Weight gain is an important issue in the use of atypical antipsychotics, including olanzapine. A retrospective analysis of patterns of weight gain and possible covariates was performed for 1191 patients diagnosed with schizophrenia or schizoaffective disorder who were treated with olanzapine for up to 52 weeks. Patients were dichotomized into 2 main groups according to the percentage of body weight gained during the first 6 weeks of treatment with olanzapine: (1) patients who gained > or =7% of their body weight (Rapid Weight Gain Group [RWG]), and (2) patients who lost weight, gained no weight, or gained <7% of their body weight (Nonrapid Weight Gain Group [NRWG]). Results demonstrated that approximately 15% of the patient population showed rapid increases in weight (RWG group), whereas 85% of patients gained weight more slowly or not at all (NRWG group). Patients in the RWG group gained an average of 4% of their body weight (approximately 4-7 lb) within the first 2 weeks of treatment with olanzapine. Furthermore, patients in the RWG group were younger, had a lower baseline body mass index, were more likely to report an increase in appetite, and showed a more robust clinical response compared with patients in the NRWG group. Over the course of 52 weeks, patients in the RWG group gained significantly more weight and reached a higher plateau for mean weight increase at 38 weeks compared with the mean increase observed for patients in the NRWG group. By measuring the weight of patients during the first few weeks of olanzapine treatment and by assessing changes in appetite, clinicians may be able to identify those patients at risk for substantial weight gain.

Carcass measurements for weight, longissimus muscle area, 12-13th-rib fat thickness, and marbling score, as well as for live animal measurements of weight at the time of ultrasound, ultrasound longissimus muscle area, ultrasound 12-13th-rib fat thickness, and ultrasound-predicted percentage ether extract were taken on 2,855 Angus steers. The average ages for steers at the time of ultrasound and at slaughter were 391 and 443 d, respectively. Genetic and environmental parameters were estimated for all eight traits in a multivariate animal model. In addition to a random animal effect, the model included a fixed effect for contemporary group and a covariate for measurement age. Heritabilities for carcass weight, carcass longissimus muscle area, carcass fat thickness, carcass marbling score, ultrasound weight, ultrasound longissimus muscle area, ultrasound fat thickness, and ultrasound-predicted percentage ether extract were 0.48, 0.45, 0.35, 0.42, 0.55, 0.29, 0.39, and 0.51, respectively. Genetic correlations between carcass and ultrasound longissimus muscle area, carcass and ultrasound fat thickness, carcass marbling score and ultrasound-predicted percentage ether extract, and carcass and ultrasound weight were 0.69, 0.82, 0.90, and 0.96, respectively. Additional estimates were derived from a six-trait multivariate animal model, which included all traits except those pertaining to weight. This model included a random animal effect, a fixed effect for contemporary group, as well as covariates for both measurement age and weight. Heritabilities for carcass longissimus muscle area, carcass fat thickness, carcass marbling score, ultrasound longissimus muscle area, ultrasound fat thickness, and ultrasound-predicted percentage ether extract were 0.36, 0.39, 0.40, 0.17, 0.38, and 0.49, respectively. Genetic correlations between carcass and ultrasound longissimus muscle area, carcass and ultrasound fat thickness, and carcass marbling and ultrasound-predicted percentage ether extract were 0.58, 0.86, and 0.94, respectively. The high, positive genetic correlations between carcass and the corresponding real-time ultrasound traits indicate that real-time ultrasound imaging is an alternative to carcass data collection in carcass progeny testing programs.

Valid analyses of longitudinal data can be problematic, particularly when subjects dropout prior to completing the trial for reasons related to the outcome. Regulatory agencies often favor the last observation carried forward (LOCF) approach for imputing missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood-based analyses developed under the missing at random framework provide viable alternatives. The within-subject error correlation structure is often the means by which such methods account for the bias from missing data. The objective of this study was to extend previous work that used only one correlation structure by including several common correlation structures in order to assess the effect of the correlation structure in the data, and how it is modeled, on Type I error rates and power from a likelihood-based repeated measures analysis (MMRM), using LOCF for comparison. Data from four realistic clinical trial scenarios were simulated using autoregressive, compound symmetric and unstructured correlation structures. When the correct correlation structure was fit, MMRM provided better control of Type I error and power than LOCF. Although misfitting the correlation structure in MMRM inflated Type I error and altered power, misfitting the structure was typically less deleterious than using LOCF. In fact, simply specifying an unstructured matrix for use in MMRM, regardless of the true correlation structure, yielded superior control of Type I error than LOCF in every scenario. The present and previous investigations have shown that the bias in LOCF is influenced by several factors and interactions between them. Hence, it is difficult to precisely anticipate the direction and magnitude of bias from LOCF in practical situations. However, in scenarios where the overall tendency is for patient improvement, LOCF tends to: 1) overestimate a drug's advantage when dropout is higher in the comparator and underestimate the advantage when dropout is lower in the comparator; 2) overestimate a drug's advantage when the advantage is maximum at intermediate time points and underestimate the advantage when the advantage increases over time; and 3) have a greater likelihood of overestimating a drug's advantage when the advantage is small. In scenarios in which the overall tendency is for patient worsening, the above biases are reversed. In the simulation scenarios considered in this study, which were patterned after acute phase neuropsychiatric clinical trials, the likelihood-based repeated measures approach, implemented with standard software, was more robust to the bias from missing data than LOCF, and choice of correlation structure was not an impediment to its implementation.