N3030_Module 4_Quantitative Research and Designs (1)

7/29/2019 N3030_Module 4_Quantitative Research and Designs (1)

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NURS3030H NURSING RESEARCH IN PRACTICE

MODULE 4

Quantitative research & designs

J anet Rush, RN, PhD, 2010


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Objectives:

Review: quantitative methodology

Introduce designs where questions

are asked about comparisons,associations, risks

Comparing the independent anddependent variables

Experimental designs

Effectiveness Trials

Causation Trials


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Quantitative methodology

Emanates from a positivist perspective

Has been the predominant biomedical focus

Useful in nursing and allied health: programs,

interventions, understanding/comparing factors inpopulation health

Uses objectivity, logic, experimental/scientificprocesses to:

Compare Control

Predict

Infer

Establish statistical probabilitiesDefine statistical significance

Test hypotheses


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The research question

Recall PICO and PECO

P = population

I or E = intervention or exposure (independent

variable) C = comparator group(s)

O = the outcome/result (the dependent variable)

PICO for studies of effectiveness Among (P), what is the effectiveness of (I) versus (C) on (o)?

PECO for studies of causation/risk What is the risk of (O) among (P) who are/were exposed to (E) versus (C)?


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Examples: PICO, PECO

PICO: questions of effectiveness of treatment,programs, interventions

Aim to assess new or existing programs of potential benefit

Among LTC elder women with incontinence, what is the effectiveness of a prompted

voiding protocol vs. conventional care on mean, weekly incidence of incontinence

over 1 month>

Among a rural community, what is the effectiveness of routine PHN visits for first-

time mothers vs. conventional followup on the length of exclusive breastfeeding at 6

months?

PEOC: risk or causation questions Aim to assess the level or risk or harm

What is the risk of MI among men (40-60 yrs) with Type 2 DM vs. those without

Type 2 DM?

What is the relative risk of lung cancer among adult women (age 40-70) with a

history of smoking vs. those who have never smoked?
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Answering quantitative questions

Literature review getting the answer from the existingevidence

PICO real time or prospective

PECO often retrospective (ethical reasons)

Designing studies: Longitudinal, comparitive/descriptive

Retrospective . Prospective

Case/Control RCT, (quasi) RCT, Cohort analytic

What are the strengths and limitations of

retrospective and prospective studies?


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Retrospective vs. Prospective

Which design to use - influenced bymany factors ($, time, ethics, feasibility)

Effectiveness studies best design isprospective randomized controlled trial

(aka, experimental), then quasi RCT,then cohort analytic (a prospective studywithout randomization), then pre/postcomparison

Causation studies most feasibledesign is retrospective case control(sometimes called cohort comparison)


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Design architexture

Research question

Sample of the population

Intervention/data collection

Statistical comparisons

Group A Group BExperimental subjects Control subjects

Results, conclusions, decisions, dissemination

Designs varied, rules for rigor to be followed every step!

For strong internal validity of the study (otherwise, bias exists)
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Simple designs

Questions re. descriptions,comparisons or correlations

E.gWhat is the prevalence of Alzheimers disease in Peterborough, ON?

Key considerations: representative sample, accurate outcomeassessment/measurement tool/assessors

Sample Outcome-Yes (the answer)- No

(denominator)

May want to make secondary analyses Think about the factors of interest- Think about other data to collect

Research question
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Other analyses/observations

Relationships between/among variables

Extraneous, confounding variables?

Think. determinants of health

IncomeCulture

Context

.e.g., social

Education

Biological

Health

What will affectThe relationships?


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Causation

E.g., what is the risk of Alzheimers disease among

adults exposed to lower vs. higher income levels?*

Find cases and controls those with Alzheimers

and those without (Case control design) Gather income data

Make comparisons

-Cases (with Alzheimers)

-Controls (without Alzheimers)

- Low income, yes- Low income, no

- Low income, yes- Low income, no

* From N414 student, A. Clawson
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Experimental designs

Can be for PICO or PECO

Can be retrospective or prospective

Hypothesis testing is undertaken Inferential thinking

Inferential statistics

Strength of the associations Finding a difference (vs. the null

hypothesis of no difference)
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The RCT:Each step has biases and rules

Research question

Select the sample & design

Group 1Intervention

Group 2Control

ANALYSIS

DESCRIBE GROUPS COMPARE GROUPS

COMPUTE SIGNIFICANCE

Decision/Application/Communicate

(Knowledge Transfer)

R*

Study TeamOutcome measures

Inclusion criteriaApproval/Consent

Allocation concealment

Consecutive subjectsAuditbecause.Follow up

* R = randomization

R l d Bi d i it i ht


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Rules and Bias doing it right

SAMPLE

Finding the sample? Convenience

Pilot

Random

Volunteer

Issue of the setting

Similar at baseline? How do you know?

Inclusion/Exclusion Criteria
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The issue of Sample Size (SS)

SS is based on Probability theory Assumption of a random sample

When is SS important? pilot vs. full trial

Rarely use SS calculation for pilot Use for hypothesis testing in inferential stats

Interpreting the articles what to look for? our SS was based on

Hypothesis testing: need to know The expected proportion or mean differences between

groups

Consider the % difference you expect with yourintervention & change to proportion ( e.g., 15% is .15)


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Sample Size, Website

On-line reference freedownload

http://biostat.mc.vanderbilt.edu/twiki

/bin/view/Main/PowerSampleSize


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Once downloaded, the icon will appear on your desktop. Whenyou click it, this screen will come up click continue to start the program


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Example

Procedure follow along the screen

prompts

For intervention/treatment studies:

click dichotomous,

independent samples, 2proportions, uncorrected chi-

square test), type in ()alpha

.05, () beta .80, p 0 (firstproportion), then p1 (the second

proportion), then click m = 1 &

finally, click calculate


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Here, I suggested a 25%change between the groups. That is,usual rate = 50% (.50) & hypothesizedrate with a new intervention = 75% (.75)


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58 subjects/group


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Error

Beta error: rejecting the nullhypothesis when it is false (whenthere IS a difference) & should berejected about the adequacy ofthe sample size too small?

insufficient power to detectdifferences between groups ifdifferences do exist (type 2 error).

Alpha error: The probability of

erroneously concluding there is adifference between groups whenthere is, in fact, no difference (type 1error) may be accepting a falsepositive result usually willing toaccept = 0.05


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Measurement:Validity / Reliability

ValidityConsider an appendicitis scale . Content validity: scale includes all the aspects, eg, signs, symptoms

Construct validity: scale supports or lines up with the theory of theconcept. Eg., high WCB with appendicitis

Criterion validity: scale has the capacity to diagnose or predict

changes., eg., if low score on the scale, then the pt probably does nothave appendicitis - - it reflects reality rules in or rules out.

Reliabilitythe extent to which the scales variance isattributed to random error should have sufficient variabilitybut should control for sources of variation should be able tobe reproducible, one setting/population to another.

Considers test-retest reliability, inter and intra-rater reliability.

Measure: Cronbachs alpha (>.8 = acceptable)


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Randomization & Allocation

Agreement to be randomized Issue of informed consent

Random sampling Computer generated random number table

Blocking factor (of 4, 6, 8 etc)

Quasi randomization Pros and cons?

No randomization?

When to use

Cohort analytic trials Allocation to group(s)

Concept of concealment

Not to be confused with blinding


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The study groups

Sticking to protocol Ways of maintaining adherence Involving subjects Auditing

Contamination

Cross-over How many/group is OK? What to do about cross-overs

Co-intervention Why/when might this happen?

What to do? Follow up

Dropouts, losses to follow up How many/group is OK?

Fi i hi h
http://www.marmaray.com/images/tech_bored_crosstracks.jpg


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Fitting everything together

start with the question getting the right designsampling methods, inclusion/exclusion+++ variables confounders

internal validity/data collection & audits tight manoeuvres well explained analysis?decisions from an analysis

Criticalappraisal

every step!!

Analysis - Using the data


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Analysis Using the data

Results significant?

P value (

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N3030_Module 4_Quantitative Research and Designs (1)

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