Study Design and Statistical Analysis
Study Design and Statistical AnalysisAnna Hitron, PharmD., MS,
MBA, BCOPOncology Pharmacy SpecialistBaptist Health Louisville
[email protected]
September 2014ObjectivesReview the types of study designs used
to answer common research questionsProvide key elements necessary
for well-designed research Discuss common pitfalls/errors to
consider in good study designOutline the statistical methods for
data analysis and hypothesis testing
Why Research?Allows for the identification of measurable
relationships between factorsOften stems from clinical
observationsOwn practiceLiterature basedProvides evidence for
practiceAllows for applicability of new data into current
practice
Elements of a Research StudyResearch question/purposeClear,
direct with measurable endpointsBackground/SignificanceHelps to
determine how question relates to whats already knownRationale for
why this is importantMay provide additional study
considerationsDesignIndentify subjects, groups, exposures,
outcomesAnalysisProvides what was found (ie: results!)
Factors to consider in study designStructural consideration What
should be defined by the authors?Type of studyStudy population
Exposure/interventionStudy outcomeInference considerations What
impacts how this study relates to other populations?ValidityStudy
biasConfoundersError
Types of studiesDescriptive studyProvides initial look at
question often seen as case reports, surveys, etc.Example:
Case-report published where child developed skin discoloration from
candy consumptionObservational studiesCross-sectionalProvides
one-time look slice of life Example: How many children that have
eaten candy develop skin discoloration?Limited conclusions can be
drawn only determines prevalence of eventESTABLISHING
CAUSALITYCross-sectional looks at the outcome and exposure at the
same time6Types of studiesObservational studiesCase-control
Compares exposure over known patients with and without outcomeGood
for rare events since outcome is set at study
onsetRetrospectiveExample: Select 5 patients with discoloration and
10 without look to see if had candy consumption at anytime prior to
eventHighly subject to confounders and biasESTABLISHING
CAUSALITYCase control establishes outcome and looks for
exposure7Types of studiesObservational studiesCohortCompares
outcome between patients with different exposuresProvides strongest
measure of association in observational studiesAllows for most
control of confounding variablesCan be retrospective or
prospectiveExample: Select 200 patients to follow for development
of skin discoloration look to see what factors may cause (type of
school attended, parent income, pet ownership, candy
consumption)Limited by cost, duration of follow-up ESTABLISHING
CAUSALITYCase control establishes outcome and looks for exposure;
cohort establishes exposures and looks for outcome. Cohorts are
always followed forward in time. If the cohort was established in
the past, its retrospective; if they are established now, its
prospective8Types of studiesExperimental studies (aka: randomized
controlled trials (RCT))Gold-standard for evidence based
medicineAllows for most control of confounders and reduction of
biasTruly allows for establishment of causal relationshipExample:
Give kids in group A candy and kids in group B only get vegetables.
Look to determine which group has a higher incidence of skin
discolorationLimited by high cost, ethical standardsESTABLISHING
CAUSALITYCase control establishes outcome and looks for
exposure9Study PopulationImportant to define early onInclusion
criteria what is the population to be evaluated?May be impacted by
demographic, clinical, geographic or temporal
characteristicsExample: Patients with prostate cancer using
Kentucky Medicaid between January 1, 2000 and December 31,
2005Impacts internal validityExclusion criteria will help to reduce
confounding variablesEliminates patients that would otherwise be
studied, but have 1 or 2 characteristics that would limit
follow-up, data collection or put them at increased riskExample:
Exclude patients who had compliance rates of 180/110 is 108%
(2.08x) greater in patients that have just come from the DMV than
those who have not.Hypertensive urgencyNormal blood
pressureTotalsDMV visit7596121No DMV
visit82218350Totals157314471Calculating relative riskOften risk
will be given over time since not all patients in the cohort are
followed for the same timeRisk identified in person-yearsMultiplies
total for each exposure by time followedIncidence is described as
number of diseases over time followedRelative risk measure is the
sameOther measures of associationAttributable risk/absolute risk
reductionMeasures the excess risk added by the exposureAttributable
risk (AR or ARR)| Incidence Exposure Incidence Unexposed |Number
needed to treatOutlines how many people must receive exposure to
prevent outcomeNNT = 1/AR
Other measures of associationIncidence in high exercise group
(exposed) 19/9,176=0.0021 Incidence in low exercise group
(unexposed)358/83,649=0.0043Attributable risk (reduction)|
Incidence Exposure Incidence Unexposed || 0.0021 0.0043 | =
0.002222 cases of ovarian cancer are attributed to not exercising 7
hrs/wkNumber needed to treat 1/0.0022 = 454.5 people would need to
exercise 7 hrs/wk to prevent 1 case of ovarian cancer
Ovarian CANo Ovarian CATotalsExercise 7
hrs/wk1991579,176Exercise < 7
hrs/wk3588329183,649Totals3779244892825Statistically Controlling
for ConfoundersUse to measure the effect of multiple variables on
the outcome (ie: Do these factors predict my outcome? How
well?)Statistically chosen variablesClinically chosen
variablesImportant to understand what is independent and dependent
variableDependent variable (y) the factor that is the outcome of
interest. Is it affected by the other variables?Independent
variable (x) other factors that may have an impact on the outcome
(ie: confounders)
y = a + bx1 + bx2 + bx3 + + bxn Prediction modelStatistically
Controlling for ConfoundersMethod used is based on data type of
primary (dependent) outcome variableNominal = Mantel-HanzelOrdinal
or Continuous = ANCOVARegression models can be used to help
determine how different variables interplay to produce a
resultNominal = Logistic RegressionOrdinal or Continuous = Multiple
RegressionAssumes linear relationship of primary exposure to
outcomeSeek statistician guidance
Survival AnalysisUsed if time is important factor in the
measurement of outcomeTime to ________ (progression, recurrence,
etc.)SurvivalAccounts for censored dataPatients that are followed,
but unable to determine when outcome occurredExample: In evaluating
survival, patients still be alive at study end or loss to follow-up
are censored. Unable to determine how long it will take for them to
dieUses Kaplan-Meier curves (descriptive), log-rank testing
(hypothesis testing) and Cox Proportional Hazard Regression
(regression analysis for confounders)Example Kaplan-Meier
Curve/Log-Rank Testing
Patient censored lost to follow-up before cancer foundPatient
censored study ended before cancer foundQuestion: What is the
development of cancer over time in at-risk patients receiving
preventative treatment?Is null rejected or not?In the report of the
National Surgical Adjuvant Breast and Bowel Project P-1 Study
13,175 women enrolled from 1992-97 examined the use of tamoxifen 20
mg daily for prevention in high risk patients for 5 years (mean 4
years). In this trial, most patients were 40-60 years old and had 1
1st degree relative with cancerTamoxifen 20 mg Qday x 5 years was
found to decrease overall risk of invasive and non-invasive breast
cancer by 49% to 50% (p
