How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) estimating the PS after applying MI to impute missing confounders; 2) conducting PSM within each imputed dataset followed by averaging the treatment effects to arrive at one summarized finding; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model

The Paleozoic Origin of Enzymatic Lignin Decomposition Reconstructed from 31 Fungal Genomes

Wood is a major pool of organic carbon that is highly resistant to decay, owing largely to the presence of lignin. The only organisms capable of substantial lignin decay are white rot fungi in the Agaricomycetes, which also contains non–lignin-degrading brown rot and ectomycorrhizal species. Comparative analyses of 31 fungal genomes (12 generated for this study) suggest that lignin-degrading peroxidases expanded in the lineage leading to the ancestor of the Agaricomycetes, which is reconstructed as a white rot species, and then contracted in parallel lineages leading to brown rot and mycorrhizal species. Molecular clock analyses suggest that the origin of lignin degradation might have coincided with the sharp decrease in the rate of organic carbon burial around the end of the Carboniferous period

A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack

ObjectivesDetection of atrial fibrillation (AF) in post-cryptogenic stroke (CS) or transient ischemic attack (TIA) patients carries important therapeutic implications.MethodsTo risk stratify CS/TIA patients for later development of AF, we conducted a retrospective cohort study using data from 1995 to 2015 in the Stanford Translational Research Integrated Database Environment (STRIDE).ResultsOf the 9,589 adult patients (age ≥40 years) with CS/TIA included, 482 (5%) patients developed AF post CS/TIA. Of those patients, 28.4, 26.3, and 45.3% were diagnosed with AF 1-12 months, 1-3 years, and >3 years after the index CS/TIA, respectively. Age (≥75 years), obesity, congestive heart failure, hypertension, coronary artery disease, peripheral vascular disease, and valve disease are significant risk factors, with the following respective odds ratios (95% CI): 1.73 (1.39-2.16), 1.53 (1.05-2.18), 3.34 (2.61-4.28), 2.01 (1.53-2.68), 1.72 (1.35-2.19), 1.37 (1.02-1.84), and 2.05 (1.55-2.69). A risk-scoring system, i.e., the HAVOC score, was constructed using these 7 clinical variables that successfully stratify patients into 3 risk groups, with good model discrimination (area under the curve = 0.77).ConclusionsFindings from this study support the strategy of looking longer and harder for AF in post-CS/TIA patients. The HAVOC score identifies different levels of AF risk and may be used to select patients for extended rhythm monitoring

Baseline characteristics before and after inverse probability of selection weighting (IOPW).

Baseline characteristics before and after inverse probability of selection weighting (IOPW).</p

Fig 2 -

Kaplan Meier Curves in a) IPTW adjusted (for confounding only) cohort of second-line patients b) IPTW and IOPW adjusted (for both confounding and non-representativeness) cohort of first-line patients. Time is shown in days. Note that sample size was impacted by weighting in our analyses.</p

Comparison of variables involved in Step 1 and Step 2 analyses.

Comparison of variables involved in Step 1 and Step 2 analyses.</p

Directed acyclic graph to illustrate our two-step approach to adjust for confounding with inverse probability of weighting (IPTW) and non-representativeness with inverse probability of selection weighting (IOPW).

A = treatment assignment; Y = outcome; S = selection into original population from target population; C = confounders; E = treatment effect modifiers.</p

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.</p