Patient-controlled hospital admission for patients with severe mental disorders: study protocol for a nationwide prospective multicentre study.

INTRODUCTION: Patient-controlled hospital admission for individuals with severe mental disorders is a novel approach in mental healthcare. Patients can admit themselves to a hospital unit for a short stay without being assessed by a psychiatrist or contacting the emergency department. Previous studies assessing the outcomes of patient-controlled hospital admission found trends towards reduction in the use of coercive measures and length of hospital stay; however, these studies have methodological shortcomings and small sample sizes. Larger studies are needed to estimate the effect of patient-controlled hospital admission on the use of coercion and of healthcare services. DESIGN AND METHODS: We aim to recruit at least 315 patients who are offered a contract for patient-controlled hospital admissions in eight different hospitals in Denmark. Patients will be followed-up for at least 1 year to compare the use of coercive measures and of healthcare services, the use of medications and suicidal behaviour. Descriptive statistics will be used to investigate hospitalisations, global assessment of functioning (GAF) and patient satisfaction with treatment. To minimise selection bias, we will match individuals using patient-controlled hospital admission and controls with a 1:5 ratio via a propensity score based on the following factors: sex, age group, primary diagnosis, substance abuse as secondary diagnosis, coercion, number of psychiatric bed days, psychiatric history, urbanity and suicidal behaviour. Additionally, a historical control study will be undertaken in which patients serve as their own control group prior to index date. ETHICS AND DISSEMINATION: The study has been approved by The Danish Health and Medicines Authority (j.nr.: 3-3013-934/1/) and by The Danish Data Protection Agency (j.nr.: 2012-58-0004). The study was categorised as a register study by The Danish Health Research Ethics Committee and therefore no further approval was needed (j.nr.: H-2-2014-FSP70). Findings will be disseminated through scientific publications, presentations and in a PhD thesis.Danish Ministry of Health and Mental Health Centre, Frederiksberg

Multi-state models and arthroplasty histories after unilateral total hip arthroplasties: Introducing the Summary Notation for Arthroplasty Histories

Background and purpose: An increasing number of patients have several joint replacement procedures during their lifetime. We investigated the use and suitability of multi-state model techniques in providing a more comprehensive analysis and description of complex arthroplasty histories held in arthroplasty registries than are allowed for with traditional survival methods. Patients and methods: We obtained data from the Australian Orthopaedic Association National Joint Replacement Registry on patients (n = 84,759) who had undergone a total hip arthroplasty for osteoarthritis in the period 2002–2008. We set up a multi-state model where patients were followed from their first recorded arthroplasty to several possible states: revision of first arthroplasty, either a hip or knee as second arthroplasty, revision of the second arthroplasty, and death. The Summary Notation for Arthroplasty Histories (SNAH) was developed in order to help to manage and analyze this type of data. Results: At the end of the study period, 12% of the 84,759 patients had received a second hip, 3 times as many as had received a knee. The estimated probabilities of having received a second arthroplasty decreased with age. Males had a lower transition rate for receiving a second arthroplasty, but a higher mortality rate. Interpretation: Multi-state models in combination with SNAH codes are well suited to the management and analysis of arthroplasty registry data on patients who experience multiple joint procedures over time. We found differences in the progression of joint replacement procedures after the initial total hip arthroplasty regarding type of joint, age, and sex.Marianne H Gillam, Philip Ryan, Amy Salter, Stephen E Grave

Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model

A reference relative time-scale as an alternative to chronological age for cohorts with long follow-up

Background: Epidemiologists have debated the appropriate time-scale for cohort survival studies; chronological age or time-on-study being two such time-scales. Importantly, assessment of risk factors may depend on the choice of time-scale. Recently, chronological or attained age has gained support but a case can be made for a ‘reference relative time-scale’ as an alternative which circumvents difficulties that arise with this and other scales. The reference relative time of an individual participant is the integral of a reference population hazard function between time of entry and time of exit of the individual. The objective here is to describe the reference relative time-scale, illustrate its use, make comparison with attained age by simulation and explain its relationship to modern and traditional epidemiologic methods. Results: A comparison was made between two models; a stratified Cox model with age as the time-scale versus an un-stratified Cox model using the reference relative time-scale. The illustrative comparison used a UK cohort of cotton workers, with differing ages at entry to the study, with accrual over a time period and with long follow-up. Additionally, exponential and Weibull models were fitted since the reference relative time-scale analysis need not be restricted to the Cox model. A simulation study showed that analysis using the reference relative time-scale and analysis using chronological age had very similar power to detect a significant risk factor and both were equally unbiased. Further, the analysis using the reference relative time-scale supported fully-parametric survival modelling and allowed percentile predictions and mortality curves to be constructed. Conclusions: The reference relative time-scale was a viable alternative to chronological age, led to simplification of the modelling process and possessed the defined features of a good time-scale as defined in reliability theory. The reference relative time-scale has several interpretations and provides a unifying concept that links contemporary approaches in survival and reliability analysis to the traditional epidemiologic methods of Poisson regression and standardised mortality ratios. The community of practitioners has not previously made this connection

Joint synthesis of conditionally related multiple outcomes makes better use of data than separate meta-analyses

Background: When there are structural relationships between outcomes reported in different trials, separate analyses of each outcome do not provide a single coherent analysis, which is required for decision-making. For example, trials of intrapartum anti-bacterial prophylaxis (IAP) to prevent early onset group B streptococcal (EOGBS) disease can report three treatment effects: the effect on bacterial colonisation of the newborn, the effect on EOGBS, and the effect on EOGBS conditional on newborn colonisation. These outcomes are conditionally related, or nested, in a multi-state model. This paper shows how to exploit these structural relationships, providing a single coherent synthesis of all the available data, while checking to ensure that different sources of evidence are consistent. Results: Overall, the use of IAP reduces the risk of EOGBS (RR: 0.03; 95% Credible Interval (CrI): 0.002-0.13). Most of the treatment effect is due to the prevention of colonisation in newborns of colonised mothers (RR: 0.08, 95% CrI: 0.04-0.14). Node-splitting demonstrated that the treatment effect calculated using only direct evidence was consistent with that predicted from the remaining evidence (p=0.15). The findings accorded with previously published separate meta-analyses of the different outcomes, once these are re-analysed correctly accounting for zero cells. Conclusion: Multiple outcomes should be synthesised together where possible, taking account of their structural relationships. This generates an internally coherent analysis, suitable for decision making, in which estimates of each of the treatment effects are based on all available evidence (direct and indirect). Separate meta-analyses of each outcome have none of these properties

A frailty model for (interval) censored family survival data, applied to the age at onset of non-physical problems

Family survival data can be used to estimate the degree of genetic and environmental contributions to the age at onset of a disease or of a specific event in life. The data can be modeled with a correlated frailty model in which the frailty variable accounts for the degree of kinship within the family. The heritability (degree of heredity) of the age at a specific event in life (or the onset of a disease) is usually defined as the proportion of variance of the survival age that is associated with genetic effects. If the survival age is (interval) censored, heritability as usually defined cannot be estimated. Instead, it is defined as the proportion of variance of the frailty associated with genetic effects. In this paper we describe a correlated frailty model to estimate the heritability and the degree of environmental effects on the age at which individuals contact a social worker for the first time and to test whether there is a difference between the survival functions of this age for twins and non-twins. © 2009 The Author(s)

Contribution of income and job strain to the association between education and cardiovascular disease in 1.6 million Danish employees

AIMS: We examined the extent to which associations between education and cardiovascular disease (CVD) morbidity and mortality are attributable to income and work stress. METHODS AND RESULTS: We included all employed Danish residents aged 30-59 years in 2000. Cardiovascular disease morbidity analyses included 1 638 270 individuals, free of cardiometabolic disease (CVD or diabetes). Mortality analyses included 41 944 individuals with cardiometabolic disease. We assessed education and income annually from population registers and work stress, defined as job strain, with a job-exposure matrix. Outcomes were ascertained until 2014 from health registers and risk was estimated using Cox regression. During 10 957 399 (men) and 10 776 516 person-years (women), we identified 51 585 and 24 075 incident CVD cases, respectively. For men with low education, risk of CVD was 1.62 [95% confidence interval (CI) 1.58-1.66] before and 1.46 (95% CI 1.42-1.50) after adjustment for income and job strain (25% reduction). In women, estimates were 1.66 (95% CI 1.61-1.72) and 1.53 (95% CI 1.47-1.58) (21% reduction). Of individuals with cardiometabolic disease, 1736 men (362 234 person-years) and 341 women (179 402 person-years) died from CVD. Education predicted CVD mortality in both sexes. Estimates were reduced with 54% (men) and 33% (women) after adjustment for income and job strain. CONCLUSION: Low education predicted incident CVD in initially healthy individuals and CVD mortality in individuals with prevalent cardiometabolic disease. In men with cardiometabolic disease, income and job strain explained half of the higher CVD mortality in the low education group. In healthy men and in women regardless of cardiometabolic disease, these factors explained 21-33% of the higher CVD morbidity and mortality

An approach to trial design and analysis in the era of non-proportional hazards of the treatment effect

Background: Most randomized controlled trials with a time-to-event outcome are designed and analysed under the proportional hazards assumption, with a target hazard ratio for the treatment effect in mind. However, the hazards may be non-proportional. We address how to design a trial under such conditions, and how to analyse the results. Methods: We propose to extend the usual approach, a logrank test, to also include the Grambsch-Therneau test of proportional hazards. We test the resulting composite null hypothesis using a joint test for the hazard ratio and for time-dependent behaviour of the hazard ratio. We compute the power and sample size for the logrank test under proportional hazards, and from that we compute the power of the joint test. For the estimation of relevant quantities from the trial data, various models could be used; we advocate adopting a pre-specified flexible parametric survival model that supports time-dependent behaviour of the hazard ratio. Results: We present the mathematics for calculating the power and sample size for the joint test. We illustrate the methodology in real data from two randomized trials, one in ovarian cancer and the other in treating cellulitis. We show selected estimates and their uncertainty derived from the advocated flexible parametric model. We demonstrate in a small simulation study that when a treatment effect either increases or decreases over time, the joint test can outperform the logrank test in the presence of both patterns of non-proportional hazards. Conclusions: Those designing and analysing trials in the era of non-proportional hazards need to acknowledge that a more complex type of treatment effect is becoming more common. Our method for the design of the trial retains the tools familiar in the standard methodology based on the logrank test, and extends it to incorporate a joint test of the null hypothesis with power against non-proportional hazards. For the analysis of trial data, we propose the use of a pre-specified flexible parametric model that can represent a time-dependent hazard ratio if one is present