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Considerations for Statistical Analysis in Observational Comparative Effectiveness Research. Prepared for: Agency for Healthcare Research and Quality (AHRQ) www.ahrq.gov. Outline of Material. This presentation will: - PowerPoint PPT Presentation
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Considerations for Statistical Analysis in Observational Comparative Effectiveness
ResearchPrepared for:
Agency for Healthcare Research and Quality (AHRQ)www.ahrq.gov
This presentation will: Describe the key variables of interest with
regard to factors that determine appropriate statistical analysis
Propose descriptive analysis or graph according to treatment group
Propose the model that will be used for primary and secondary analytical objectives
Outline of Material
When observational data are used in comparative effectiveness research, careful and often complex analytic strategies are required to adjust for confounding.
Statistical considerations Descriptive Statistics/Unadjusted Analyses Traditional Multiple Regression Model Selection Model Assumptions Propensity Scores/Disease Risk Scores Instrumental Variables Missing Data Considerations Time-Varying Exposures/Covariates
Introduction
Descriptive statistics Continuous variables: measures of range,
dispersion, and central tendency Categorical variables: frequency (n) and
percentage Data distributions: Kaplan-Meier plots
Unadjusted analysis Conducted to identify covariates associated with
the exposure and/or study outcome (e.g., t-test) Can provide a broad picture of study subject
characteristics
Descriptive Statistics and Unadjusted Analysis
Traditional multivariable regression Control for potential confounding variables in
the estimation of treatment effects Useful when there is a sufficient number of
outcome events per covariate and exposure is not infrequent
If exposure is common and the outcome rare, consider propensity scores.
If exposure is infrequent, consider disease risk scores.
Adjusted Analysis
Model Selection Number of Followup Measures and Time Intervals
Single measureRepeated measure, fixed intervals
Repeated measure, variable intervals
Outcome measure No clustering Clustering (e.g.,
multisite study)
Dichotomous
Logistic regression
Multilevel (mixed) logistic regression, GLMM, GEE, conditional logistic regression
Repeated measures ANOVA (MANOVA), GLMM, GEE
GLMM, GEE
Continuous Linear regression
Multilevel (mixed) linear regression, GLMM, GEE
Repeated measures ANOVA (MANOVA), GLMM, GEE
GLMM, GEE
Time to event
Cox proportional hazards regression
Variance-adjusted Cox model or shared frailty model
Time to event (aggregate or count data)
Poisson regression
Multilevel (mixed) Poisson regression
Propensity scores measure the probability of receiving treatment (or exposure) conditional on observed covariates.
Propensity scores are favorable in studies with a common exposure and rare or multiple outcomes.
They can be used in subclassification or stratification, matching, and weighting.
They include covariates that are true confounders or at least related to study outcome.
Propensity Scores
Disease risk scores (DRSs) measure the estimated probability or rate of outcome occurrence as a function of covariates.
Estimation approaches: Fit regression model for entire cohort, adjusting for
exposure Fit regression model for unexposed/referent group Compute fitted values assuming unexposed/referent
group for all study subjects DRSs are favorable in studies having a common
outcome and rare exposure. DRSs are useful for effect modification by disease risk.
Disease Risk Scores
Instrumental variables are measures that are causally related to exposure but unrelated to outcome and study covariates.
They are useful for adjusting for potential unmeasured confounders. However, it may be difficult to identify a high-
quality instrument. They can be used in conjunction with
traditional multiple regression and propensity score matching.
Instrumental Variables
Observational studies commonly have missing data.
Missingness can be characterized by using exploratory data analyses. Complete-case analysis for subjects with no
missing data: Can reduce sample size, limiting efficiency Can result in potential bias if missingness is
differential between groups Imputation for missing completely at random
or missing at random
Missing Data Considerations
Time-dependent Cox regression models can account for time-varying exposures and covariates. However, difficult issues arise when both treatment
and confounding variables vary over time. Inverse-probability-of-treatment weighting can be
used to estimate a marginal structure model. This approach is a generalization of propensity score
weighting to the time-varying treatment context. Intent-to-treat analysis can be conducted in which
exposure status is assumed throughout followup when treatment adherence is low.
Time-Varying Exposures/Covariates
Observational comparative effectiveness studies are often strongly affected by confounding.
Thoughtful application of statistical approaches can adjust for confounding and improve causal inference.
An appropriate analytical technique is based on assumptions.
Consider the effect of missing data on analyses.
Sensitivity analyses can address residual confounding.
Conclusion
Summary Checklist (1 of 3)
Guidance Key ConsiderationsDescribe the key variables of interest with regard to factors that determine appropriate statistical analysis
• Independent variables (when are they measured, fixed, or time-varying; e.g., exposures, confounders, effect modifiers)
• Dependent variables or outcomes (continuous or categorical, single or repeated measure, and time to event)
• State if there will be a “multilevel” analysis (e.g., looking at effects of both practice-level and patient-level characteristics on outcome)
Summary Checklist (2 of 3)
Guidance Key ConsiderationsPropose descriptive analysis or graph according to treatment group
• Should include the available numbers per group, n missing for all key covariates, and distributions or graphs that are needed to decide if transformation of data is needed or determine an accurate functional form of the final model
• Should include all potential confounders and effect modifiers to assess initial covariate balance by study group
Summary Checklist (3 of 3)
Guidance Key Considerations
Propose the model that will be used for primary and secondary analytical objectives
• Should take into account the design (independent vs. dependent observations, matched, repeated measurement, clustered), objectives, functional form of model, fixed/time-varying followup period, fixed and time-varying exposure and other covariates, assessment of effect modification/heterogeneity, type of outcome variables (categorical, ordinal, or continuous), censored data, and the degree of rarity of outcome and exposure
• Should propose a suitable approach for adjusting for confounding (e.g., multiple regression model, propensity scores, instrumental variables [could be secondary or main analysis])