - To what did they compare their data?

Ramdomized & Non-randomized Control Studies Methodological concern - Analysis

- Designs that summarize the work of other studies - Takes the results of a large numbers of primary research studies and combines them into one - Synthesis a great deal of research!

Systematic Reviews and Meta-analyses

- Thorough, comprehensive, and explicit way of interpreting the medical literature. - Appraise and synthesize all the empirical evidence to answer a given research question!

Systematic Reviews

- Statistical approach to combine the data derived from several selected studies - A method for combining data from several selected studies that are similar enough to develop a single conclusion that has greater statistical power - The conclusion is statistically stronger than any single study

Meta-analyses

What is CPG?

Clinical Practice Guidlines

Methodology of __________ Review 1. State objectives and outline eligibility criteria 2. Search for trials that meet criteria 3. Establish methods for assessing methodological quality of each study 4. Apply eligibility criteria and justify exclusions 5. Assemble the most complete collection possible 6. Analyze results using synthesis of data 7. Compare alternate analyses, if necessary 8. Prepare a critical summary of the review 9. Restate aims, methods, and results

Systematic Review Methodology

Strengths of __________ Review - Exhaustive review - Less costly Less time required than conducting a new study - Results can be generalized and extrapolated more broadly than individual studies - More reliable and accurate than individual studies - Considered an evidence-based resource

Systematic Review

Limitations of __________ Review - Very time and labor consuming - May not be easy to combine studies

Systematic Review

Strengths of __________ Review - Greater statistical power - Confirmatory data analysis - Greater ability to extrapolate to the general population

Meta-analyses Review

the “average” – sum of the set divided by the number in the set

Measures of Central Tendency Mean

The middle point (arrange the data smallest to largest, then find the middle point) The point/score at which 50% of scores fall below it and 50% fall above it

Measures of Central Tendency Median

The score that occurs most frequently in a set of data May have two most common values = “bimodal distribution” Most frequently occurring value This is the most general and least precise measure of central tendency

Measures of Central Tendency Mode

Quantifies the amount of variability, or spread, around the mean of the measurements.

Standard Deviation vs. Variance (σ2 ) Variance (σ2 )

A measure of variation of scores about the mean. The “average distance” to the mean Used more frequently than the variance Has the same units as the measurements of the mean. When comparing two groups, the group with the larger standard deviation exhibits a greater amount of variability (heterogeneous) while the groups with smaller deviation has less variability (homogeneous).

Standard Deviation vs. Variance (σ2 ) Standard Deviation

Take each difference from the mean, square it, and then average the result.

Calculate Variance

- take the √ of the variance - "Bell Curve"

Calculate Standard Deviation

Empirical rule for data used in \_\_\_\_\_\_\_\_\_\_?

Standard Deviation Bell Shaped Curve Analysis

\_\_\_\_\_\_\_\_\_\_ Rule 68-95-99 - only applies to a set of data having a distribution that is approximately bell-shaped

Standard Deviation Empirical rule for data

\_\_\_\_\_\_\_\_\_\_ Rule 68-95-99 - only applies to a set of data having a distribution that is approximately bell-shaped

Standard Deviation Empirical rule for data

68-95-99 Approximately 68% of all scores fall with 1 standard deviation of the mean Approximately 95% of all scores fall with 2 standard deviations of the mean Approximately 99.7% of all scores fall with 3 standard deviations of the mean

Empirical rule for data

Evaluates the strength of linear relationships or associations between variables Looks at correlation: How strongly is one variable related to another?

Scatterplot

Direct or inverse relationship? * X increases and Y increases = positive correlation * X increases and Y decreases = negative correlation * 0 is no correlation

Scatterplot Correlation

The absolute value of the coefficient (its size, not its sign) tells you how strong the relationship is between the variables.

Correlation Coefficient “r ” (rho)

Tells us how strongly two variables are related ◦“r” can not be \> 1 or \< -1 ◦Closer to -1 or +1: the stronger the relationship ◦Closer to 0 : the weaker the relationship

Correlation Coeffiient Relationtionship

The most common measure of association. Results can be misleading if the relationship is non-linear Pearson’s correlation is very sensitive to outlying values

Pearson Correlation

A non-parametric version of Pearson’s correlation. The calculation is based on the ranks of the data points of the x and y values

Spearman Correlation

The statement that establishes a relationship between variables being assessed * Example: In a clinical trial the hypothesis states the new drug is better the placebo “research hypothesis” Stating the expected relationship between independent and dependent variables. If there IS a statistically significant difference, then the researchers “ACCEPT” the alternative hypothesis and “REJECT” the null hypothesis.

Alternative hypothesis (Ha or H1)

The statement of no difference or no relationship between the variables * Example: In a clinical drug trial the null hypothesis states that the new drug is no better than placebo If there IS a statistically significant difference, then the researchers must “REJECT” the Null hypothesis If there IS NOT a statically significant difference, then the researchers must “RETAIN” or “FAIL to REJECT” the Null hypothesis.

Null hypothesis (Ho)

Rejection error or an α error. ◦Made if we reject the null hypothesis when null hypothesis is true. ◦The probability of make a type I error is determined by the significance level of the test.

Hypothesis Error Type I error

Hypothesis Error

An acceptance error or an β error ◦made if we fail to reject null hypothesis. ◦The probability of committing a type II error is represented by the Greek letter β.

Hypothesis Error Type 2 error

◦The probability of finding an effect ◦The probability of correctly rejecting the null hypothesis ◦The probability of seeing a true effect if one exists ◦Designers of studies typically aim for a power of 80% or 0.8 ◦Implies there is an 80% chance of getting it right ◦Generally speaking: More people = more power

Power Analysis Statistical Power

\_\_\_\_\_\_\_\_\_\_ may also be thought of as the likelihood that a particular study will detect a deviation from the null hypothesis given that one exists. \_\_\_\_\_\_\_\_\_\_ is the probability of avoiding a type II error.

Power

1- β ## Footnote Called the __________ of the test of hypothesis.

Power

Calculates the number of participants a study must have to draw accurate conclusions ◦Takes into consideration: estimated effect size, sample means, etc.

Power Analysis

The probability of rejecting a true H0 (Null Hyp) α = .05 usually set, acceptable error Chance that 5 times out of 100 the H0 (Null Hyp) would be falsely rejected

Significance

reject the H0 the results are statistically significant

Statistical significance and p-value p ≤ α

Chance of random error

P-Value ## Footnote *P*

fail reject the H0 the results not statistically significant

Statistical significance and P-Value p \> α

* Less than 5% chance the effect happened by chance. * “There is less than a 5% chance that the null hypothesis is falsely rejected” * Often reported as “significant at the 0.05 level.”

Statistical significance and p-value ## Footnote **A statistically significant p = or \< .05**

* If the number of subjects is small, p value tells us that the effect was either large or consistent (or both) * If the number of subjects is large, the effect size may not be that large

Statistical significance and p-value ## Footnote **A highly significant p \< .001**

* If the number of subjects is small, there might not have been enough subjects to find a difference that truly does exist * If the number of subjects is large, we can be confident that either there is no difference between treatments, or the treatment effect is not consistent

Statistical significance and p-value ## Footnote **An insignificant p \> .05**

* How large the effect was * How consistent the effect was * How many patients were studied

Statistical significance and p-value Factors to consider

◦More important than p value – a better determination of significance ◦Any statistic is simply an estimate of the true value of that statistic ◦CI produces a range within which the true value most likely lies ◦95% CI states that we can be 95% certain that the “true” value is within the CI range ◦Narrower CI is better ◦If the CI include 1 (null value) then the results is clinical insignificant.

Confidence interval (CI)

A screening test is used to separate from a large group of apparently well persons those who have a high probability of having the disease, so that they may be given a diagnostic work up, and if diseased can be treated.

Screening Tests

* The target disease is an important cause of mortality and morbidity. * A proven and acceptable test exists to detect individuals at an early stage of disease. * There is a treatment available to prevent mortality and morbidity once positives have been identified

Screen Conditions

The proportion of people with the disease who have a positive test for the disease. * The ability of the test to identify correctly those who have the test.

Sensitivity

The proportion of people without the disease who have a negative test. * The ability of the test to identify correctly those who do not have the disease

Specificity

* test’s ability to identify presence of disease * test with high sensitivity will not miss many patients who have the disease * A highly useful test when NEGATIVE * Tends to rule OUT the disease * High Sensitivity means low probability of false negative

Sensitivity Screening

* Screening test’s ability to truly identify absence of disease * That is, how likely is a negative test actually reporting the right answer? * A highly useful test when it is POSITIVE * Tends to rule IN the disease * High Specificity means low probability of false positive

Specificity Screening

Usefulness of Sensitivity & Specificity?

* A highly sensitive test is most useful to the clinician when it is NEGATIVE * A highly specific test is most useful to the clinician when it is POSITIVE

proportion of patients who HAVE the disease and a positive test

Positive Predictive Value (PPV)

Proportion of patients who DO NOT HAVE the disease, and have a negative test

Negative Predictive Value (NPV)

Percent of patients with positive test who actually have the disease * Assesses reliability of positive test * i.e. PPV 90% = positive test 90% of the time the test is correct With low prevalence (% of population) of disease: * Lower PPV * False positives increase * Less reliable positive test result

Positive predictive value (PPV)

Percentage of patients with a negative test who actually do NOT have the disease * Assesses reliability of a negative test * i.e. NPV 90% = negative 90% of the time the test is correct With low prevalence(% of pop) of disease : * Higher NPV * False negative test decreased * A negative test result is more reliable

Negative Predictive Value (NPV)

The occurrence, rate, or frequency of a disease * Obtained from cohort studies * Must follow a cohort through time

Incidence

The number of occurrences at one particular time * Obtained from cross-sectional studies * No time line, only a snap shot

Prevalence

* explore the relationship between two continuous variables * method of predicting change in the dependent variable by changing one or more independent variables * What % of variation in the dependent variable can be explained by a change in the independent variable

Regression analysis

Four __________ types: Categorical * Nominal * Ordinal Continuous * Interval * Ratio

Data Type

Named categories with no implied order * ◦Gender, race, ABO blood type, group

Categorical Data Nominal

Sequenced or ranked data * Smallest to largest, lightest to heaviest, easiest to most difficult

Categorical Data Ordinal

Intervals along the scale are equal to one another (i.e. integers) * Set on an underlying continuum that allows you to talk about how much higher one value is than another * 0 on the scale does not mean the absence of the item (e.g., degrees Fahrenheit)

Continuous Data Interval

Characterized by the presence of absolute zero on the scale * An absence of any of the trait being measured (e.g. weight) * Most precise

Continuous Data Ratio

\_\_\_\_\_\_\_\_\_\_ Method 1. Sequential (Two Stage) Testing 2. Simultaneous Testing

Testing Method

* Screening, a less expensive, less invasive, or less uncomfortable test is generally performed first * Those that screen positive are recalled for further testing with a more expensive, more invasive, or more uncomfortable test. * Loss in net sensitivity, and gain in net specificity

Multiple Test Method Sequential (Two-stage) Testing

* Net gain in sensitivity, and net loss in specificity. * Patient is considered positive if they test positive on any test or both. * Patient is considered negative if they test negative on all the tests performed.

Multiple Test Method Simultaneous Testing

Research Evaluation Exam Flashcards

(173 cards)