Sentence examples for full matching method from inspiring English sources
The optimal full matching method, on average, offered the best covariate balance compared to stratification and weighting methods for both Bayesian and frequentist propensity score approaches.
On average, the Bayesian stratification method had 6.2 % wider interval than conventional approach, the Bayesian weighting approach achieved an 8.9%% wider interval, and the Bayesian optimal full matching method obtained as much as 14%% wider interval.
That is, although the frequentist propensity score approach provided slightly better covariate balance for the propensity score stratification and weighting methods, the two-step Bayesian approach offered slightly better covariate balance under optimal full matching method.
After calculating a propensity score for each person, we performed optimal full matching, a method that allows one or multiple treated subjects to be matched to one or multiple untreated subjects [ 31].
Conversely, two state-of-the-art full atom-matching methods (SitesBase [83], SiteEngine [84]) failed in finding the same binding sites similar (SiteEngine Match score = 18.71; no output for SitesBase).
The full form terms are then mapped using the string matching method.
After the posterior propensity scores are obtained, a Bayesian outcome model is fit in the second step to estimate the treatment effect via various propensity score methods such as stratification, weighting and optimal full matching.
Because the asymmetric video has asymmetric frame rate, we apply stereo matching method for disparity estimation on a pair of synchronized full resolution and lower resolution frames.
We developed a full non-parsimonious model that included all variables as shown in tables 1 and 2. Each pair was matched by a greedy algorithm and the nearest available pair matching method among patients with an individual propensity score.
Full matching may often have superior performance to fixed M 1 matching (38).
We propose an optimized matching method where we ascertain family configurations that are most informative, while making sure that relevant features, such as the risk to relatives of cases, are similar in the sampled data and the full cohort.
