According to World Cancer Research Fund International/American Institute for Cancer Research, it is ‘probable’ that dairy products decrease the risk of colorectal cancer (CRC). However, meta-analyses restricted to women have not shown associations between milk intake and risk of CRC. The aim of this study was to examine the association between milk intake and risk of CRC, colon cancer and rectal cancer among women. Data from 81 675 participants in the Norwegian Women and Cancer Cohort Study were included, and multivariable Cox proportional hazard regression models were used to investigate milk intake using two different analytical approaches: one that included repeated measurements and one that included baseline measurements only (872 and 1084 CRC cases, respectively). A weak inverse association between milk intake and risk of colon cancer may be indicated both in repeated measurements analyses and in baseline data analyses. Hazard ratios (HR) for colon cancer of 0·80 (95 % CI 0·62, 1·03, Ptrend 0·07) and 0·81 (95 % CI 0·64, 1·01, Ptrend 0·03) and HR for rectal cancer of 0·97 (95 % CI 0·67, 1·42, Ptrend 0·92) and 0·71 (95 % CI 0·50, 1·01, Ptrend 0·03) were found when comparing the high with the no/seldom milk intake group in energy-adjusted multivariable models. Our study indicates that there may be a weak inverse association between milk intake and risk of colon cancer among women. The two analytical approaches yielded different results for rectal cancer and hence CRC. Our study indicates that the use of single or repeated measurements in analyses may influence the results.

Random regression (RR) analysis has been recommended to estimate the genetic parameters of longitudinal data. The objective of this study was to evaluate the growth of turkeys using RR models. Data were collected from 957 turkeys and included 15 478 individual body weight recorded during the first week of life and between weeks 2 and 32 by 2-week intervals. To take into account the repeated measurements of weight for each animal, a specific overall growth curve was modelled using a cubic smoothing spline. Animal deviation to this curve was also modelled using an RR function. All data were analysed with the ASReml package. The results showed an increase in heritability estimates over the trajectory and peaked at 0.60 around 20 to 32 weeks of age. Genetic correlations showed that turkeys could be selected at earlier time points, at 12 weeks of age, in order to increase the growth rate. In general, genetic correlation estimates were higher among adjacent ages, decreasing markedly with the increase of distance between ages. Negative genetic correlations were observed between ages.

Specific procedures are described to measure the body dimensions of large African predators; these include measurements of body and tail length, head, canines, mane, limbs, paws and testes. Data on body mass and dimensions were collected from an immobilized sub-adult male African lion Panthera leo. He was 40 months old and weighed 190 kg. After being immobilized, data on body mass and dimensions were collected by a team comprising a scribe, two operators and assistants. The procedures of measuring the 43 variables on body dimensions were then repeated three times in quick succession, each session lasting about 10 min. The four sessions were handled as if a different individual was measured each time. Except for reports on body mass of lions, there is a paucity of comparable data in the literature for most of the variables presented in this paper. The small dispersion measures of the 43 variables suggest that the specific procedures to measure the body dimensions of a large African predator such as the lion have a high degree of repeatability and can be applied with confidence. The procedure has been used to obtain data on body dimensions of a large number of lions as well as dead specimens of black-backed jackal Canis mesomelas, caracal Caracal caracal and Cape fox Vulpes chama. The data form part of a database on body dimensions of large African predators created by ALPRU. The objective is to develop non-invasive techniques to determine whether wild predator populations might have been subjected to abnormal growth and development as a result of negative impacts on their habitat and thus food base.

In 1987, Jennings enumerated data analysis procedures that authors must follow for analyzing effects in repeated measures designs when submitting papers to Psychophysiology. These prescriptions were intended to counteract the effects of nonspherical data, a condition know to produce biased tests of significance. Since this editorial policy was established, additional refinements to the analysis of these designs have appeared in print in a number of sources that are not likely to be routinely read by psychophysiological researchers. Accordingly, this paper includes additional procedures not previously enumerated in the editorial policy that can be used to analyze repeated measurements. Furthermore, I indicate how numerical solutions can easily be obtained.

In 1987, Jennings enumerated data analysis procedures that authors must follow for analyzing effects in repeated measures designs when submitting papers to Psychophysiology. These prescriptions were intended to counteract the effects of nonspherical data, a condition know to produce biased tests of significance. Since this editorial policy was established, additional refinements to the analysis of these designs have appeared in print in a number of sources that are not likely to be routinely read by psychophysiological researchers. Accordingly, this paper includes additional procedures not previously enumerated in the editorial policy that can be used to analyze repeated measurements. Furthermore, I indicate how numerical solutions can easily be obtained.