Polygenic liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness.

OBJECTIVE: To test whether polygenic risk score for schizophrenia (PRS-S) interacts with childhood adversity and daily-life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress-sensitivity measures. METHODS: The data were retrieved from a general population twin cohort including 593 adolescents and young adults. Childhood adversity was assessed using the Childhood Trauma Questionnaire. Daily-life stressors and momentary mental state domains were measured using ecological momentary assessment. PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta-analysis. The analyses were conducted using multilevel mixed-effects tobit regression models. RESULTS: Both childhood adversity and daily-life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS-S was only associated with increased positive affect. No gene-environment correlation was detected. There is novel evidence for interaction effects between PRS-S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, P = 0.013), positive affect (b = -0.05, P = 0.043), and subtle psychosis expression (b = 0.11, P = 0.007)] and stress-sensitivity measures. CONCLUSION: Exposure to childhood adversities, particularly in individuals with high PRS-S, is pleiotropically associated with emotion dysregulation and psychosis proneness.

[Text message reminders in the hospital-psychiatry outpatient setting - a quasi-experimental study on cost aspects]. / Sms-herinneringen op de polikliniek ziekenhuispsychiatrie ­ een quasi-experimenteel onderzoek op kostenaspecten.

BACKGROUND: During their specialty training program residents are stimulated to think and work in a goal-oriented way. Patient 'no shows' are quite common in the mental healthcare system, consequently causing the ineffective use of healthcare services.
AIM: To reduce the amount of 'no shows' in an outpatient clinic for hospital psychiatry by sending reminders via text messaging.
METHOD: A quasi-experimental study was conducted at an outpatient clinic for hospital-psychiatry in 2016, in which 101 patients were included. Eventually, 50 patients received a text message to remind them of their appointment, while 46 did not. We used a χ2 test to evaluate group differences. The effect size was expressed in the 'number needed to cash' (nnc), similar to the number needed to treat (nnt). Routinely available hospital-data was used to estimate lost revenue per year.
RESULTS: A significant group difference was found in the number of outpatient clinic visits in favour of sending a text message reminder (74% vs. 92%, p = 0.018). This corresponded to a nnc of 5.53, i.e. 6 text messages need to be sent in order to accomplish one extra patient showing up for their intake. Based on hospital-data from 2016 the estimated lost revenue was € 53.017,38 / year at our outpatient clinic.
CONCLUSION: Sending reminders via text messaging is effective in reducing the number of 'no shows' at an outpatient clinic for hospital psychiatry.

Simultaneous confidence bounds for relative risks in multiple comparisons to control.

We discuss the construction of asymptotic simultaneous upper confidence limits that jointly bound relative risks formed by comparing several treatments to a control. Motivated by a vaccine study, we investigate the performance of several methods under such settings. Inverting the minimum of score statistics, together with estimating the correlation matrix of these statistics under the null gives simultaneous coverage rates closest to the nominal level. In typical settings of vaccine studies, this method proves to be the most powerful of the ones considered, but computationally simpler alternatives are also worth exploring when the number of comparisons is large. Simultaneous lower and two-sided confidence intervals are also considered. All procedures can be implemented and evaluated using freely available and general R code.

Proof of concept and dose estimation with binary responses under model uncertainty.

This article suggests a unified framework for testing Proof of Concept (PoC) and estimating a target dose for the benefit of a more comprehensive, robust and powerful analysis in phase II or similar clinical trials. From a pre-specified set of candidate models, we choose the ones that best describe the observed dose-response. To decide which models, if any, significantly pick up a dose effect, we construct the permutation distribution of the minimum P-value over the candidate set. This allows us to find critical values and multiplicity adjusted P-values that control the familywise error rate of declaring any spurious effect in the candidate set as significant. Model averaging is then used to estimate a target dose. Popular single or multiple contrast tests for PoC, such as the Cochran-Armitage, Dunnett or Williams tests, are only optimal for specific dose-response shapes and do not provide target dose estimates with confidence limits. A thorough evaluation and comparison of our approach to these tests reveal that its power is as good or better in detecting a dose-response under various shapes with many more additional benefits: It incorporates model uncertainty in PoC decisions and target dose estimation, yields confidence intervals for target dose estimates and extends to more complicated data structures. We illustrate our method with the analysis of a Phase II clinical trial.

Testing marginal homogeneity against stochastic order in multivariate ordinal data.

SUMMARY: Many assessment instruments used in the evaluation of toxicity, safety, pain, or disease progression consider multiple ordinal endpoints to fully capture the presence and severity of treatment effects. Contingency tables underlying these correlated responses are often sparse and imbalanced, rendering asymptotic results unreliable or model fitting prohibitively complex without overly simplistic assumptions on the marginal and joint distribution. Instead of a modeling approach, we look at stochastic order and marginal inhomogeneity as an expression or manifestation of a treatment effect under much weaker assumptions. Often, endpoints are grouped together into physiological domains or by the body function they describe. We derive tests based on these subgroups, which might supplement or replace the individual endpoint analysis because they are more powerful. The permutation or bootstrap distribution is used throughout to obtain global, subgroup, and individual significance levels as they naturally incorporate the correlation among endpoints. We provide a theorem that establishes a connection between marginal homogeneity and the stronger exchangeability assumption under the permutation approach. Multiplicity adjustments for the individual endpoints are obtained via stepdown procedures, while subgroup significance levels are adjusted via the full closed testing procedure. The proposed methodology is illustrated using a collection of 25 correlated ordinal endpoints, grouped into six domains, to evaluate toxicity of a chemical compound.