Module 17 · Part 6 · Using Your Data
Analyzing Registry Data
Understand the analytic approaches used in registry research and how to design your registry for analysis from the start.
Patient Registries 101 · Dr. Danielle Boyce · EpilepsyLive
Design for analysis
- The most common registry analysis failure: data was collected without a prespecified analysis plan.
- Pre-specify your primary analyses before collecting data.
- Primary outcome(s)
- Primary exposure(s) or comparison groups
- Covariates to adjust for
- Statistical methods
Design for analysis (cont.)
- Missing data handling approach
- Your SAB should review and approve the analysis plan.
Disease characterization
- Prevalence / incidence estimation , How common is the disease? How many new cases per year?
- Symptom frequency and distribution , What proportion of participants have each clinical feature?
- Demographic analysis , Age at onset, sex distribution, time to diagnosis
- Genotype phenotype analysis , Which genetic variants are associated with which clinical features?
Natural history
- Disease progression modeling , How do symptoms change over time?
- Survival analysis (time-to-event) , Time to disease milestones, time to treatment initiation
- Longitudinal mixed models , Track individual trajectories and population level trends
Treatment patterns and outcomes
- Treatment use , Which treatments are participants receiving, and for how long?
- Comparative effectiveness , Do participants on treatment A have better outcomes than participants on treatment B? (Requires careful confounding adjustment)
Small sample sizes
- Most rare disease registries operate with small N.
- Pre-specify analyses to avoid overfitting
- Use Bayesian methods where appropriate (allow incorporation of prior knowledge)
- Be cautious about subgroup analyses, report effect sizes and confidence intervals, not just p-values
- Power analyses should be conducted before data collection, not after
Missing data
- Missing data is ubiquitous in observational registries.
- Complete case analysis , Easiest; valid only when data is missing completely at random (MCAR), which is rarely true
- Multiple imputation , Appropriate for data missing at random (MAR); widely used
- Sensitivity analysis , Test how your conclusions change under different missing data assumptions
Confounding
- Registry data is observational, participants are not randomized to treatments or exposures.
- Propensity score matching or weighting , Balance treatment groups on observed covariates
- Instrumental variable analysis , For when confounding by indication is severe
- Target trial emulation , Explicitly design the analysis as if it were a randomized trial
Key resources
- AHRQ Registry User's Guide Chapter 6: Analysis
- Book of OHDSI, Characterization Chapter
- FDA Guidance on Natural History Data
- STROBE Statement, Reporting observational studies
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