chapter 5: critically appraising quantitative evidence for clinical decision making

Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins

Chapter 5: Critically Appraising Quantitative Evidence for Clinical Decision Making


Objectives

Discuss importance of critical appraisal for clinical decision-making

DescribeValidityReliabilityApplicability

Critically appraiseCase control studyCohort study


Why Do Nurses Read Healthcare Literature?

Reasons

Maintain knowledge about new advances in professionUpdate specialized knowledge in their specialty Help make informed decisions for evidence-based practiceChallengesHigh volume of informationContradictory findings – need to critically appraise


Critically Appraising Quantitative Studies

The process focuses on three questions:

1. Are the results of the study valid? (Validity)

2. What are the results? (Reliability)

3. Will the results help me in caring for my patients? (Applicability)

Interpretation of results requires consideration of the clinical significance of the study findings and the statistical significance of the results


Questions to Ask in a Critical Appraisal

Why was the study done?

What is the sample size?

Are measurements reliable and valid?

How were the data analyzed?

Did any untoward event happen during the study?

How do the findings fit with previous research?

What does this mean for practice?


What is Clinical Significance?

Amount of degree of changeLarge, reliable change in symptoms or behaviorReturn to normative levels

What about a small change that has practical value?

ExampleAn older woman still experiences urinary incontinent

episodes but the number of episodes are reduced and she returns to activities she had stopped.

Experimental Hierarchy


The Question of Validity

Bias - anything that distorts study findings in a systematic way and arises from the study methodology

Selection bias

Knowledge of who is or is not receiving an intervention

Measurement bias

Recall bias

Contamination


Examples of Bias

Selection bias – factors influencing subject assignment to treatment and control groups

Examples:

People insisting on receiving treatment conditions.

Research staff wanting to meet the desires of potential subjects, meet recruitment benchmarks.


Bias: Knowing Who Is Receiving Treatment

Researchers knowing who gets treatment may treat subjects differently or this knowledge affects measurement.

What is double blind study?

Why are there single blind studies?


Bias: Measurement

Systematic errorsEquipment not calibratedDeviating from established data collection protocolsResearcher personality traits affecting information elicited

from subjects

Examples

Biofeedback equipment not calibrated between measurementsInterviewer administers survey rather than subjects’ self-reportCoaching or encouraging subjects, creating social desirability among subjects


Recall BiasParticipants or respondents do not remember or have

inaccurate memories or responses.

ExampleParticipants cannot recall what they ate or did at a specific

time.


Bias: Contamination

Exposing subjects in control conditions to experimental protocol

Example

Older adults recruited from same senior center to determine effect of a bladder health educational class on reducing number of urinary incontinent episodes.

State PICOT question:How could contamination occur?


Question

Tell whether the following statement is true or false.

The best way to prevent selection bias is to randomly assign study participants to groups.


Answer

True

Rationale: Selection bias in quantitative studies is best controlled by assigning participants to groups on a random basis. Other systematic and deliberate methods of assignment normally increase the chance of selection bias.


Causation and ConfoundingIn any experiment there are many kinds of variables that will effect the

experiment. The independent variable is manipulated during the experiment and you are measuring the effect the IV has on the dependent variable.

Confounding variables are things in which have an effect on the dependent variable, but were taken into account in the experimental design.

For example, you want to know if Prompted voiding has an effect on reducing incontinence episodes. The researchers must design the experiment so that they are as sure as possible the subjects in the study had fewer incontinence episodes because of the influence of prompted voiding, and that the fewer incontinence episodes are not caused by other factors. Those other factors would be confounding variables.


The Question of Validity (cont’d)

Confounded Study Results

A study’s results may be confounded when a relationship between two variables is actually due to a third, either known or unknown variable

Often encountered in studies about lifestyle and health and can be the result of participant history.

Confounding Variables


Confounding

Remember there can be multiple explanations for results of a study

Confounding occurs when the relationship between two variables is due to a third variable.ExampleMurder rates associated with ice cream sales (warmer weather in summer time)Alcohol consumption associated with lung cancer (smoking)


Reliability Evaluate whether the sum of all n values equals the

original N

Magnitude of the effect

How strong is the difference between groups?

Tables

Statistical tests


Reliability (cont’d)

Strength of association

Absolute risk reduction (ARR)

Absolute risk increase (ARI)

Relative risk (RR)

Relative risk reduction (RRR)

Odds ratio (OR)

Number needed to treat/harm (NNT/NNH)

Measures of Effect

Expected Outcome

Occurred

Yes No Total

Exposure Occurred

Yes a b a + b

No c d c + d

Total a + c b + da + b + c

+ d

Strength of Association

Outcome: Incontinence

Incidence

Yes No Total

Exposure (smoking) Occurred

Yes 26 74 100

No 4 96 100

Total 30 170 200

Absolute risk (AR)

Formula:Risk in exposed (Re) = a/(a+b)

Risk in unexposed (Ru) = c/(c+d)

Urinary Incontinence Example:(Re) = 26/(26+74) = 26/100 = 0.26

(Ru) = 4/(4+96) = 4/100 = 0.04

Absolute risk reduction (ARR)

Formula:ARR = Ru – Re

Urinary Incontinence Example:Not appropriate

Absolute risk increase (ARI)

Formula:ARI = Re - Ru x 100%

Urinary Incontinence Example:ARI = 0.26 – 0.04 x 100%

= 0.22 x 100%

= 22%

Relative risk (RR)

Formula:RR = Re/Ru

Urinary Incontinence Example:RR = 0.26/0.04

= 6.5

Relative risk reduction (RRR)

Formula:RRR = {|Re-Ru|/Ru} x 100%

Urinary Incontinence Example:RRR = {|0.26-0.04|/0.04} x 100% = {0.22/0.04} x 100% = 5.5 x 100% = 550%

Odds Ratio (OR)

Formula:Odds of the exposed = a/b

Odds of the unexposed = c/d

OR = (a/b)/(c/d)

Urinary Incontinence Example:Odds of smokers = 26/74 = 0.351

Odds of non-smokers = 4/96 = 0.042

OR = 0.351/0.042 = 8.432

Source:http://latimesblogs.latimes.com/photos/uncategorized/2008/09/09/cracks1.jpg

Chiarelli et al 2009



Random error Variations that occur purely by chance

The extent to which random error may influence a measurement can be reported using statistical significance (or p values) or by confidence intervals.



Statistical significance The aim of statistical analysis is to determine

whether an observed effect arises from the study intervention or has occurred by chance

Study hypothesis and null hypothesis

The smaller the p value, the less likely the null hypothesis is true

Confidence interval - describes the range in which the true effect lies with a given degree of certainty


Confidence Intervals


Precision in the Measurement of Effect: Random Error

Random error: introduced by chance and affects precision of study findings/

Importance: random error can lead to reporting the effects that are smaller or larger than the true effect.

Random error can be reported as statistical significance as the p value or confidence intervals


Question

The findings of a quantitative study testing a high school-based sexual health program reveal that for every 140 female students who take the program, one pregnancy is prevented. This conclusion indicates the:

a. OR

b. NNT

c. NNH

d. ARR


Answer

b. NNT

Rationale: The number needed to treat (NNT) represents the number of people who would need to receive the therapy or intervention (the educational program) to prevent one bad outcome (teenage pregnancy).


Appraising Case Studies

Historically ranked lower in the hierarchy of evidence

Must be used with caution to inform practice, and any application requires careful evaluation of the outcomes


Appraising Case Control Studies

These investigate why certain people develop a specific illness, have an adverse event with a particular treatment, or behave in a particular way

Questions to ask

How were the cases obtained?

Were appropriate controls selected?

Were data collection methods the same for the cases and controls?


Appraising Cohort Studies

These are used for investigating the course of a disease or the unintended consequences of a treatment

Questions to ask

Was there a representative and well-defined sample of individuals at a similar point in the course of the disease?

Was follow up sufficiently long and complete?

Were objective and unbiased outcome criteria used?


Appraising Cohort Studies (cont’d)

Did the analysis adjust for important prognostic risk factors and confounding variables?

What is the magnitude of the relationship between predictors (i.e., prognostic indicators) and targeted outcome?

How likely is the outcome event(s) in a specified period of time?

How precise are the study estimates?


Question

A team of researchers have received a grant to investigate the potential links between diet and the development of stomach cancer. What methodology is most likely to answer the researchers’ clinical question?

a. Case control

b. Case study

c. Randomized controlled trial (RCT)

d. Qualitative study


Answer

a. Case control

Rationale: A case control study often selects individuals with a particular disease (e.g., stomach cancer) and looks back to identify factors that may underlie that disease (e.g., diet). Neither a case study nor a qualitative study would inform this relationship and an RCT would be unethical and impractical.

chapter 5: critically appraising quantitative evidence for clinical decision making

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