Research Study guide

CH 1: EVIDENCE BASED PHYSICAL THERAPIST PRACTICE

Evidence based practice is defined as:o The conscientious, explicit, and judicious use of current best evidence

in making decisions about the care of individual patients “judicious” refers to treating the patient as an individual

Three key terms to being an EBPT:o Best evidence- hierarchial levels of published articles o Clinical expertise- reflective practitioner

Not to be confused with clinical experience which is based off of time

o Patient values- participatory, informed consumer (be honest about outcomes)

Evidence based practice requires a lot of tasks, but two of the most important things are: APPLYING RESEARCH IN CLINICAL PRACTICE and EVALUATING THE OUTCOME OF IMPLIMENTED PRACTICE

Steps to being Competent in Evidence Based Practice:1. Ask a searchable clinical question2. Access the best evidence3. Critically appraise the evidence4. Integrate the appraised evidence with expertise and patient preferences5. Evaluate the effectiveness

Barrierso Timeo Access to literatureo Competency in critical appraisal of statistical analysis and study

designo (more likely to use EBP if young, recent grad, masters or DPT)

Three ways to improve1. Educational interventions

a. Be prepared academicallyb. Go to continuing ed courses

2. Personal practice interventionsa. Be committed to developing professionally, don’t just rely on years

of experienceb. Standardize outcome measures

3. Organizational Improvementsa. Culture of EBP and Reflective Practice

i. Use the resources you have in other PTs

Ch. 2- LEVELS OF EVIDENCE

Evidence may come in the form of: Published research articles Clinical practice guidelines Patient/client records Recall of patient/client cases

Evidence should be SPECIFIC to a clinical question You need a REPRESENTATIVE sample (the age, stage of illness-chronic vs.

acute, gender, race, setting…)- this allows for external validity Use PEER REVIEWED publications Make sure context is REVERENT

Physical Therapy Evidence is limited in: Randomized control trials (RCTs) Large sample sizes Representative populations Well controlled intervention studies

Levels of bias control in research design:

Low<----------------------------------------------------------------------HighCase report Non-experimental Quasi-Experimental Experimental (random)

What is BIAS? Results or inferences that systematically deviate from the truth—may be

uncontrolled influences or unwanted influences RCTs help control for bias Single system design- one that allows participants to receive both the

experimental and control condition.o This design may not always work in PT because some people just get

better over time

Efficacy- What is the outcome UNDER IDEAL CONDTIONS?Effectiveness- To what extent can an intervention produce the outcome under TYPICAL CLINICAL CONDITIONS

What is a SYSTEMATIC REVIEW? A collection of research gathered in effort to reach an UNBIASED conclusion

Inclusion criteria and analysis established a priori to control for bias Usually RCTs

Ideal Evidence:1. Random2. More than 1 group (for comparisons)3. Control: subjects and interventions4. Measurement- be able to identify if the patient is actually improving or is just

getting better at the specific test you are using5. Systematic data collection and analysis

Hierarchy for CLINICAL REPORTS (from highest down to lowest) Groups of quality pt-centered studies Single quality pt/client-centered studies Single lesser quality pt-centered study Physiologic (basic science) study Case report

Hierarcy for ARTICLES (from highest to lowest) Systematic review of RCT RCT Systematic review of Cohort study Individual Cohort Outcomes research Systematic Review of Case-Control Individual Case-control Case or cohort study Expert Opinion

Retrospecitve study- look at the past- uses data that has already been collected (outcome measures, clinical prediction rule)Prospective study- get data in the present and gather more later, in the future(Prognosis, Clinical Prediction rule)Longitudinal study- Over TIMECross-sectional- pick a point in time to collect data on a subject (Diagnosis)Randomized Control Trial- (Interventions, Clinical prediction rule)

CH. 3- THE CLINICAL QUESTION

Clinical Questions can be predicted outcomes, treatment options, measures, or strictly anatomic problems.

Background question= GENERAL KNOWLEDGE ABOUT A CONDTION a root (who, what, where, when, how, why) + a disorder, test, or treatment….

Ex: WHAT are the signs and symptoms of ALS?

Foreground Question= SPECIFIC KNOWLEDGE TO INFORM CLINICAL DECISIONS1. patient/client details (age, gender, diagnosis)2. specific diagnostic test, clinical measure, predictive factor, intervention3. comparisons (which intervention is best)4. consequence of interest

PICO FORMAT (for foreground intervention questions)

Diagnosis: (PEcO) Prognosis (PIO) Intervention (PICO)P-patient P- patient P-patientE-exposure to test I- Intervention I- InterventionC-comparison/control O- Outcome C- Compare/controlO- Outcome O- Outcome

EXAMPLE:Does an elderly patient with autograft for full thickness facial burns have a better scar outcome if he uses compression garments or no garments?P- elderly with full thickness burnI-compression garmentsC- no garmentsO- scar outcome

SEARCHING FOR EVIDENCE primary (original research reports such as peer reviewed journals) secondary ( textbooks, summaries, review papers)- try not to site

CH 4: THE RESEARCH QUESTION

different from an evidence based practice question, which is directed by discovery in a clinical setting

states WHAT is being researched aka Purpose or Problem statement try not to be too broad or too small try to extend your existing knowledge

You need relevant background information which includes: info to support the need for the study literature review (look at previous studies, related studies, limitations, etc.) epidemiology data ( pattern of health/conditions at population level; relates

to application of findings)

Definitions:

Concepts : mental images of OBSERVABLE phenomenon described in words (fatigue, pain, age, flexibility)

Constructs : NON-OBSERVABLE abstraction created for a specific purpose that is defined by observable measures (satisfaction, quality of life, EBP, readiness for change, motivation)

Theory : an Organized set of relationships among concepts and constructs that is proposed to explain relationships

To be successful it should: be consistent with empirical observations be supported by repeated testing predict future behavior

Grand Theory- comprehensive, impractical to test in entirety, supported by an accumulation of evidenceConceptual framework- “smaller scale theory”, schematic forms

Hypotheses-predictions on the outcomes of a studyHa- Research Hypothesis- what WILL happen if research is successfulHo- NULL hypothesis- prediction that NO difference will occur b/t variables (this type is preferred)

A research paper should include:1. abstract2. background and purpose3. research question (hypothesis)4. review of literature

a. conceptual framework/theory (if relating to a research question)

CH. 5: QUANTITATIVE DESIGN

Two types of approaches- Qualitative and Quantitative

Quantitative Design can be:1. experimental- purposeful manipulation of variable; cause and effect

relationships examined2. Quasi-experimental- one subject group or lacks randomization; controlled

manipulation is preserved, lower level of control3. Non Experimental/Observational- manipulation of variables is lacking;

naturally occurring rather than random group characteristics4. Within Subject- repeated measures compared5. Between Subject- group difference compared6. Cross-sectional- single time point or within specified time interval7. Longitudinal- phenomenon occurring over time8. Retrospective- previously collected information9. Prospective- forward design for data collection

Within-subject designs - compares repeated measures (everyone serves as their own control) of an outcome WITHIN the SAME individuals

Between-subject designs - compares outcomes between two or more groups of subjects

(studies will usually have both)

CONTROL- the need to minimize bias in a study TIME ELEMENTS- duration of a study( cross-sectional and longitudinal) and

Direction of a study (retrospective and prospective)

***LOOK AT TABLE 5-1 in book for a table on general features of research designs

Designs for questions about prognostic factors:1. Cohort designs:

Are used for questions about prognostic factors Time to ensure that the outcome occurs is essential May be prospective( more control) Or Retrospective (assure the outcome has occurred)

2. Case-Control design: Retrospective approach Subjects with the outcome compared to a control group free of the outcome Risk factor identification

Designs for questions about interventions Determine beneficial effects and/or harmful consequences of interventions Efficacy- measures the extent to which an intervention produces a desired

outcome under ideal conditions Effectiveness is measuring the impact of an intervention under usual clinical

conditions1. Experimental designs

a. Gold standard for intervention questionb. Randomized controlled trial (RCT)

i. Controls unwanted influences2. Quasi-Experimental Studies- purposeful intervention

a. Time seriesi. People serve as their own control

ii. Measure the usual intervention and the test intervention after certain periods of time

b. Nonequivalent Control Groupi. Relies on naturally occurring groups

c. Single-System designsi. One subject undergoes an experimental treatment and a

control period

Designs for Clinical Prediction Rules- Usually non experimental designs- Ottawa Ankle Rules

Designs for Outcomes Research- focus is on impact of clinical practice in the real world- basis for assessing quality of care across settings and disciplines- nonexperimental; usually an observational format with less control features

QUALITATIVE DESIGN

When to use it?- If topic isn’t well understood- If topic will be best described by detailed examples and narratives

3 stances:1. interpretivism2. hermeneutics 3. Social constructionsism

KEY TERMS:1. positivism: assert that objective accounts of the world can be given2. Post Positivism: hold that only partially objective accounts of the world can

be produced; all methods are flawed3. Structuralism: any system is made up of a set of oppositional categories

embedded in language4. Post Structuralism: holds that language is an unstable system of referents,

thus it is impossible to capture the meaning of action, text, intention.5. Post modernism: “contemporary sensibility”, that privileges no single

authority, method, or paradigm

Three theories:1. Grounded Theory

a. Systematic qualitative approachb. Analysis of data

2. Ethnographya. Anthropologic approachb. Data collection through observation, interviewc. Goal- understand culture or personal characterization of experience

3. Phenomenologya. Provides faithful discriptions of an individual or group’s experienceb. Highly abstract

Quantatative Vs. Qualitative quality criteria

Quantitative Research Qualitative ResearchInternal validity CredibilityReliability DependabilityExternal Validity TransferabilityObjectivity Confirmability

1. Credibility- how believable results are from the participant perspective2. Dependability- research can not be replicated; describes changes that can

occur3. Transferability- degree that qualitative findings can be generalized to other

contexts4. Confirmability- degree that results could be corroborated by others

Evaluative Criteria:1. subjective meaning: researcher provides interpretation of participants words2. participant validation: participants should find conclusions relevant3. Description of content: setting, roles, group dynamics, background4. Lay knowledge: participant perspectives are equal in value to those of

“experts”5. Flexibility: Variability is the standard for qualitative research6. Sampling: selected based on participant’s willingness and ability to share info7. Generalizability: situationsl rather than demographic- uses statistical

approach

SYSTEMATIC REVIEW AND META ANALYSIS

A systematic review is the summary of results from multiple studies, including:- Background/review of lit- Protocol- Comprehensive search- Appraisal of quality- Data extraction and meta-analysis- Clinical/research implication

Forest plot-***CHECK NOTES OR IN BOOK FOR BETTER EXPLANATIONS***- if the confidence interval DOESN’T cross the “no difference line”, it means the

study is significant.

CH. 6: QUANTITATIVE RESEARCH SUBJECT AND SAMPLING

***Target population- total aggregate of individuals who the investigators wish to apply their findings**Accessible population- the pool of potential research subjects available*Subjects- individuals participating in the study

-subsets of accessible population-called a “sample”-primary data collected in real time (taste test, observation)-secondary data collected during routine business or prior research activity (sales data)

- Subject Identification should have inclusion and exclusion criteria

There are 2 common selection methods:1. Probablistic (random selection)- minimize bias and sampling error

a. Simple Random Samplei. Each potential subject has an equal chance of being selected

ii. Drawing or random # generator iii. Impractical if huge pool of potential subjects

b. Systematic Samplingi. First subject is random, all others are chosen based on their

numerical distance from first subject. ii. Need to use a value that can be ranked (birth date, SSN, MRN)

c. Stratified random Samplei. Subgroups of a population are identified and randomly

selected to ensure their inclusion in the studyii. Defined by naturally occurring differences (age, race, gender)

d. Clusteri. Subjects randomly selected from naturally occurring pockets of

the population of interestii. Cost effective

2. Non-Probabilistic (non random)- extended study time, easier to implementa. Convenience Sample

i. Subjects who are readily available are selectedii. Consider systematic dissimilarities b/t individuals

participating and those who are notb. Snowball Sampling

i. Initial subjects recruit additional participantsc. Purposive Sampling

i. Individuals hand selected to participate based on characteristics important to researcher

Group Assignment:1. Random Assignment

a. Based on drawing number or coin flipb. May have unequal groups/characteristicsc. Not good for small sample sizesd. Block assignment (number in each group is predetermined, then

randomly assigned to each group)e. Systemic assignment (subjects count off numbers until all are

assigned)f. Matched assignment (first subgroups- age, race, gender; then

randomly assigned to study groups)2. Non-Random Assignment

a. Subjects members of preexisting groups of interestb. Used in Retrospective studiesc. Assignment determined by presence or absence of characteristic of

interest

- Power - the probability that a statistical test will detect, if present, a relationship b/t two or more variables or a difference between two or more groups

- Type II error - false negative (due to insufficient sample size)

CH. 7: VARIABLES AND LEVEL OF MEASUREMENT

Variables- A characteristic of an individual, objects, or environmental condition that may take on different values

1. Independent (IV)a. The variable purposefully manipulated by investigators in an effort to

produce a change in outcomeb. May have one or more IVc. IVs may have 2 or more levels or may be continuousd. Observational studies and descriptive studies do not have

independent variables2. Dependent (DV)

a. Outcome of interestb. In intervention studies, happens as a result of the manipulation of the

IVc. Prognostic studies, DV is predicted by, rather than caused by IV

3. Extraneous (EV)a. Factor other than the IV which may influence or confound the DV

Definitions:1. Discrete- values are distinct categories (location/setting, or WBing

/NWBing)

2. Dichotomous- two values of characteristic possible (men/women, or success/unsuccessful)

3. Continuous- a scale with increments (gait speed or distance)

LEVELS OF MEASUREMENT1. Nominal

a. Classifies objects or characteristics but lacks rank order and a known equal distance between categories

b. Religion, sex, yes/no response2. Ordinal-

a. Classifies objects or characteristics in rank order but lacks mathematical properties of a known equal distance between categories, may or may not have natural zero point.

3. Intervala. Classifies objects or characteristics in rank order with a known equal

distance b/t categories but that lacks a known empirical zero point, i.e, “0” does not reflect the absence of the characteristic.

b. F/C scales, IQ, Year4. Ratio

a. Classifies objects or characteristics in rank order with known equal distance b/t categories and a known zero point

b. Cant be negativec. Height, weight, BP, speed, distance

Reference Standard-- Norm referenced- derived from previously tested subjects- Criterion referenced- compared to previously established ‘absolute’ standard

**Reliability- extent to which repeated measurements agree with one anotherFor instruments:

1. Internal Consistency- extent to which subsections of instrument measure same concept or construct

2. Parallel forms- test 2 versions of tool that measure same concepts or constructs

3. Split-half- test 2 versions of tool that are combined into one survey administered at one time

For Raters:1. Inter-rater- ACROSS two or more examiners2. Intra-rater- SAME examiner

**Validity- degree to which a measure captures what it is intended to measure. 1. Face Validity- subjective assessment (user feedback)2. Content Validity- degree to which items of an instrument cover all facets

of variable being measured (expert review of instrument)3. Construct Validity- does it match the operational def. of the concept or

construct said to represent?

4. Convergent Validity- degree to which 2 or more measures of the same characteristic produce similar scores

5. Discriminant Validity- can an instrument distinguish b/t or among different concepts/constructs?

6. Criterion Validity- do the instrument’s scores relate to a reference standard instrument’s scores

7. Concurrent Validity- agreement b/t the results by given instrument and results by “gold standard”

8. Predictive validity- agreement b/t results obtained by evaluated instrument and one that is more direct.

Responsiveness to change is the ability of a measure to detect change in the phenomenon of interest. Determined by calculating the minimal detectable change (MDC)

Floor Effect- no change seen in scores with lower performancesCeiling Effect- no change in scores even with better performances

CH. 8: RESEARCH VALIDITY

Statistical conclusion validityThreat CounterLow power Sample sizeViolation of statistical assumption Subject homogeneityType 1 error with “fishing” Statistical adjustmentsLow reliability Internally reliable testsTreatment differs across occasions Define and standardize treatmentRandom confounds Stable environmentRandom differences in subjects Use within subject design

Internal ValidityThreat CounterHistory Random assignment to groupsMaturation “Mortality “Testing “Statistical regression to mean “Selection “Unknown direction of causation “Diffusion of treatment Share rationale with subjectsCompensatory equalization of tx Treat all justlyCompensatory rival by subjects Hide hypothesis from subjectsResentful demoralization of subjects Treat all justly

Construct ValidityThreat CounterPoor representation of constructs Measures of high validity define

constructsSingle operation bias Measures of high validity measure IV in

many waysSingle method bias Measure DV in many waysHypothesis guessing by subjects Double blindEvaluation manipulation Measures with high validityExperimenter expectation Double blind studyConfounding constructs and measurement level

Continusous levels of measurement

Multiple treatment confound Single treatment, not a mixMeasurement and treatment interaction Measure effects of pretest on DVRestricted generalization across possible dependent variables

More than one DV

External ValidityThreat CounterSample doesn’t represent population of interest

Select from well defined population

Setting does not represent setting of interest

Vary settings and analyze them

Time during study does not represent all future times

Replicate at different times

Research Study Guide: Factor Analysis thru Ethical Considerations (3/16-4/18)

15) Factor Analysis and Structural Equation Modeling:

Factor Analysis : A technique used to identify factors that statistically explain the variation and covariation among measures.

Examines the structure within a large number of variables Explains relationships between variables Unlike other statistical tests; factor analysis does not have established or

known independent variables (IV) or predictors IV are unknown at start of analysis DV are continuous and are often a set of measures A research question is present, but a hypothesis is NOT tested

Example of a Factor Analysis test = eHarmony Factor analysis is a more complex statistical procedure based on correlations

between items, just like Reliability analysis.

Internal consistency: the extent to which procedures or tests assess the same skill, characteristic, or quality

It is a measure of the precision between the observers or of the measuring instruments used in a study. This type of reliability often helps researchers interpret

data and predict the value of scores and the limits of the relationship among variables.

Cronbach’s Alpha: a coefficient of reliability, often used as a measure of internal consistency and reliability

Structural Equation Modeling : SEM expresses relations among several variables which may be directly observed variables or unobserved hypothetical variables.

Multivariate analysis technique Pattern of relationship variables is established a priori (before analysis)

based on theoretical expectations Mediators: a variable that describes how instead of when effects will occur

How or why effects occur Moderator: the interaction- or the relationship between the IV and DV

specify when certain effects occur

o the extent of the moderator will influence the degree of your DV Structural equation modeling is a clinically useful approach to analyze relationships

between constructs and outcomes.

Factor analysis and structural Equation models are used to establish reliability

16) Evidence in Diagnostic Factors:

Diagnostic Tests:- The decision to perform a diagnostic test is made along a continuum of probability

between the decision points of “test threshold” and “treatment threshold”.o Test threshold : probability below which the test will not be performed-

because the probability is remoteo Treatment threshold : probability above which the test will not be

performed- because the probability is high- and immediate treatment is needed

Study credibility:- Appraisal of evidence to support study credibility begins with assessment of

research validity. Higher levels of validity indicate greater confidence that there is a lack of bias.

Reliability:- Verification of reliability is an acknowledgement that the data captured in a test is

composed of the “true value” and error.- Error must be minimized to avoid false positives- Statistics used in Diagnostic tests to determine reliability:

o Pearson’s product moment correlations and ICC (interclass correlation coefficient) are used with interval or ratio level data

o Spearman’s rho and kappa are used with ordinal and nominal data, respectively

o Pearson’s r and Spearman’s r compare two measureso ICC and K compare multiple pairs of measures simultaneously

Validity:- Face validity- does the test measure what you want it to measure

- Statistical significance- Criterion validity - Concurrent validity- 2 x 2 table (chi-square test of association)- **A valid diagnostic test consistently produces true positives or true negatives, or

both

True Positive : test detects something that is really there False Positive : test detects something that is not really there True Negative : test detects nothing, and nothing is there False Negative : test detects nothing, but there is something there

Sensitivity:- Sensitivity (Sn): the proportion of individuals with the condition of interest that

have a positive test result (“true positives”)- Sensitivity allows a clinician to determine which test is most appropriate to use to

determine if a particular diagnosis is present.- When a negative result is obtained using a highly sensitive test, the clinician can

confidently Rule Out the condition (SnOut)Specificity:

- Specificity (Sp): The proportion of individuals without the condition of interest who have a negative test result (“true negatives”)

- Clinicians use diagnostic tests with high specificity when knowledge that the patient does not have the condition of interest is important

- When a positive result is obtained using a specific test, a clinician can confidently Rule In the condition (SpIn)

Positive Predicted Value: - PPV describes the ability of a diagnostic test to correctly determine the proportion

of patients with the disease from all the patients with positive test results.o Calculated as a percentage

Negative Predicted Value:- NPV describes the ability of a diagnostic test to correctly determine the proportion

of patients without the disease from all the patients with negative test results.o Calculated as a percentage

Likelihood Ratios:- Positive Likelihood ratio:

o likelihood that a positive test result was obtained in a person with the condition

o obtained by : sensitivity/(1-specificity) - - result will be >1- Negative Likelihood Ratio:

o Likelihood that a negative test result was obtained in a person with the condition

o Obtained by: (1-sensitivity)/specificity - - result will be < 1- A Likelihood Ratio = 1 tells you nothing about the test and is not significant

*Instruments must have reliability, validity, and responsiveness, the ability to detect change in the phenomenon of interest*Comparison to ‘gold standard’ enhances validity

17) Evidence in Prognostic Factors

Prognosis: a prediction about the future status of a patient/client with respect to either disease or disorder development, disease or disorder outcome, and/or response to physical therapy intervention.

Elements of prognosis:o Outcomes possibleo The likelihood the outcomes will occuro Time frame required for achievement

Pt characteristics may predict future outcomes Predictors of future (adverse) events are called risk factors

Study Credibility:o Studies pertaining to prognostic factors must be evaluated with assessment

of its research validityo Appraisal of evidence about prognostic (risk) factors should use a series of

questions such as those developed by the Centre for Evidence-Based Medicine – Oxford

Prognosis research uses descriptive statistics and tests of relationships to identify risk factors

o Proportions (percentages): percentage of participants with a particular risk factor who developed the outcome of interest

o Survival curves: plots number of outcomes or events over time; outcome is dichotomous

o Odds Ratio (OR ): the odds that an individual with a prognostic (risk)factors had an outcome interest as compared to the odds for an individual without the prognostic (risk) factor

<1: decreased odds, >1: increased odds, =1: no change in oddso Hazard ratios (HRs ): an estimate of the relative risk of developing the

problem of interest over the course of the study, weighted by the number of subject available

o Relative Risk: the ratio of the risk of developing a disorder in patients with a prognostic (risk) factor compared to the risk in patients without the prognostic (risk) factor; longitudinal studies- (rate of development of condition)

o Prognostic factor studies also report study results using p values and confidence intervals.

18) Evidence to Support Intervention:

Evidence on interventions should be assessed based on research validity Higher research validity allow results to be believable and reasonably free from bias The highest level of research validity in intervention studies will be found in

randomized clinical trails (RTC). Quasi-experimental and non-experimental studies may produce lower levels of

research validity and higher bias allowing overestimation of treatment effects, but may be appropriate for determining ineffectiveness or harm from an intervention of interest.

o Quasi-experimental designs are more feasible and may represent research that is more clinically relevant

Intervention studies use descriptive statistics to elaborate on subject characteristics and summarize baseline performance measures

Effects of treatment are described by tests of group differences – compares groups using means, ranks, or frequencies

o Parametric (t-test, ANOVA) or non parametric (chi-square)o Effects size: identifies the magnitude of difference between two group

means 0.20 – minimal effect size, 0.50 – mod effect size, 0.80- large effect

size

- Higher Validity will occur with:o Randomly assigned groups – equally distributed and random allocation to

groupso Blinding of researchers, subjects and/or outcome assessors to group

assignment single or double blind study Counters potential for bias

o Low attrition rate: low withdrawal or loss of subjects fro follow up Statistical power is compromised with loss of subjects 5/20 rule (loss of 20% of subjects is detrimental to results)

o Researches performed a confirmation of findings

p values:- Used to determine statistical importance in intervention studies - The smaller the p value the more convincing the results are that the intervention

made a differenceConfidence intervals:

- Are also used to determine statistical importance in intervention studies- May have negative numbers to indicate harm from interventions

Number Needed to Treat (NNT):- Estimate of the number of subjects that must receive an intervention in order for 1

subject to increase his/her benefit or reduce risk- NNT = 1/ABI OR NNT = 1/ARR- ABI = absolute benefit increase ARR = absolute risk reduction

19) Evidence on Clinical Prediction Rules:

Clinical Prediction Rule: (CPRs)CPRs: are systematically derived and statistically tested combinations of clinical findings that provide meaningful predictions about an outcome of interest (diagnosis, prognostic factor, treatment, or outcome).

- Accuracy and efficiency of decision-making is enhanced through the use of clinical prediction rules (CPR).

- CPRs can be depicted in graphical forms- called algorithms- Evidence on CPRs may focus on derivation of the rule, validation of the rule, or both.

Building a CPR:Derivation:

- CPRs are based on logistical regression- CPRs are predicting a categorical outcome- therefore yield a predicted value of the

probability that the outcome of interest occurred - Step 1:

o Identify the predictors that should be included in the model These are determined based on evidence and clinical practice

o Test the significance of predictors supported by evidence T-test or regression weights

- Step 2:o Build and test the modelo Logistical regression

Significance of the model, classification of plots, significance of predictors within the model, odds Ratios

- Step 3:o Establish meaningful cut points- aka values that the clinician can quickly

apply to effectively interpret findings o Build the Prediction Rule based on the predictors and their cut scores

CPRs:- CPRs are systematically derived and tested combinations of clinical findings that

provide predictions about an outcome of interest- The most useful CPR have

Few, yet clinically sensible predictive factors Demonstrated ability to make accurate predictions

- CPR with wide-spread application to populations and settings are ‘best in class”

20) Outcomes Research, Self-report Outcomes, and Clinical Guidelines:

- Outcomes research: the study of the impact of clinical practice as it occurs in the “real world”

o Outcomes focus on the end results of treatment rather than efficacy or effectiveness of individual interventions

o Can be prospective or retrospective and cross sectional or longitudinalo may focus on design elements that may enhance the research validation of

the observational studieso assessing the meaningfulness of results about outcomes studies depends on

obtaining p values and confidence intervals.o Minimal clinically important difference (MCID) may be used in outcomes

studies to identify a threshold of change. Self report measures:

- Outcomes may be capture through direct objective examination or a pt/client’s perceptions, or both

- Results of self-report instrument’s clinimetric properties may be presented in both quantitative and qualitative form.

o Clinimetric properties are the measurement characteristics of surveys or other indexes used to obtain patients’ perspective about an aspect of their condition.

- Statistical tests used are dependent upon the phase of evolution of the self-report instrument

- Results of a self-report instrument’s clinimetric properties may be presented in both quantitative and qualitative form

- Reliability of self report measures:o Internal consistency is reported using Cronbach’s alpha ( )αo Interclass correlation coefficients (reproducibility) should be between 0.70-

0.95o Kappa statistic and standard error- used to evaluate agreement among

repeated scoresClinical Practice Guidelines:

- Clinical Practice Guidelines (CPG) are systematically developed statements to assist the practitioner and patient decisions about appropriate health care for specific circumstances

- CPG’s intent is to improve efficiency and effectiveness of health care and are based on current best evidence and expert judgment

- Clinical Practice guidelines are statements to facilitate decision making and quality of care.

21) Ethical Considerations in Research:

Regulations for conduct of research involving Human Subjects:- Voluntary and informed consent- Independent review of research protocols- Must indicate the significance of the study The study must be reviewed and approved by the Institutional Review Board (IRB):- Considers scientific merit, investigator competence, risks to subjects, feasibility,

risk-benefit ratio- All proposals involving human subjects MUST be submitted to the IRB

Elements of Informed Consent:- Fully informed- Risk and benefits identified- Confidential and anonymous- Lay language- Patient questions addressed - Refusal or withdrawal