Bayesian adaptive dose‐escalation procedures for binary and continuous responses utilizing a gain function

One of the main aims of early phase clinical trials is to identify a safe dose with an indication of therapeutic benefit to administer to subjects in further studies. Ideally therefore, dose‐limiting events (DLEs) and responses indicative of efficacy should be considered in the dose‐escalation procedure. Several methods have been suggested for incorporating both DLEs and efficacy responses in early phase dose‐escalation trials. In this paper, we describe and evaluate a Bayesian adaptive approach based on one binary response (occurrence of a DLE) and one continuous response (a measure of potential efficacy) per subject. A logistic regression and a linear log‐log relationship are used respectively to model the binary DLEs and the continuous efficacy responses. A gain function concerning both the DLEs and efficacy responses is used to determine the dose to administer to the next cohort of subjects. Stopping rules are proposed to enable efficient decision making. Simulation results shows that our approach performs better than taking account of DLE responses alone. To assess the robustness of the approach, scenarios where the efficacy responses of subjects are generated from an E _max model, but modelled by the linear log–log model are also considered. This evaluation shows that the simpler log–log model leads to robust recommendations even under this model showing that it is a useful approximation to the difficulty in estimating E _max model. Additionally, we find comparable performance to alternative approaches using efficacy and safety for dose‐finding. Copyright © 2015 John Wiley & Sons, Ltd.     

Psychosocial risk factors for excessive gestational weight gain: A systematic review

BackgroundExcessive weight gain during pregnancy can have adverse health outcomes for mother and infant throughout pregnancy. However, few studies have identified the psychosocial factors that contribute to women gaining excessive weight during pregnancy.AimTo review the existing literature that explores the impact of psychosocial risk factors (psychological distress, body image dissatisfaction, social support, self-efficacy and self-esteem) on excessive gestational weight gain.MethodsA systematic review of peer-reviewed English articles using Academic Search Complete, Cumulative Index to Nursing and Allied Health Literature, MEDLINE Complete, PsycINFO, Informit, Web of Science, and Scopus was conducted. Quantitative studies that investigated psychosocial factors of excessive GWG, published between 2000 and 2014 were included. Studies investigating mothers with a low risk of mental health issues and normally-developing foetuses were eligible for inclusion. From the total of 474 articles located, 12 articles were identified as relevant and were subsequently reviewed in full.FindingsSignificant associations were found between depression, body image dissatisfaction, and social support with excessive gestational weight gain. No significant relationships were reported between anxiety, stress, self-efficacy, or self-esteem and excessive gestational weight gain.ConclusionThe relationship between psychosocial factors and weight gain in pregnancy is complex; however depression, body dissatisfaction and social support appear to have a direct relationship with excessive gestational weight gain. Further research is needed to identify how screening for, and responding to, psychosocial risk factors for excessive gestational weight gain can be successfully incorporated into current antenatal care.     

On testing random effects in the nested-error regression model

We derive an exact F-test for random effects in the nested-error regression model. The derivation utilizes a matrix decomposition that offers a transformation of the response vector into two independent subvectors. When the random effects are absent, the test statistic reduces to a ratio of two independent residual sums of squares that are computed by fitting a regression model using each subvector. A small simulation study compares the power of the F-test with various recent tests and shows that the proposed test has a competitive performance under small as well as large number of clusters.     

Estimating nonlinear additive models with nonstationarities and correlated errors

In this paper, we study a nonparametric additive regression model suitable for a wide range of time series applications. Our model includes a periodic component, a deterministic time trend, various component functions of stochastic explanatory variables, and an AR(p) error process that accounts for serial correlation in the regression error. We propose an estimation procedure for the nonparametric component functions and the parameters of the error process based on smooth backfitting and quasimaximum likelihood methods. Our theory establishes convergence rates and the asymptotic normality of our estimators. Moreover, we are able to derive an oracle‐type result for the estimators of the AR parameters: Under fairly mild conditions, the limiting distribution of our parameter estimators is the same as when the nonparametric component functions are known. Finally, we illustrate our estimation procedure by applying it to a sample of climate and ozone data collected on the Antarctic Peninsula.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

The effects of data input errors on subsequent statistical inference

Data input errors can potentially affect statistical inferences, but little research has been published to date on this topic. In the present paper, we report the effect of data input errors on the statistical inferences drawn about population parameters in an empirical study involving 280 students from two Polish universities, namely the Warsaw University of Life Sciences – SGGW and the University of Information Technology and Management in Rzeszow. We found that 28% of the students committed at least one data error. While some of these errors were small and did not have any real effect, a few of them had substantial effects on the statistical inferences drawn about the population parameters.     

On the usefulness of knowledge of error variances in the consistent estimation of an unreplicated ultrastructural model

This article considers an unreplicated ultrastructural model and discusses the asymptotic properties of three consistent estimators of slope parameter arising from the knowledge of measurement error variances. Conditions are deduced when knowing the error variances associated with both the study and the explanatory variables is more/less beneficial than using a single error variance in the formulation of slope estimators.     

Estimating the impact of change in pre-pregnancy body mass index on development of Gestational Diabetes Mellitus: An Australian population-based cohort

BackgroundInternational studies examining maternal overweight and obesity have found GDM risk increases with increasing weight gain between pregnancies.AimThe study aimed to estimate the association between pre-pregnancy maternal body mass index (BMI), change in BMI between pregnancies and Gestational Diabetes Mellitus (GDM) amongst women with consecutive births in an Australian cohort.MethodsWe used a population cohort of women who had at least two consecutive singleton births between 2010 and 2017 in one NSW health district to investigate the risk of GDM in the pregnancy after the index pregnancy, BMI change between pregnancies and the impact of BMI change on risk of GDM.FindingsOf 10,074 women 1987 (16.7%) had no GDM in the index pregnancy but GDM in the subsequent one while 823 (8.2%) had GDM in both pregnancies. No change in BMI between pregnancies occurred in 47% of women, while 12% had a decrease and 41% an increase. After adjusting for socio-demographic characteristics and selected maternal and perinatal confounders, a reduction in BMI between births in women without GDM in the index pregnancy was associated with a 36% lower risk in GDM (aRR: 0.64; 95% CI: 0.49?0.85), while an increase in BMI was associated with increased risk of GDM with the greatest risk amongst those who gained 4+ kg/m² (aRR 2.27; 95%CI: 1.88–2.75).ConclusionInterpregnancy weight change is an important modifiable risk factor for the risk of GDM in a subsequent pregnancy. Clinical guidelines and health messages about interpregnancy weight change are important for all women.     

Behavior Change and the Freshman 15: Tracking Physical Activity and Dietary Patterns in 1st-Year University Women

Objective and Participants: The authors assessed the stability of diet and physical activity and their relationship to weight changes in first-year university women. Methods: They collected anthropometric and body composition data from 101 resident women at the beginning of their first year of college and again at 12 months. The authors obtained physical activity and dietary logs 4 times throughout the year. Results: Caloric intake decreased over 12 months in all participants (p = .01). There was little change in physical activity in participants who lost weight (p = .73, d = .18). Those who gained weight experienced a trend toward decreased physical activity (p = .13, d = .38). A significant Time X Group interaction on physical activity (p = .04) suggests that physical activity patterns differed substantially between individuals who gained weight and those who lost weight. Conclusions: Reduction in physical activity appears to be the defining characteristic in freshman weight gain.     

Testing for heteroscedasticity of exponential correlation mixed-effects linear models based on M-estimation

Homogeneity of variance is a basic assumption in longitudinal data analysis. However, the assumption is not necessarily appropriate. In this paper, Fisher scoring method is applied to get M-estimator in the exponential correlation mixed-effects linear model. The score tests for heteroscedasticity and correlation coefficient based on M-estimator are then studied. Monte Carlo method is applied to investigate the properties of test statistics. At last, the methods and properties are illustrated by an actual data example.