Statistical details of the SRGC time series dataset.

The dataset contains data from the project "Efficacy of the Metacognitive Training on symptom severity, neurocognition and social cognition in a sample of patients diagnosed with schizophrenia: a single blind, randomised, controlled trial". The database indicates the missing data. Data imputation was completed using the Predictive Mean Matching (PMM) method. Data were collected in two different psychiatric wards. Participants were randomized into intervention and control groups. Participants in the intervention group took part in a standard Metacognitive Training (MCT) along with psychiatric treatment as usual (TAU,), while patients in the control group only received TAU. Data were recorded at three time points: before MCT (T0), after MCT (T1) and after a six-month follow-up period (T2). The assessment time points are indicated in the column headings. IQ values shown were used to screen for inclusion criteria (IQ > 70). The database contains data from the following measures in that order: Positive and Negative Syndrome Scale for Schizophrenia (PANSS) , Theory of Mind Picture Stories Task (ToM PST), Baron-Cohen Reading the Mind in the Eyes Test (RMET), Wisconsin Card Sorting Test (WCST), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).
Code dictionary of the dataset: Intervention_Control: 1= randomised to intervention group; 2= randomised to control group Gender: 1= male; 2= female Marital status: 1= single; 2= married; 3= divorced; 4= widowed; 5= in a relationship Level of education: 1= less than primary education; 2= primary education; 3= vocational education; 4= high school diploma; 5= higher education Occupational status: 1= supported job for psychiatric patients; 2= normal employment part-time; 3= normal employment full-time; 4= unemployed Antipsychotic treatment: 1= yes, 2= no

The controlled imputation method refers to a class of pattern mixture models (PMM) that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These PMMs assume that subjects in the experimental arm after dropout have similar response profiles to the control subjects, or have worse outcomes than otherwise similar subjects who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose two approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation (MDA) technique for the sequential logistic regression, and a parameter expanded MDA scheme for the multivariate probit model.We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial, and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods.

Unique tryptic peptides of host hemoglobins used for blood meal source identification using PMM-based MALDI-TOF mass spectrometry.

For the assessment of sarcopenia or other geriatric frailty syndromes, psoas major area may be one of the primary indicators. Aim to develop and cross-validate the psoas cross-sectional area estimation equation of L3-L4 of the elderly over 60 years old by bioelectrical impedance analysis (BIA). Ninety-two older adults with normal mobility were enrolled (47 females, 45 males), and were randomly divided into a modeling group (MG, n = 62) and validation group (VG, n = 30). Computed tomography (CT) was used to measure the psoas major area at the’ L3-L4 lumbar vertebrae height as a predictor. Estimated variables were height (h), whole body impedance (Zwhole), whole body impedance index (h2/Zwhole, WBI), age, gender (female = 0, male = 1), and body weight (weight) by standing BIA. Relevant variables were estimated using stepwise regression analysis. Model performance was confirmed by cross-validation. BIA estimation equation for PMM obtained from the MG was: (PMMBIA = 0.183 h2/Z– 0.223 age + 4.443 gender + 5.727, r2 = 0.702, n = 62, SEE = 2.432 cm2, p < 0.001). The correlation coefficient r obtained by incorporating the VG data into the PMM equation was 0.846, and the LOA ranged from -4.55 to 4.75 cm2. PMMBIA and PMMCT both correlate highly with MG or VG with small LOA. The fast and convenient standing BIA for measuring PMM may be a promising method that is worth developing.