1 probability of treatment weighting using the
propensity score.
2 ble logistic regression model to calculate a
propensity score.
3 bability of treatment was weighted using the
propensity score.
4 matched into 208 pairs through the use of a
propensity score.
5 talization were balanced on 44 covariates of
propensity score.
6 ed 2:1 untreated to treated based on age and
propensity score.
7 erse probability treatment weighting using a
propensity score.
8 vaccine during pregnancy were matched 1:4 on
propensity scores.
9 tin nonusers (<28 cDDD) were matched through
propensity scores.
10 tching identified 2126 patients with similar
propensity scores.
11 n bias for PEG treatment through generalized
propensity scores.
12 rs of other glucose-lowering drugs by use of
propensity scores.
13 probability of treatment weighting based on
propensity scores.
14 2015 were evaluated and matched 1:1 based on
propensity scoring.
15 of 56,448 HCV-infected patients and 169,344
propensity score (
1:3)-matched non-HCV patients, we exam
16 Using
propensity scores,
461 transplants in the improved-donor
17 m 0.16% (IV: 95% CI: 0.03%, 0.28%) to 0.25% (
propensity score:
95% CI: 0.17%, 0.33%).
18 dence interval [CI]: 1.81%, 2.27%) to 2.29% (
propensity score:
95% CI: 2.14%, 2.44%) and mortality re
19 After adjustment by a
propensity score,
ABO was an independent predictor of pe
20 The RPD and OPD cohorts were matched by
propensity scores accounting for factors significantly a
21 We conducted a
propensity score adjusted cohort study using nationally
22 A
propensity score-
adjusted analysis, which included patie
23 ality were compared between hospitals, using
propensity score-
adjusted difference-in-differences anal
24 Cox regression was used to determine
propensity score-
adjusted hazard ratios (HRs) with 95% c
25 atifying based on clinical nodal status, the
propensity score-
adjusted OS was significantly better fo
26 ciated with a reduced 30-day mortality after
propensity score adjustment (hazard ratio, 0.94; 95% CI,
27 een OMH and LMH patients was performed after
propensity score adjustment on factors influencing the c
28 Outcomes were compared by use of
propensity score adjustment to account for baseline diff
29 e data, discrete-time survival analysis with
propensity score adjustment, and sensitivity analysis.
30 After
propensity score adjustment, rates of AKI, dialysis, and
31 After
propensity score adjustment, there was no difference in
32 ted models that was largely attenuated after
propensity score adjustment.
33 In sensitivity analyses using
propensity scores adjustment in addition to other availa
34 e 3 or more severe, following adjustment for
propensity score,
age, and calendar year, and accounting
35 Propensity score analyses were conducted with each iAE p
36 mined using adjusted logistic regression and
propensity score analyses, whereby the associations betw
37 We performed
propensity-score analyses by weighting and matching and
38 Comparisons were performed using a matched
propensity score analysis and examined for all cardiac a
39 These associations were attenuated in the
propensity score analysis but remained statistically sig
40 Based on
propensity score analysis incorporating 10 clinically re
41 The
propensity score analysis indicates a significant superi
42 fore a gestational age of 32 weeks, we did a
propensity score analysis of the effect of feeding in th
43 2011, and Nov 6, 2013, using a prespecified
propensity score analysis to account for between-trial d
44 We used a
propensity score analysis to adjust for potential confou
45 ompare the different cannabis use groups and
propensity score analysis to validate the results.
46 A stratified
propensity score analysis was performed to match treated
47 Propensity score analysis was used to match patients for
48 ysis; OR=0.95, 95% CI 0.45-1.97, p=0.884 for
propensity score analysis).
49 ese findings, which were also confirmed in a
propensity score analysis, allowed the development of a
50 Propensity score analysis, including degree of hypoxia i
51 In a
propensity score analysis, the rate of cataracts was hig
52 modern systemic chemotherapy and remained in
propensity score analysis.
53 Results were confirmed by
propensity score analysis.
54 -adjusted Cox proportional hazard models and
propensity score analysis.
55 In the
propensity-score analysis we included 963 patients treat
56 ator (hazard ratio for mortality adjusted on
propensity score and all mortality predictors: 0.76; 95%
57 from partially overlapping subgroups, using
propensity score and greedy matching algorithms.
58 Survival-time inverse probability weighting
propensity scores and instrumental variable analyses wer
59 Propensity-score and cause-of-death analyses were used t
60 Using a matched
propensity score approach, we matched persons who had co
61 , and long-term survival were examined after
propensity score balancing.
62 ent dialysis and death were determined after
propensity score-
based 1:3 matching of patients with sol
63 A
propensity score-
based approach was used to assess the i
64 In this paper, we propose 2
propensity score-
based approaches to vertically distribu
65 rt study of SOTRs from 1995 to 2015 and used
propensity score-
based inverse probability of treatment
66 ing resuscitation) based on a time-dependent
propensity score calculated from multiple patient, event
67 ing resuscitation) based on a time-dependent
propensity score calculated from multiple patient, event
68 1994 to 2013, 3741 had complete data for the
propensity score calculation.
69 as evaluated using Cox regression model with
propensity score calibrated for each oral exposure.
70 mpared with fitting separate domain-specific
propensity scores (
called the "parallel approach").
71 For a given level of risk assessed by
propensity score,
colonization by C. neonatale and/or S.
72 weighting based on derived high-dimensional
propensity scores (
computerized algorithm used to select
73 Crude and
propensity score-
corrected analyses were performed using
74 ations were observed in patients with a high
propensity score decile (HR, 0.52; 95% CI, 0.50-0.54), p
75 ditional subgroup analyses were performed in
propensity score deciles and within strata of age, gende
76 Stratified analyses adjusted for
propensity score demonstrated an improvement in overall
77 The hazard ratio adjusted by
propensity score demonstrated longer OS with HAI: 0.67 (
78 ghts based on baseline body mass index and a
propensity score estimated from baseline variables.
79 We generated a
propensity score for high-dose colistin and conducted pr
80 We used a
propensity score for receiving combination therapy and a
81 Cox regression including a
propensity score for receiving OADs was performed to ana
82 Propensity score for SGLT-2i initiation was used to matc
83 onths) and matched 1:1 by inclusion date and
propensity score for statin treatment.
84 ring the first week of life, we calculated a
propensity score for the risk of NEC (Bell's stage 2 or
85 Propensity scores for discharge heart rate <70 beats/min
86 Propensity scores for induction treatment were calculate
87 ricular ejection fraction >/=40%, we derived
propensity scores for nitrate use using 52 baseline vari
88 using generalised estimating equations with
propensity score (
generated by boosted regression) adjus
89 e prespecified time windows were matched for
propensity score in a 1:4 ratio with women who did not h
90 The
propensity score included important demographic, physica
91 andard oxygen) were matched according to the
propensity score,
including 91 of 180 (51%) who received
92 Although the
propensity score incorporated several biologically relev
93 Similar results were obtained after
propensity score inverse probability of treatment weight
94 Propensity score match analysis in a 1:1 case-match was
95 We created a
propensity score matched (PSM) cohort and a low cardiova
96 From these sources, we
propensity score matched 430 women with heart failure wh
97 We performed a
propensity score matched cohort study of adult kidney tr
98 alcium channel blocker users and nonusers, a
propensity score matched cohort was constructed to accou
99 A one-to-one
propensity score matched dataset was created to compare
100 We
propensity score matched patients on clopidogrel before
101 Using only
propensity score matched transplants, the effect of indu
102 diagnosis, 75 (7) years; 52.0% female) were
propensity score matched, including 6214 patients in the
103 n patients treated with RC or TMT by using a
propensity score matched-cohort analysis.
104 ability progression among 'DMT stoppers' and
propensity-score matched 'DMT stayers' in the MSBase Reg
105 HT and ground transport subjects were
propensity-score matched based on prehospital physiology
106 r open lower extremity revascularization for
propensity-score matched cohorts of Medicare beneficiari
107 obustness of this definition, outcomes among
propensity-score matched early and delayed surgical pati
108 Compared with 13731
propensity-score matched patients who received surgery e
109 Conclusions and Relevance: In
propensity score-
matched analyses among patients with AC
110 Propensity score-
matched analyses redemonstrated improve
111 Stratified and
propensity score-
matched analyses to account for confoun
112 In
propensity score-
matched analyses, receiving noninvasive
113 k test, Cox proportional hazards models, and
propensity score-
matched analyses.
114 univariate and multivariate Cox models, and
propensity score-
matched analyses.
115 Propensity score-
matched analysis demonstrates reduced m
116 Propensity score-
matched analysis of 1620 SL versus 1584
117 To our knowledge, this is the first
propensity score-
matched analysis of robotic vs open pan
118 Overall survival (OS) was examined using a
propensity score-
matched analysis with a shared frailty
119 Retrospective cohort with
propensity score-
matched analysis.
120 ADT on risk of Alzheimer's disease using 1:5
propensity score-
matched and traditional multivariable-a
121 In the 145 738
propensity score-
matched cases, suicide (OR 1.326, 95% C
122 ; 95% confidence interval, 0.84-1.58) or the
propensity score-
matched cohort (hazard ratio, 0.97; 95%
123 In the
propensity score-
matched cohort (n = 2270), survival was
124 The
propensity score-
matched cohort comprised 152 RPDs and 1
125 ates between 2007 and 2011, we created a 1:1
propensity score-
matched cohort of 4000 patients and exa
126 Compared with a
propensity score-
matched cohort of patients without ASAR
127 Propensity score-
matched cohort study analyzing data pro
128 This nationwide, population-based,
propensity score-
matched cohort study used data from Tai
129 In the
propensity score-
matched cohort, no significant associat
130 Examination of risk of renal outcomes in 1:1
propensity score-
matched cohorts of patients taking H2 b
131 Analysis of
propensity score-
matched cohorts provided similar result
132 ortional hazards regression was performed in
propensity score-
matched cohorts to investigate the outc
133 ization, and sternal wound infection between
propensity score-
matched cohorts who underwent primary,
134 In
propensity score-
matched cohorts, partial nephrectomy as
135 0.11 to 0.19, versus radical nephrectomy) in
propensity score-
matched cohorts.
136 In a
propensity score-
matched comparison, there was no differ
137 For each case, we identified a
propensity score-
matched control never initiated on trea
138 ecognized patient-centered medical homes and
propensity score-
matched control practices in the same P
139 ecognized patient-centered medical homes and
propensity score-
matched control practices in the same P
140 Compared with
propensity score-
matched controls and adjusted for confo
141 effect of ADT on the risk of dementia using
propensity score-
matched Cox proportional hazards regres
142 Among
propensity score-
matched groups, the median overall surv
143 Survival was compared using
propensity score-
matched groups: patients in the bottom
144 The mean +/- SD age of the
propensity score-
matched HHD and KTx patients at baselin
145 = 0.51, 95% CI 0.48-0.54, P < 0.001) in the
propensity score-
matched model.
146 Propensity score-
matched new episodes of prescribed ther
147 A meta-analysis of all the
propensity score-
matched observational studies comparing
148 gnificant association with 30-day mortality,
propensity score-
matched odds ratio (OR) 1.39 (0.76-2.55
149 In 340
propensity score-
matched pairs, IVUS was also associated
150 Propensity score-
matched pairs, subgroups, and sensitivi
151 rction identified from hospital claims among
propensity score-
matched patients starting treatment wit
152 ients starting dabigatran therapy and 25 289
propensity score-
matched patients starting warfarin ther
153 Among
propensity score-
matched patients, we found no differenc
154 Similar results were obtained in a
propensity score-
matched population.
155 ts were in-hospital/30-day mortality for the
propensity score-
matched populations and long-term morta
156 Long-term mortality in the
propensity score-
matched populations was the primary end
157 subgroup analysis compared outcomes between
propensity score-
matched recipients of a right internal
158 ary 1, 2012, through December 31, 2015, used
propensity score-
matched regression analysis to compare
159 use mortality was analyzed in a total of 314
propensity score-
matched S. aureus bloodstream infection
160 Eight
propensity score-
matched studies reporting on 10 287 mat
161 METHODS AND This multicenter,
propensity score-
matched study compared hemodynamics and
162 oups were compared using: paired t test on a
propensity score-
matched subset and multivariable analys
163 3 persons on PrOD, 5497 on LDV/SOF, and 6970
propensity score-
matched untreated persons.
164 Propensity score-
matched, population-based cohort study.
165 Propensity-score-
matched cohorts were constructed to com
166 tients with physician-diagnosed asthma and a
propensity-score-
matched comparison sample from the gene
167 Results In a
propensity-score-
matched sample of 1,970 individuals, fa
168 After
propensity score matching (200 patients/group), MACE rem
169 After
propensity score matching (39 high-flow nasal cannula pa
170 Propensity score matching (based on sex, age, comorbidit
171 ivity was associated with lower PPCS risk on
propensity score matching (n = 1108 [28.7% for early phy
172 Propensity score matching (PSM) analysis was used to ide
173 Moreover,
propensity score matching (PSM) was employed in balancin
174 Propensity score matching (PSM) was utilized.
175 After
propensity score matching 225 patients were comparable i
176 A
propensity score matching analysis extracted 63 patients
177 Propensity score matching analysis identified 79 matched
178 After
propensity score matching analysis, all-cause mortality
179 After
propensity score matching and adjusting for potential co
180 analyzed nonlobar and lobar ICH cases using
propensity score matching and Cox regression models.
181 To compare bivalirudin with UFH,
propensity score matching and instrumental variable (IV)
182 We used
propensity score matching and inverse probability of tre
183 Propensity score matching and inverse probability of tre
184 After
propensity score matching and inverse probability-of-tre
185 After
propensity score matching and risk-adjustment, there was
186 Propensity score matching and stratified Cox regression
187 .76), and similar results were achieved with
propensity score matching and weighting.
188 To account for risk imbalances, 1:1
propensity score matching based on 16 baseline clinical
189 Propensity score matching confirmed these findings, as d
190 One-to-one
propensity score matching for receipt of adjuvant therap
191 351 everolimus-eluting stent and 3265 CABG),
propensity score matching identified 2126 patients with
192 Propensity score matching of 521 patient pairs revealed
193 atistics, as well as internal validation and
propensity score matching of factors known to affect rec
194 is was performed after the implementation of
propensity score matching on the 2 main cohorts (laparos
195 One-to-one
propensity score matching provided 215 pairs, whose base
196 The 1:1
propensity score matching resulted in 92 matched pairs.
197 Propensity score matching techniques were used to match
198 After
propensity score matching the percentage of patients wit
199 By using
propensity score matching to account for differences in
200 After 1:1
propensity score matching to account for potential treat
201 a from the 2013-14 influenza season and used
propensity score matching to account for the probability
202 Therefore, we used
propensity score matching to balance baseline covariates
203 We used
propensity score matching to compare similar women who u
204 We used multiple regression and
propensity score matching to estimate the strength of in
205 We used
propensity score matching to select 145 738 cases for an
206 Propensity score matching using 34 preoperative characte
207 Propensity score matching was applied to create comparab
208 al hazard regression modeling and stratified
propensity score matching was assessed.
209 Propensity score matching was conducted by using a neare
210 Propensity score matching was performed 1:2 and among th
211 Propensity score matching was performed between SLK-HCC
212 Propensity score matching was performed to adjust for po
213 Propensity score matching was performed using six differ
214 logistic regression, linear regression, and
propensity score matching was performed.
215 Propensity score matching was used at baseline (12 month
216 For analysis,
propensity score matching was used to adjust the confoun
217 Propensity score matching was used to assemble a cohort
218 Propensity score matching was used to balance cohort cha
219 Propensity score matching was used to correct for differ
220 Propensity score matching was used to estimate the chang
221 Propensity score matching was used to minimize bias from
222 Propensity score matching was used to select 1802 patien
223 Multivariate analyses and
propensity score matching were used to adjust for confou
224 Logistic regression and
propensity score matching were used to adjust for unbala
225 Multivariable hierarchical analyses and
propensity score matching were used to compare outcomes
226 not experience cardiopulmonary arrest using
propensity score matching with a 1:1 ratio.
227 Propensity score matching yielded 6,088 statin user pair
228 Propensity score matching yielded a cohort of 247 patien
229 With
propensity score matching, 64 patients undergoing primar
230 (n=1709) and replacement (n=213) overall, by
propensity score matching, and by inverse probability-of
231 After
propensity score matching, and stratification by severit
232 After
propensity score matching, C-->PORT remained associated
233 After
propensity score matching, the median vascular access-to
234 After
propensity score matching, the overall incidence of PPCs
235 After
propensity score matching, we included 2,805 children wi
236 reValve were compared in a 1:2 fashion after
propensity score matching.
237 roportional hazard regression analysis after
propensity score matching.
238 and ten slow-privatised towns selected using
propensity score matching.
239 ere analyzed in overall population and after
propensity score matching.
240 compared using both logistic regression and
propensity score matching.
241 Similar results were also found after
propensity score matching.
242 ank tests, multivariable Cox regression, and
propensity score matching.
243 eligible patients, 813 were discarded after
propensity score matching; 442 and 442 patients who unde
244 A total of 791 patients were studied after
propensity score matching; the mean (SD) age of patients
245 ence interval, 1.20-2.34; P=0.002) and after
propensity-score matching (propensity-adjusted hazard ra
246 Propensity-score matching tested the effect of anti-tumo
247 mental and socio-economic datasets, and used
propensity-score matching to assess: (i) how deforestati
248 We used
propensity-score matching to compare family-reported out
249 A
propensity score-
matching analysis based on 28 pretreatm
250 Propensity score-
matching identified a cohort of 822 pat
251 ontrol individuals in an FFS program using a
propensity score method.
252 Propensity score methods have theoretical advantages ove
253 applications of causal effect estimation use
propensity score methods or G-computation, targeted maxi
254 Propensity score methods were used to account for observ
255 The use of
propensity score methods within the principal stratifica
256 dy-the first population-based analysis using
propensity score methods-provides evidence of a favorabl
257 ompared using multivariable adjustment and a
propensity score model.
258 A
propensity-score model for receiving appropriate empiric
259 administration data of the two groups using
propensity scores obtained via the inverse probability o
260 acteristics, and treatment) in sequentially,
propensity-scored,
optimally matched patients by using m
261 be the evolution of the use and reporting of
propensity score (
PS) analysis in observational studies
262 Propensity score (
PS) for insulin use was calculated usi
263 Previous studies have compared calipers for
propensity score (
PS) matching, but none have considered
264 Propensity score (
PS) methods can be used to achieve com
265 A
propensity score (
PS) model's ability to control confoun
266 rs with nonusers by year of BC diagnosis and
propensity score (
PS) with the caliper widths at 0.25 st
267 This investigation was a
propensity score (
PS)-adjusted and PS-matched longitudin
268 orted with a TCS device were compared with a
propensity score (
PS)-matched cohort of children support
269 Propensity scores (
PS) are an increasingly popular metho
270 egies (multivariable logistic regression and
propensity score [
PS]-adjusted and PS-matched analyses)
271 ween induction treatment groups based on the
propensity score,
reducing potential biases.
272 Following stratification by quintile of
propensity score,
site, and year, VE was estimated to be
273 Using a covariate-balancing
propensity score strategy, we investigated the different
274 0 mL/min/1.73 m(2)) and separately underwent
propensity score stratification and matching.
275 ment weighting based on the high-dimensional
propensity score,
the association was not significant (H
276 an squared error) for sequentially passing a
propensity score through each data domain (called the "s
277 All multivariable analyses accommodated a
propensity score to balance patient characteristics betw
278 We used a
propensity score to minimize confounding by indication o
279 erse probability of treatment weighting with
propensity scores to adjust for the imbalance in covaria
280 ical disorders and diabetes, were matched by
propensity scores to another non-HCV cohort.
281 After using
propensity scores to match on consistent campaign ITN us
282 A sensitivity analysis used
propensity scores to match patients on ITN use propensit
283 oma in situ status, and hydronephrosis) with
propensity scores to patients who underwent RC.
284 A
propensity score was calculated and then entered into a
285 A
propensity score was calculated to account for selection
286 Propensity score was used to adjust for nonrandom assign
287 ic regression analysis, using the calculated
propensity scores,
was performed.
288 A
propensity-score weighted method was used to balance cov
289 ceiving non-biologic systemic therapies in a
propensity score-
weighted Cox proportional hazards model
290 This is a retrospective study utilizing a
propensity score-
weighted intervention (n = 16,930) and
291 A
propensity score-
weighted logistic regression model was
292 PV prevalence were significant using inverse
propensity score-
weighted logistic regression.
293 We used
propensity score weighting to balance confounders betwee
294 formed a Cox proportional hazards model with
propensity score weighting to evaluate breast cancer mor
295 Propensity scores were calculated to determine the facto
296 e-dependent endocrine drug use variables and
propensity scores were conducted.
297 High-dimensional
propensity scores were used to address selection bias fo
298 Propensity scores were used to adjust for the likelihood
299 Multivariate logistic regressions and
propensity scores were used to identify predictors of CP
300 by curative-intent resection were matched by
propensity score with patients whose tumors were resecte