Real World Health Care Data Analysis. Uwe Siebert
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Chapter 5: Before You Analyze – Feasibility Assessment
5.2 Best Practices for Assessing Feasibility: Common Support
5.2.1 Walker’s Preference Score and Clinical Equipoise
5.2.2 Standardized Differences in Means and Variance Ratios
5.2.4 Proportion of Near Matches
5.3 Best Practices for Assessing Feasibility: Assessing Balance
5.3.1 The Standardized Difference for Assessing Balance at the Individual Covariate Level
5.3.2 The Prognostic Score for Assessing Balance
5.4 Example: REFLECTIONS Data
5.4.1 Feasibility Assessment Using the Reflections Data
5.4.2 Balance Assessment Using the Reflections Data
5.5 Summary
References
5.1 Introduction
This chapter demonstrates the final pieces of the design phase, which is the second stage in the four-stage process proposed by Bind and Rubin (Bind and Rubin 2017, Rubin 2007) and described as our best practice in Chapter 1. Specifically, this stage covers the assessment of feasibility of the research and confirmation that balance can be achieved by the planned statistical adjustment for confounders. It is assumed at this point that you have a well-defined research question, estimand, draft analysis plan, and draft propensity score (or other adjustment method) model. Both graphical and statistical analyses are presented along with SAS code and are applied as an example using the REFLECTIONS data.
In a broad sense, a feasibility assessment examines whether the existing data are sufficient to meet the research objectives using the planned analyses. That is, given the research