Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer

Abstract

Propensity scoring (PS) is an established tool to account for measured confounding in non-randomized studies. These methods are sensitive to missing values, which are a common problem in observational data. The combination of multiple imputation of missing values and different propensity scoring techniques is addressed in this work. For a sample of lymph node-positive vulvar cancer patients, we re-analyze associations between the application of radiotherapy and disease-related and non-related survival. Inverse-probability-of-treatment-weighting (IPTW) and PS stratification are applied after multiple imputation by chained equation (MICE). Methodological issues are described in detail. Interpretation of the results and methodological limitations are discussed.

Implementing lower‐risk brief resolved unexplained events guideline reduces admissions in a modelled population

“…The integration of causal inference methods and guideline implementation is a novel concept and may help maximize dissemination once a CPG is formally released. IPTW and MICE are underutilized in paediatric emergency medicine research, although this type of methodology has been applied recently to cardiology, oncology, surgery, and population‐based survey research . However, it is important for researchers to explore the implication of changes to policies and guidelines as the epidemiology of paediatric emergency care is changing.…”

Fluid Management in Patients with Acute Respiratory Distress Syndrome and Diabetes Mellitus

“…Based on simulations of the model, the missing values were replaced. [ 23 – 25 ] We created 5 imputed data sets for the purposes of our analysis to create variability in the replaced missing values. The missing variables for diabetic and non-diabetic participants were imputed separately.…”

Empirical use of causal inference methods to evaluate survival differences in a real‐world registry vs those found in randomized clinical trials

“…Methods for creating the replicate datasets necessary for multiple imputations have been well developed, [27][28][29][30] but uncertainty persists about whether and how to incorporate the outcome into the process [31][32][33][34] and which of two predominant approaches for combining the results over the multiple imputations leads to better bias and variance reduction. [35][36][37][38][39][40] Both approaches to combining results initially construct a propensity score model within each imputation to estimate a single propensity score for each subject within that imputation. The "across" approach averages the individual propensity scores over imputations before analyzing the outcomes; the alternative "within" approach uses the propensity scores to analyze each individual imputation dataset separately and then combines results over imputations.…”

“…A number of simulation studies have compared the two approaches but have been limited in terms of the number and type of covariates and the type of outcome variables examined with, in particular, little methodological guidance on which approach would be best for analyzing survival data with missing values. [35][36][37][38][39][40] In the absence of strong recommendations, we assessed the performance of four analytic approaches: paired combinations of propensity matching vs IPTW, and averaging propensities before analyzing (across approach) vs analyzing before averaging (within approach). The point was to determine whether they would lead to different conclusions regarding treatment-attributable survival differences between RCT-eligible patients and those deemed not eligible in a large real-world registry.…”

“…Commonly recommended to account for the uncertainty associated with missing covariate values, multiple imputations involve simulating several replicate datasets by imputing values of the missing data according to distributional assumptions and then performing the planned analysis. Methods for creating the replicate datasets necessary for multiple imputations have been well developed, 27‐30 but uncertainty persists about whether and how to incorporate the outcome into the process 31‐34 and which of two predominant approaches for combining the results over the multiple imputations leads to better bias and variance reduction 35‐40 . Both approaches to combining results initially construct a propensity score model within each imputation to estimate a single propensity score for each subject within that imputation.…”