Research Study Design and Analysis for Cardiologists Nathan D. Wong, PhD, FACC

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Research Study Design and Analysis for
CardiologistsNathan D. Wong, PhD, FACC
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• Advantages and disadvantages of different
  research study designs - which is best for you?
• Calculating sample size and power
• Which statistical tests to use
• Fallacies in presenting results
• Steps for protocol development
• Recommendations for further assistance

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Strength of Studies to Prove Causation

• Weakest Observational, cross-sectional
• Weak Observational, case-control
• Modest Observational, prospective
• Strongest Randomized clinical trial
• Within each of these studies, features that
  further strengthen or weaken the case include
  sample size, selection of comparison group
  (control or placebo), selection of study
  population, length of time of follow-up, and
  control for potential confounders

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Observational, cross-sectional

• Examines association between two factors (e.g, an
  exposure and a disease state) assessed at a
  single point in time, or when temporal relation
  is unknown
• Example lipids, blood pressure, and C-reactive
  protein levels
• Conclusions Associations found may suggest
  hypotheses to be further tested, but are far from
  conclusive in proving causation

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Observational, case-control

• Useful for uncommon or rare outcomes that could
  take years (or longer) to obtain sufficient cases
  in a prospective follow-up or population sample
• Often used for etiological studies of cancer
• Selection of control group (e.g., hospital vs.
  healthy community controls) and consideration of
  possible confounders crucial
• Cannot always be certain about temporal relation
  between exposure and disease outcome since
  historical information on exposure history is
  obtained

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Prospective cohort studies

• Examples Framingham Heart Study, Cardiovascular
  Health Study (CHS), Multiethnic Study of
  Atherosclerosis (MESA), Nurses Health Study
• Advantages large sample size, ability to follow
  persons from healthy to diseased states, temporal
  relation between risk factor measures and
  development of disease
• Disadvantages expensive due to large sample size
  often needed to accrue enough events, many years
  to development of disease, possible attrition,
  causal inference not definitive

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Randomized Clinical Trial

• Considered the gold standard in proving causation
  by reducing in risk factor of interest--e.g.,
  cholesterol inconclusive as risk factor until
  early trial showed that lowering it lowered CHD
  risk
• Expensive, labor intensive, attrition from loss
  to follow-up or poor compliance can jeopardize
  results, esp. if more than outcome difference
  between groups
• Conditions are highly controlled and may not
  reflect clinical practice or the real world
• Randomization equalizes known and unknown
  confounders/covariates so that results can be
  attributed to treatment with reasonable confidence

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Guidelines for Sample Size / Power Determination

• Necessary for any research grant application
• Need to estimate what control group rate of
  disease or outcome is
• Need to state what is minimum difference (effect
  size) you want to detect that is clinically
  significant--e.g., difference in rates, or risk
  ratio
• Either power can be estimated for a fixed sample
  size at fixed alpha (usually 0.05 two-tailed) for
  different effect, OR sample size can be estimated
  for a given power (usually 0.80) for different
  effect sizes

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Statistics and Statistical Procedures for
Different Study Designs

• Cross-sectional Pearson correlation, Chi-square
  test of proportions- prevalence odds ratio for
  likelihood of factor Y in those with vs. w/o X
• Case-control Odds ratio for likelihood of
  exposure in diseased vs. non-diseased--
  Chi-square test of proportions / logistic
  regression
• Prospective Relative risk (RR) for incidence
  of disease in those with vs. without risk factor
  of interest, adjusted for covariates and
  considering follow-up time to event--Cox PH
  regression. Correlations and linear/ transformed
  regression used for continuous outcomes.

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Statistics and Statistical Procedures (continued)

• Randomized clinical trial Relative risk (RR)
  of event occurring in intervention vs. control
  group - Cox PH regression
• For continuously measured outcomes, such as
  pre-post changes in risk factors (lipids, blood
  pressure, etc.) initial treatment vs. control
  differences examined by Students T-test,
  repeated measures ANOVA / ANCOVA used for
  multiple measures across a treatment period and
  covariates

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Fallacies in Presenting Results Statistically
vs. Clinically Significant?

• Having a large sample size can virtually assure
  statistically significant results--but at a very
  low correlation or relative risk
• Conversely, an insufficient sample size can hide
  (not significant) clinically important
  differences
• Statistical significance directly related to
  sample size and magnitude of difference, and
  indirectly related to variance in measure

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Steps to Protocol Development

• Aims and Hypotheses
• Background
• Methods, including subject recruitment,
  eligibility criteria, screening procedures,
  treatment phase or follow-up procedures
• Study power and sample size justification
• Statistical methods of analysis
• Potential study limiations

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Data Collection / Management

• Always have a clear plan on how to collect data--
  design and pilot questionnaires, case report
  forms.
• The medical record should only serve as source
  documentation to back up what you have coded on
  your forms
• Use acceptable error checking data entry screens
  or spreadsheet software (e.g., EXCEL) that is
  covertable into a statistical package (SAS highly
  recommended and avail via UCI site license)
• Carefully design the structure of your database
  (e.g, one subject/ record, study variables in
  columns) so convertible into an analyzable format

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Where to Go for Help

• Epidemiology and statistics books
• Institutional Review Board - considers mainly
  subject projection issues
• Deans Scientific Review Committee - considers
  appropriateness of research design, procedures,
  statistical considerations
• Questions? ndwong_at_uci.edu