戻る
「早戻しボタン」を押すと検索画面に戻ります。 [閉じる]

コーパス検索結果 (１語後でソート)

通し番号をクリックするとPubMedの該当ページを表示します
1 core, genotype, level of BB exposure, and BB propensity score.
2 in at baseline, matched 1:1 on age, sex, and propensity score.
3 , adjusted for hospital-level clustering and propensity score.
4 ame time (unexposed), using a time-dependent propensity score.
5 verse probability weighting according to the propensity score.
6 BR and NOM patients were matched 2:1 using a propensity-score.
7 ion and 375 were excluded because of extreme propensity scores.
8  identified 4,878 patients with well-matched propensity scores.
9 ment weighting (IPTW) using high-dimensional propensity scores.
10 ronic hepatitis B virus (HBV) controls using propensity scores.
11 cytokine storm and matched to controls using propensity scores.
12  probability of treatment weighting based on propensity scores.
13  healthcare encounter history using exposure propensity scores.
14 omparison group from two other regions using propensity scores.
15 ome studied, and models were weighted on the propensity scores.
16 le logistic regression models correcting for propensity score 1 (aOR 1.15 95%CI 1.03-1.27; p = 0.007)
17                        After 1:1 matching on propensity score, 123 752 patients were identified in co
18  1 (aOR 1.15 95%CI 1.03-1.27; p = 0.007) and propensity score 2 (aOR 1.15 95%CI 1.04-1.27; p = 0.007)
19 h 3910 patients treated with SE-THV by using propensity score (25 clinical, anatomical, and procedura
20                  Linear regression model and propensity-score adjusted cox proportional model were us
21                                              Propensity-score adjusted logistic regression models wer
22                                              Propensity score-adjusted costs were significantly highe
23 osts of hospitalization were estimated using propensity score-adjusted mortality outcomes for 2010-20
24                                      We used propensity score-adjusted regression models to determine
25                   Implementing a generalized propensity score adjustment approach with 3.8 billion pe
26 bstantial control of measured confounding by propensity score adjustment, and minimal residual system
27                                      After a propensity score adjustment, the difference was signific
28                                            A propensity score analysis (PSA) was used to compare outc
29                                    Moreover, propensity score analysis does not compensate for poor s
30                                      Matched propensity score analysis found no significant differenc
31                                              Propensity score analysis is overall a more favorable ap
32 e, multicenter cohort study which included a propensity score analysis using an odds of treatment wei
33              To reduce any selection bias, a propensity score analysis was applied.
34                                           On propensity score analysis, HD was associated with a +0.4
35 an inverse probability of treatment weighted propensity score analysis.
36            This finding was confirmed in the propensity score analysis.
37 an inverse probability of treatment weighted propensity score analysis.
38 rse probability of treatment weighting using propensity score analysis.
39                             We constructed a propensity score and applied inverse probability of trea
40 nts to evaluate associations between aspirin propensity score and progression to late AMD (and its su
41                                              Propensity scoring and multivariable models were used to
42                     After adjustment for the propensity score, and accounting for clustering of patie
43  input sequence, post-processing of disorder propensity scores, and a feature selection that optimize
44 robability-of-treatment weights, generalized propensity scores, and standard conditional linear regre
45 hted Cox proportional hazards model, using a propensity score based on age, staging, surgery, chemoth
46 apy and patients who did not were matched by propensity scores based on factors associated with the u
47              These groups were matched using propensity scores based on patients' and hospitals' pote
48 e treatment assignment and covariates in the propensity score-based method or a model for the outcome
49                                              Propensity score-based methods or multiple regressions o
50                     We therefore conducted a propensity-score-based comparative effectiveness analysi
51  patients in the early and late groups using propensity scores calculated on the basis of their basel
52                                            A propensity score comparing participants receiving a tumo
53             Retrospective analysis employing propensity score covariate adjustment method of prospect
54 between the exposed and unexposed cohorts, a propensity score for being prescribed FHT was created us
55                                        Using propensity scores for digoxin discontinuation, estimated
56                                        Using propensity scores for receipt of loop diuretics estimate
57                                          Two propensity scores (for being male) were obtained for con
58 istance matching, and fine stratification by propensity score (FS).
59 distance matching and fine stratification by propensity score (FS).
60   In the model with inverse weighting by the propensity score, infectious disease consultation was as
61 istic regression, propensity score matching, propensity score inverse probability of treatment weight
62                                              Propensity-score (inverse probability of treatment weigh
63 ly well-suited to settings where an exposure propensity score is difficult to model.
64 of in-hospital mortality across quintiles of propensity score (Mantel-Haenszel odds ratio: 0.48; 95%
65                                              Propensity score match (PSM) analysis was performed with
66                                        A 1:1 propensity score match was performed to account for base
67   We employed a prevalent new-user design to propensity-score match, in a 1:2 ratio, patients switchi
68 -cause death, and composite outcome in a 3:1 propensity score matched cohort in patients with AF who
69             From 4153 patients, we created a propensity score matched cohort of 386 patients, includi
70 ients undergoing AF ablation compared with a propensity score matched cohort of patients treated with
71 iate comparisons were made, as well as using propensity score matched cohorts to determine if VTE che
72                                Patients were propensity score matched for age, body mass index, sex,
73 reatment approaches in the whole cohort, and propensity score matched groups.
74 3 TAVR were not significantly different to a propensity score matched SAVR cohort.
75                                Retrospective propensity score matched study comparing MIPD in 14 cent
76  351 patients newly prescribed apixaban were propensity score matched to 39 351 patients newly prescr
77            Exposed infants (n = 38 473) were propensity score matched to nonexposed controls (n = 76
78 newly prescribed an SGLT2 inhibitor were 1:1 propensity score matched to patients newly prescribed a
79                                Children were propensity score matched using condition at admission an
80                                         This propensity-score matched analysis demonstrated significa
81                                       In the propensity-score matched cohort (n=7746; 3873 per group)
82 rates compared between the test campus and a propensity-score matched cohort at the control campus.
83                              A single-center propensity-score matched cohort study, including all con
84 OE) for esophageal cancer using a nationwide propensity-score matched cohort.
85 esophagectomy was equivalent to open in both propensity-score matched cohorts of patients undergoing
86 was calculated by comparing IT patients to a propensity-score matched control cohort of severely inju
87 me comparison, contemporary OE controls were propensity-score matched from NSQIP and NCDB databases.
88                 Outcomes were compared among propensity-score matched groups using Cox proportional h
89 rwent DS or ER in the curative setting, were propensity-score matched in a 1:2 ratio.
90                                Patients were propensity-score matched on the likelihood of undergoing
91                           Compared with 2993 propensity-score matched patients undergoing a lateral o
92 ary analysis, and 4,264 were included in the propensity-score matched secondary analysis.
93                           In this nationwide propensity-score matched study, DS as a BTS for LSOCC wa
94                       RYGB patients were 1:1 propensity-score matched with sleeve gastrectomy patient
95 d congenital heart disease patients, using a propensity score-matched (PSM) analysis.
96  to compare baseline characteristics and 1:1 propensity score-matched adjusted 4-month follow-up heal
97 r composite cardiovascular events outcome in propensity score-matched analyses were 1.10 (95% CI, 1.0
98                                            A propensity score-matched analysis of 334 open and 334 VA
99                                 A one-to-one propensity score-matched analysis of patients who underw
100                                        A 1:1 propensity score-matched analysis was also performed to
101 riable Cox proportional hazards modeling and propensity score-matched analysis was used to compare th
102                                           In propensity score-matched analysis, CAUTI/1000-patients w
103                               In an adjusted propensity score-matched analysis, patients with NSAID A
104 riable Cox proportional hazards modeling and propensity score-matched analysis.
105                          This retrospective, propensity score-matched case-control study assessed the
106                          Furthermore, a 1:10 propensity score-matched cohort comprising AD patients w
107                                            A propensity score-matched cohort of 8064 HCV-infected pat
108 es in the United States, we identified a 1:1 propensity score-matched cohort of T2D patients >=18 yea
109 An exploratory comparison of survival with a propensity score-matched cohort receiving standard nCRT
110     This Danish, nationwide, register-based, propensity score-matched cohort study used Cox regressio
111                            Compared with the propensity score-matched cohort, a significantly longer
112 s assessed by Cox regression models in a 1:1 propensity score-matched cohort.
113 tion was not documented in the corresponding propensity score-matched cohorts.
114 n women) who underwent CT angiography and 32 propensity score-matched control patients (71 years +/-
115                       Age-, race-, sex-, and propensity score-matched controls (1:1) were also identi
116 0 through December 2015 (cases), and 449,840 propensity score-matched controls from Taiwan's National
117 n 17.9% of plasma recipients versus 28.2% of propensity score-matched controls who were hospitalized
118 of acute kidney injury (AKI) compared with a propensity score-matched ICM-unexposed patient group.
119 ter CT with intravenous ICM to be similar to propensity score-matched ICM-unexposed patient groups; s
120 ast material-enhanced CT by comparing with a propensity score-matched ICM-unexposed patient sample un
121  length of stay (LOS)] were compared between propensity score-matched MaSBO and SBO patients.
122 for baseline clinical characteristics, a 1:1 propensity score-matched model was applied.
123 can Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negat
124                                 Among 12 584 propensity score-matched pairs (mean [SD] age, 58.3 [10.
125 nsradial) in the overall population and in a propensity score-matched population involving 2978 trans
126                                       In the propensity score-matched population, VC related to the s
127 bserved in the total cohort and in 32 vs. 32 propensity score-matched recipients CONCLUSION:: NRP and
128 a), and their features were compared with 21 propensity score-matched recipients with FMF amyloidosis
129 of 1850 unique patients were included in the propensity score-matched sample (925 exposed to ICM [mea
130    Multivariate Cox regression analysis of a propensity score-matched sample comparing those who rece
131  Materials and Methods In this retrospective propensity score-matched study approved by the instituti
132             We are conducting a prospective, propensity score-matched study assessing the efficacy of
133  reported results from interim analysis of a propensity score-matched study suggesting that early tre
134                               We conducted a propensity score-matched study to explore the associatio
135  FMF-associated AA amyloidosis (group 1) and propensity score-matched transplant recipients (group 2,
136  US claims datasets (2013-2018) and were 1:1 propensity score-matched, adjusting for >95 baseline cov
137                                    Iterative propensity score-matched, survival (Cox regression and K
138            We reported consistent results in propensity-score-matched analyses and after accounting f
139                                              Propensity score matching (ratio 1:2) was performed on t
140 was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 1
141                                         In a propensity score matching analysis the adjusted risk of
142                                           In propensity score matching analysis, 7/159 (4.4%) recurre
143                                              Propensity score matching and Cox analyses were used to
144                                              Propensity score matching and Cox modeling were used for
145     Additional sensitivity analyses included propensity score matching and Cox multivariable models.
146 ontrolling for prognostic score and 2) using propensity score matching and inverse probability weight
147                                      We used propensity score matching and logistic regression to est
148 t on postoperative complications with use of propensity score matching and multilevel, multivariable
149                                              Propensity score matching and multivariable analyses wer
150                                              Propensity score matching applied on the cohort of 151 p
151                                              Propensity score matching based on 23 baseline covariate
152 wide cohort of 9 million Danes, we performed propensity score matching between patients with left-sid
153                                        After propensity score matching BFP beneficiary to nonbenefici
154                                              Propensity score matching by baseline characteristics wa
155                                              Propensity score matching for baseline characteristics a
156 r balancing on patient characteristics using propensity score matching in each imputed dataset.
157                                              Propensity score matching of open PD patients with jaund
158 CI SNFs were matched with control SNFs using propensity score matching on 2013 SNF characteristics.
159                                              Propensity score matching resulted in 2 comparable group
160                                              Propensity score matching resulted in 50 well-matched pa
161                                              Propensity score matching resulted in a pseudo-randomise
162                                              Propensity score matching revealed that early drain remo
163                        After stratifying the propensity score matching sample, this benefit persisted
164 s who were included (75 with quadritherapy), propensity score matching selected 64 unique pairs of pa
165                                      We used propensity score matching to compare the perioperative c
166 he trial-mimicking populations, we conducted propensity score matching to control for >120 preexposur
167            Meta-analytic approaches included propensity score matching to reduce confounding.
168                                              Propensity score matching usually performed almost as we
169                                              Propensity score matching was performed and differences
170                                      The 1:1 propensity score matching was performed by using 23 cova
171                                              Propensity score matching was used for the analysis of o
172                                              Propensity score matching was used for the analysis of o
173                                              Propensity score matching was used to balance treatment
174                                              Propensity score matching was used to minimize potential
175     Multivariate Cox regression analysis and propensity score matching were done to minimise bias.
176         Cox-proportional hazard models after propensity score matching were used to calculate the haz
177     Cox proportional-hazards model (PHM) and propensity score matching were used to identify predicto
178 gh a receiving operator curve analysis after propensity score matching with a series of female blood
179 ; HR = 0.81; 95% CI: 0.77-0.86; P < 0.001 in propensity score matching).
180 apy; HR=0.90; 95% CI: 0.75-1.08; P = 0.25 in propensity score matching).
181  of 194 332) patients were identified before propensity score matching, 11 490 (13.4%) of whom underw
182                                        Using propensity score matching, 12,564 matched pairs were for
183                                        After propensity score matching, 4,596 users of trazadone and
184                                        After propensity score matching, 612 patients with 806 polyps
185                                        After propensity score matching, all patients were divided int
186 ould be dosed "enough," logistic regression, propensity score matching, and inverse probability weigh
187                                    Following propensity score matching, early use of NSAIDs was not s
188                 Survival analysis, following propensity score matching, found no significant differen
189                                        After propensity score matching, having insurance was associat
190 that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching
191 that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching
192                                        After propensity score matching, mortality was higher with ECL
193 s were performed to control for confounders: propensity score matching, multivariable survival, and i
194                                        After propensity score matching, no differences in baseline ch
195 aditional multivariable logistic regression, propensity score matching, propensity score inverse prob
196                                        After propensity score matching, there were no significant dif
197                                        After propensity score matching, Valve Academic Research Conso
198                                        After propensity score matching, we included 45 patients from
199 atched on specified clinical variables using propensity score matching.
200 ion models, machine learning techniques, and propensity score matching.
201 azards regression adjusted with Kernel-based propensity score matching.
202 sted Cox proportional hazards regression and propensity score matching.
203 tional Cancer Data Base were evaluated using propensity score matching.
204  incident tuberculosis was estimated through propensity score matching.
205 inical evidence of COVID-19 and eligible for propensity score matching.
206  and 68 healthy control subjects selected by Propensity Score Matching.
207 sure and survival, cases were compared using propensity score matching.
208 l mortality using multivariable modeling and propensity score matching.Measurements and Main Results:
209                                With using of propensity scores matching to reduce treatment selection
210                                            A propensity-score matching analysis for MIE versus OE was
211 n the unmatched consecutive cohort and after propensity-score matching for harmonization.
212 verall and in those with hypertension, after propensity-score matching for receipt of each medication
213 ncome classification and drug resistance and propensity-score matching on age, sex, geographic site,
214                                        After propensity-score matching on lymph node ratio, adjuvant
215      For comparison of in-hospital outcomes, propensity-score matching was employed.
216                                              Propensity-score matching was performed for 12 clinicopa
217                                              Propensity-score matching was used to create 2 groups of
218                                        After propensity-score matching, 1555 patients were included (
219                                        After propensity-score matching, early removal was associated
220 le Cox regression analysis, before and after propensity-score matching, stratified for patients with
221 ox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardi
222 estigated through multivariable analyses and propensity-score matching.
223 sided obstructive colon cancer (LSOCC) using propensity-score matching.
224                                  We repeated propensity score-matching according to the hemoglobin na
225 oral transcatheter aortic valve replacement; propensity score-matching identified pairs of patients w
226                                          The propensity score method generated a low and intermediate
227                                          The propensity score method was used to test the hypothesis
228                                              Propensity score methods are an important tool to help r
229                                              Propensity score methods are increasingly being used in
230                                              Propensity score methods in time-to-event analyses evalu
231                                              Propensity score methods were utilized to address confou
232 ndividuals in each group were adjusted using propensity score methods.
233                                      We used propensity-score methods to match each e-cigarette user
234 ing and stratification based on an expansive propensity score model with all pre-treatment patient ch
235                                      Using a propensity score model, 421 pairs of patients were match
236 rue underlying values are mismeasured in the propensity score model.
237 between the two groups, and we estimated the propensity score of being a beneficiary of the BFP using
238 ernative to approaches based on the exposure propensity score, or as a complement to them.
239                Analyses were conducted using propensity score overlap weighting to balance baseline c
240                                        After propensity score overlap weighting was applied, the haza
241                                              Propensity scores (patients' estimated probability of re
242                                      Through propensity scoring, patients who received outpatient ID
243                                            A propensity score (PS) for EDE vs no de-escalation (NDE)
244 ss the effects of AC on outcomes, we applied propensity score (PS) matching and marginal structural m
245 , we combined network-based prediction and a propensity score (PS) matching observational study of 26
246                 We used hierarchical models, propensity score (PS) matching, and inverse probability
247 rvals (CIs) using fine stratification on the propensity score (PS) to control for over 70 confounders
248                                              Propensity score (PS) was calculated to address potentia
249 on of selection bias through methods such as propensity score (PS) weighting.
250  aim, the study groups were matched based on propensity score (PS).
251 pital mortality was assessed by quintiles of propensity score (PS).
252                                              Propensity scores (PSs) were computed, including preiden
253 visible [>=2 mm] recession) and to calculate propensity scores (PSs); 2) Youden's J statistic to sele
254 tivariable logistic regression stratified by propensity score quintile to account for PV and non-PV g
255 o: 0.21; 95% CI: 0.12 to 0.35; p < 0.001) or propensity score (rate ratio: 0.16; 95% CI: 0.09 to 0.26
256 confounding adjustment with high-dimensional propensity score reached a stable state already at analy
257 multivariable logistic regression (using the propensity score) showed that positive factors associate
258 erse probability of treatment weighting, and propensity score stratification using this clinical ques
259                                        A 1:1 propensity score system accounted for nonrandom treatmen
260                            After matching by propensity score, the characteristics of the users and n
261     While less widely used than the exposure propensity score, the disease risk score approach might
262                                    We used a propensity score to control for confounding factors rela
263 itors were matched by using time-conditional propensity scores to 208 757 recipients of DPP-4 inhibit
264 nd compared using log-rank test, with use of propensity scores to account for bias due to non-random
265                                      We used propensity scores to match 148 en bloc with 581 non-en b
266 azards models, adjusted for high-dimensional propensity scores, to generate adjusted hazard ratios (a
267 on-invasive management groups based on their propensity scores, to mitigate immortal time bias.
268                                      We used propensity score trimming and stratification based on an
269                                              Propensity scores used to match 1,519 patients with AF a
270 ility of treatment weighting (IPTW) based on propensity score was used to assess the effect of HIPEC
271 se probability of treatment weighting on the propensity score was used to balance comparison groups o
272                                            A propensity score was used to match patient underlying ch
273                                              Propensity scoring was used to account for nonrandomized
274                                        Using propensity scores, we created comparison groups of 375 n
275                                            A propensity score-weighted analysis was done to control f
276                                            A propensity score-weighted analysis was performed to cont
277      There were 186 patients included in the propensity score-weighted cohort; 45 (24%) received TZP
278 t differ significantly between the groups by propensity score-weighted comparison: 10-year OS 89% (95
279 ithin 180 days of treatment initiation using propensity score-weighted Cox proportional hazards model
280 pertension and recipient graft failure using propensity score-weighted Cox proportional hazards regre
281 pertension and recipient graft failure using propensity score-weighted Cox proportional hazards regre
282 opioid use/misuse and suicidal behaviors and propensity score-weighted logistic regression analysis t
283                                            A propensity score-weighted logistic regression model was
284                                            A propensity score-weighted logistic regression model was
285             In a multicenter, observational, propensity-score-weighted cohort of 249 adults with unco
286 tenosis between 2015 and 2017, using overlap propensity score weighting analysis to control for diffe
287 s (interquartile range, 261-759), on overlap propensity score weighting analysis, there was no differ
288                              We used inverse propensity score weighting for the first comparison to a
289                            The analyses used propensity score weighting to compare the cause-specific
290  using standard multivariate regression with propensity score weighting to reduce covariate confoundi
291                                        After propensity score weighting, ECMO remained associated wit
292                                        After propensity score weighting, hospitals participated in a
293                                        After propensity score weighting, treatment with hydroxycholor
294                                    Following propensity-score weighting, the distribution of baseline
295 tals versus at non-BPCI hospitals matched on propensity score were evaluated using a difference-in-di
296                                              Propensity scores were added to all multivariate OS mode
297                 Inverse probability weighted propensity scores were assigned for surgical management,
298                                              Propensity scores were calculated based on baseline clin
299 4-December 2015) and 13 novel ERP variables, propensity scores were constructed for low (0-5), modera
300                                              Propensity scores were used to match 9,293 women with op

 
Page Top