Maximum likelihood and pseudo score approaches for parametric time-to-event analysis with informative entry times

We develop a maximum likelihood estimating approach for time-to-event Weibull regression models with outcome-dependent sampling, where sampling of subjects is dependent on the residual fraction of the time left to developing the event of interest. Additionally, we propose a two-stage approach which proceeds by iteratively estimating, through a pseudo score, the Weibull parameters of interest (i.e., the regression parameters) conditional on the inverse probability of sampling weights; and then re-estimating these weights (given the updated Weibull parameter estimates) through the profiled full likelihood. With these two new methods, both the estimated sampling mechanism parameters and the Weibull parameters are consistently estimated under correct specification of the conditional referral distribution. Standard errors for the regression parameters are obtained directly from inverting the observed information matrix in the full likelihood specification and by either calculating bootstrap or robust standard errors for the hybrid pseudo score/profiled likelihood approach. Loss of efficiency with the latter approach is considered. Robustness of the proposed methods to misspecification of the referral mechanism and the time-to-event distribution is also briefly examined. Further, we show how to extend our methods to the family of parametric time-to-event distributions characterized by the generalized gamma distribution. The motivation for these two approaches came from data on time to cirrhosis from hepatitis C viral infection in patients referred to the Edinburgh liver clinic. We analyze these data here.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS725 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Understanding between-cluster variation in prevalence, and limits for how much variation is plausible

In clinical trials and observational studies of clustered binary data, understanding between-cluster variation is essential: in sample size and power calculations of cluster randomised trials, for example, the intra-cluster correlation coefficient is often specified. However, quantifications of between-cluster variation can be unintuitive, and an intra-cluster correlation coefficient as low as 0.04 may correspond to surprisingly large between-cluster differences. We suggest that understanding is improved through visualising the implied distribution of true cluster prevalences – possibly by assuming they follow a beta distribution – or by calculating their standard deviation, which is more readily interpretable than the intra-cluster correlation coefficient. Even so, the bounded nature of binary data complicates the interpretation of variances as primary measures of uncertainty, and entropy offers an attractive alternative. Appealing to maximum entropy theory, we propose the following rule of thumb: that plausible intra-cluster correlation coefficients and standard deviations of true cluster prevalences are both bounded above by the overall prevalence, its complement, and one third. We also provide corresponding bounds for the coefficient of variation, and for a different standard deviation and intra-cluster correlation defined on the log odds scale. Using previously published data, we observe the quantities defined on the log odds scale to be more transportable between studies with different outcomes with different prevalences than the intra-cluster correlation and coefficient of variation. The latter increase and decrease, respectively, as prevalence increases from 0% to 50%, and the same is true for our bounds. Our work will help clinical trialists better understand between-cluster variation and avoid specifying implausibly high values for the intra-cluster correlation in sample size and power calculations

Ignorability for general longitudinal data

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not actually exist. Several examples illustrate how stability may be assessed

Missing at random: a stochastic process perspective

We offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness at random conditions are equivalent to requiring particular stochastic processes to be adapted to a set-indexed filtration. These measurability conditions ensure the usual factorization of likelihood ratios. We illustrate how the theory extends easily to incorporate explanatory variables, to describe longitudinal data in continuous time, and to admit more general coarsening of observations

Factors influencing child protection professionals' decision-making and multidisciplinary collaboration in suspected abusive head trauma cases: a qualitative study

Clinicians face unique challenges when assessing suspected child abuse cases. The majority of the literature exploring diagnostic decision-making in this field is anecdotal or survey-based and there is a lack of studies exploring decision-making around suspected abusive head trauma (AHT). We aimed to determine factors influencing decision-making and multidisciplinary collaboration in suspected AHT cases, amongst 56 child protection professionals. Semi-structured interviews were conducted with clinicians (25), child protection social workers (10), legal practitioners (9, including 4 judges), police officers (8), and pathologists (4), purposively sampled across southwest United Kingdom. Interviews were recorded, transcribed and imported into NVivo for thematic analysis (38% double-coded). We identified six themes influencing decision-making: ‘professional’, ‘medical’, ‘circumstantial’, ‘family’, ‘psychological’ and ‘legal’ factors. Participants diagnose AHT based on clinical features, the history, and the social history, after excluding potential differential diagnoses. Participants find these cases emotionally challenging but are aware of potential biases in their evaluations and strive to overcome these. Barriers to decision-making include lack of experience, uncertainty, the impact on the family, the pressure of making the correct diagnosis, and disagreements between professionals. Legal barriers include alternative theories of causation proposed in court. Facilitators include support from colleagues and knowledge of the evidence-base. Participants’ experiences with multidisciplinary collaboration are generally positive, however child protection social workers and police officers are heavily reliant on clinicians to guide their decision-making, suggesting the need for training on the medical aspects of physical abuse for these professionals and multidisciplinary training that provides knowledge about the roles of each agency

Adverse childhood experiences during childhood and academic attainment at age 7 and 11 years: an electronic birth cohort study

Objectives Adverse childhood experiences (ACEs) have a negative impact on childhood health, but their impact on education outcomes is less well known. We investigated whether or not ACEs were associated with reduced educational attainment at age 7 and 11 years. Study design The study design used in the study is a population-based electronic cohort study. Methods We analysed data from a total population electronic child cohort in Wales, UK. ACEs (exposures) were living with an adult household member with any of (i) serious mental illness, (ii) common mental disorder (CMD), (iii) an alcohol problem; (iv) child victimisation, (v) death of a household member and (vi) low family income. We used multilevel logistic regression to model exposure to these ACEs and not attaining the expected level at statutory education assessments, Key Stage (KS) 1 and KS2 separately, adjusted for known confounders including perinatal, socio-economic and school factors. Results There were 107,479 and 43,648 children included in the analysis, with follow-up to 6–7 years (KS1) and 10–11 years (KS2), respectively. An increased risk of not attaining the expected level at KS1 was associated with living with adult household members with CMD (adjusted odds ratio [aOR]: 1.13 [95% confidence interval [CI]: 1.09–1.17]) or an alcohol problem (adjusted odds ratio [aOR]: 1.16 [95% confidence interval [CI]: 1.10–1.22]), childhood victimisation (adjusted odds ratio [aOR]: 1.58 [95% confidence interval [CI]: 1.37–1.82]), death of a household member (adjusted odds ratio [aOR]: 1.14 [95% confidence interval [CI]: 1.04–1.25]) and low family income (adjusted odds ratio [aOR]: 1.92 [95% confidence interval [CI]: 1.84–2.01]). Similar results were observed for KS2. Children with multiple adversities had substantially increased odds of not attaining the expected level at each educational assessment. Conclusion The educational potential of many children may not be achieved due to exposure to adversity in childhood. Affected children who come in to contact with services should have relevant information shared between health and care services, and schools to initiate and facilitate a coordinated approach towards providing additional support and help for them to fulfil their educational potential, and subsequent economic and social participation

Soil moisture content measurement using optical fiber long period gratings

The use of an optical fibre long period grating (LPG) as a soil moisture sensor is reported. Characterization of the device in both clay and sandy soils revealed a sensitivity to moisture levels in the range 10-50%, and the results were compared with the output from a Theta probe, the standard soil moisture sensor, which measures the impedance of the soil. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only