Covariance models for nested repeated measures data: analysis of ovarian steroid secretion data

2001 ◽  
Vol 21 (1) ◽  
pp. 143-164 ◽  
Author(s):  
Taesung Park ◽  
Young Jack Lee
Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.


1995 ◽  
Vol 145 (3) ◽  
pp. 479-490 ◽  
Author(s):  
B K Campbell ◽  
B M Gordon ◽  
C G Tsonis ◽  
R J Scaramuzzi

Abstract Ewes with ovarian autotransplants received either inhibin antiserum (10 ml i.v. raised in sheep against recombinant 32 kDa human inhibin; n=6) or sheep serum (10 ml i.v.; n=5) on day 3 of the luteal phase with additional daily injections (1 ml i.v.) from 48 h after the initial bolus until day 13. Jugular and ovarian venous blood samples were taken 4-hourly over days 2–13 of the luteal phase. Blood samples were also taken at more frequent intervals (every 10–15 min for 2–3 h) to examine pulsatile secretory responses from the ovary to endogenous and gonadotrophin-releasing hormone-induced (150 ng i.m.) LH pulses on days 4, 6, 8, 10 and 12 of the luteal phase. Plasma FSH levels, ovarian steroid secretion and ovarian follicular development were measured. The ovarian follicle population was estimated daily by real time ultrasound scanning. Immunisation against inhibin resulted in a 3- to 4-fold increase (P<0·001) in plasma FSH levels within 8 h with levels remaining elevated over controls for 6–7 days. Within 24 h of immunisation there was an increase in the number of small ovarian follicles (P<0·05) and by 3 days after treatment immunised ewes had 4–6 large ovarian follicles/ewe with this increase in the total number of large follicles being maintained for the rest of the experimental period (P<0·05). Mean ovarian oestradiol secretion during intensive bleeds was not different from controls 24 h after immunisation, but by 3 days after immunisation it was elevated 4- to 5-fold (P<0·001) over controls with this increase being maintained throughout the experiment. Similar responses to immunisation against inhibin in androstenedione secretion were observed although mean androstenedione secretion was not elevated until 7 days after treatment. In vitro antibody titres in immunised ewes remained elevated but declined steadily (P<0·001) over the experimental period. We conclude that the initial stimulation of follicle development and ovarian steroid secretion following passive immunisation against inhibin can be attributed to increased blood FSH. However, the fact that with time FSH declined but increased follicle development was sustained, despite maintenance of high circulating antibody titres, suggests that on a longer term basis inhibin immunisation may stimulate ovarian function by interfering with the modulation of follicle development by inhibin at an ovarian level. Journal of Endocrinology (1995) 145, 479–490


Test ◽  
2017 ◽  
Vol 27 (2) ◽  
pp. 360-378 ◽  
Author(s):  
Ivan Žežula ◽  
Daniel Klein ◽  
Anuradha Roy

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