Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in the univariate regression, and vice versa. These facts have not been well appreciated in biomedical research. Misuse of logistic regression is very prevalent in medical publications. In this paper, we study the inconsistency between the univariate and multiple logistic regressions and give advice in the model section in multiple logistic regression analysis.

Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health research.

Introduction: During Robotic-Assisted Radical Prostatectomy (RARP), the prostate may be approached extraperiteoneally (eRARP) or transperitoneally (tRARP). The former avoids the abdominal cavity which might be of benefit in patients who have had prior abdominal or pelvic surgery (PAPS). Our objective was to compare the outcomes of patients with PAPS undergoing either technique. Methods: A retrospective review of patients treated with RARP from July 1, 2003-Dec. 31, 2014 with a minimum follow-up of 3 months was undertaken. Of 2,927 patients, 620 were identified as having undergone RARP (without concomitant unrelated procedures) and PAPS (excluding patients with prior inguinal hernia repair with mesh (IHRm) or unclear surgical histories) for prostate adenocarcinoma without prior pelvic radiotherapy. Of these, 340 patients underwent eRARP and 280 patients underwent tRARP. Results: Patients in the eRARP group were younger (61.04 yrs vs. 62.32, p=0.02), had higher body mass index (29.65 vs. 28.98, p=0.09), lower American Society of Anesthesiologists scores (p=0.03), lower D’Amico risk classification disease (p2 PAPS. On univariate analysis, the eRARP group had lower operative time (188.96 min vs. 197.92 min, p=0.003), extensive lysis of adhesions (0.9% vs 14.3%, p<0.0001), length of hospital stay (1.13 days ± 0.45 vs. 1.33 day ± 1.08, p=0.003) and higher estimated blood loss (210.74 cc vs. 190.79 cc, p=0.06). The eRARP group had a lower rate of gastrointestinal complications (0% vs. 3.21%, p=0.0007), a trend towards lower early post-operative complications (8.53% vs. 12.86%, p=0.08), and lower overall complications (9.41% vs. 15%, p=0.03). In regression analysis with model selection, only LOS was lower in the eRARP group (p=0.02). Conclusions: Both methods are safe in patients with prior abdominal surgeries. A lower incidence of gastrointestinal complications and shorter length of stay were noted in the extraperitoneal cohort.

Purpose: The primary aim was to evaluate dentoalveolar expansion with Invisalign clear aligners comparing linear measurements in ClinCheck vs. cone beam computed tomography (CBCT). This would enable an assessment of to what extent expansion gained from Invisalign clear aligners was due to buccal tipping and/or bodily translation of the posterior teeth. The study also evaluated the predictive value of Invisalign ClinCheck^® (Align Technology, San Jose, CA, USA) to final outcomes. Methods: The orthodontic records of thirty-two (32) subjects comprised the sample to conduct this study. Linear values of the upper arch width were measured for premolars and molars at two different points (occlusal and gingival) utilized for ClinCheck^® measurements and three different points for CBCT measurements before (T₀ and after treatment (T₁). Paired T-tests at a significance level of 0.05 were used for analyses. Results: Expansion was found to be possible with Invisalign clear aligners. However, more expansion was measured at the cusp tips compared to gingival margins (p < 0.0001), indicating more tipping was occurring than bodily translation. ClinCheck^® also showed a significant overestimation of the amount of expansion capable, with nearly 70% expression in the first premolar area, and the expression decreased as one moved posteriorly with only 35% expressed at the first molar area (p < 0.0001). Conclusions: Dentoalveolar expansion with Invisalign is achieved through buccal tipping of posterior teeth and bodily translation; and there is a significant overestimation of the amount of expansion achieved between ClinCheck^® and clinical results.