ELIZA

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321496 occurrences (No.66 in the rank) during 5 years in the PubMed. [no cache] 500 found

109) Under a natural missing-at-random assumption for these missing failure types, we use the observed data likelihood to estimate its parameters and show that the all-cause hazard and the relative hazards appearing in vertical modelling are indeed key quantities of this likelihood.

--- ABSTRACT ---

PMID:22179822 DOI:10.1177/0962280211432067

2015 Statistical methods in medical research

* Vertical modelling: Analysis of competing risks data with missing causes of failure.

- We propose vertical modelling as a natural approach to the problem of analysis of competing risks data when failure types are missing for some individuals. Under a natural missing-at-random assumption for these missing failure types, we use the observed data likelihood to estimate its parameters and show that the all-cause hazard and the relative hazards appearing in vertical modelling are indeed key quantities of this likelihood. This fact has practical implications in that it suggests vertical modelling as a simple and attractive method of analysis in competing risks with missing causes of failure; all individuals are used in estimating the all-cause hazard and only those with non-missing cause of failure for relative hazards. The relative hazards also appear in a multiple imputation approach to the same problem proposed by Lu and Tsiatis and in the EM algorithm. We compare the vertical modelling approach with the method of Goetghebeur and Ryan for a breast cancer data set, highlighting the different aspects they contribute to the data analysis.

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(1)95 the	(18)4 these	(35)2 axial	(52)2 of
(2)13 different	(19)3 high	(36)2 censoring	(53)2 optimal
(3)12 a	(20)3 investigation	(37)2 clinical	(54)2 physiological
(4)9 general	(21)3 light	(38)2 consideration	(55)2 pulsatile
(5)8 both	(22)3 local	(39)2 controlled	(56)2 scanning
(6)8 which	(23)3 mechanical	(40)2 current	(57)2 serum-free
(7)7 various	(24)2 null	(41)2 evaluation	(58)2 several
(8)6 conditions	(25)2 12	(42)2 ex	(59)2 simulated
(9)6 curve	(26)2 15	(43)2 extreme	(60)2 specific
(10)6 static	(27)2 2	(44)2 four	(61)2 steady-state
(11)5 and	(28)2 CA	(45)2 hypertensive	(62)2 therapy
(12)4 5	(29)2 CT	(46)2 hypoxia	(63)2 this
(13)4 certain	(30)2 H2O2-induced	(47)2 hypoxic	(64)2 time
(14)4 control	(31)2 UV	(48)2 inflammatory	(65)2 treatment
(15)4 either	(32)2 acute	(49)2 laser	(66)2 ultrasound
(16)4 in	(33)2 additive	(50)2 lateral	(67)2 vertical
(17)4 normal	(34)2 adverse	(51)2 mortality	(68)2 water

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--- WordNet output for under --- =>以下の, (治療, 試練, 刑罰)を受けて, 下部の, の下に, ・・の下に Overview of adj under The adj under has 2 senses (first 2 from tagged texts) 1. (2) nether, under -- (located below or beneath something else; "nether garments"; "the under parts of a machine") 2. (1) under -- (lower in rank, power, or authority; "an under secretary") Overview of adv under The adv under has 8 senses (first 1 from tagged texts) 1. (2) under -- (down to defeat, death, or ruin; "their competitors went under") 2. under -- (through a range downward; "children six and under will be admitted free") 3. under -- (into unconsciousness; "this will put the patient under") 4. under -- (in or into a state of subordination or subjugation; "we must keep our disappointment under") 5. under -- (below some quantity or limit; "fifty dollars or under") 6. under -- (below the horizon; "the sun went under") 7. under -- (down below; "get under quickly!") 8. under, below -- (further down; "see under for further discussion") --- WordNet end ---