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Purpose: The provision and funding of long-term care (LTC) for older people varies between European countries. Despite differences, there is limited information about the comparative performance of LTC systems in Europe. In this study, we compared quality of life (QoL) of informal carers of home care service users in Austria, England and Finland.Informal carers were surveyed in Austria, England and Finland. The study data (n = 835) contained information on social care-related quality of life (SCRQoL) associated with the ASCOT-Carer measure, and characteristics of carers and care recipients from each country. We applied risk-adjustment methods using a fractional regression model to produce risk-adjusted SCRQoL scores for the comparative analysis. In a sensitivity analysis, we applied multiple imputation to missing data to validate our findings.We found that the mean values of the risk-adjusted SCRQoL of informal carers in England were 1.4–2.9% and 0.3–0.5% higher than in Finland and Austria, and the mean values of the risk-adjusted SCRQoL of carers in Austria were 0.8–2.7% higher than in Finland. Differences in the mean values of the country-specific risk-adjusted SCRQoL scores were small and statistically non-significant. English informal carers were less healthy and co-resided with care resipients more often than carers in Austria or Finland.Small differences between the risk-adjusted SCRQoL scores between Austria, England and Finland are consistent with the observation that the countries provide different types of support for informal carers. Our results help local and national decision-makers in these countries to benchmark their informal care support systems.Methods: The provision and funding of long-term care (LTC) for older people varies between European countries. Despite differences, there is limited information about the comparative performance of LTC systems in Europe. In this study, we compared quality of life (QoL) of informal carers of home care service users in Austria, England and Finland.Informal carers were surveyed in Austria, England and Finland. The study data (n = 835) contained information on social care-related quality of life (SCRQoL) associated with the ASCOT-Carer measure, and characteristics of carers and care recipients from each country. We applied risk-adjustment methods using a fractional regression model to produce risk-adjusted SCRQoL scores for the comparative analysis. In a sensitivity analysis, we applied multiple imputation to missing data to validate our findings.We found that the mean values of the risk-adjusted SCRQoL of informal carers in England were 1.4–2.9% and 0.3–0.5% higher than in Finland and Austria, and the mean values of the risk-adjusted SCRQoL of carers in Austria were 0.8–2.7% higher than in Finland. Differences in the mean values of the country-specific risk-adjusted SCRQoL scores were small and statistically non-significant. English informal carers were less healthy and co-resided with care resipients more often than carers in Austria or Finland.Small differences between the risk-adjusted SCRQoL scores between Austria, England and Finland are consistent with the observation that the countries provide different types of support for informal carers. Our results help local and national decision-makers in these countries to benchmark their informal care support systems.Results: The provision and funding of long-term care (LTC) for older people varies between European countries. Despite differences, there is limited information about the comparative performance of LTC systems in Europe. In this study, we compared quality of life (QoL) of informal carers of home care service users in Austria, England and Finland.Informal carers were surveyed in Austria, England and Finland. The study data (n = 835) contained information on social care-related quality of life (SCRQoL) associated with the ASCOT-Carer measure, and characteristics of carers and care recipients from each country. We applied risk-adjustment methods using a fractional regression model to produce risk-adjusted SCRQoL scores for the comparative analysis. In a sensitivity analysis, we applied multiple imputation to missing data to validate our findings.We found that the mean values of the risk-adjusted SCRQoL of informal carers in England were 1.4–2.9% and 0.3–0.5% higher than in Finland and Austria, and the mean values of the risk-adjusted SCRQoL of carers in Austria were 0.8–2.7% higher than in Finland. Differences in the mean values of the country-specific risk-adjusted SCRQoL scores were small and statistically non-significant. English informal carers were less healthy and co-resided with care resipients more often than carers in Austria or Finland.Small differences between the risk-adjusted SCRQoL scores between Austria, England and Finland are consistent with the observation that the countries provide different types of support for informal carers. Our results help local and national decision-makers in these countries to benchmark their informal care support systems.Conclusion: The provision and funding of long-term care (LTC) for older people varies between European countries. Despite differences, there is limited information about the comparative performance of LTC systems in Europe. In this study, we compared quality of life (QoL) of informal carers of home care service users in Austria, England and Finland.Informal carers were surveyed in Austria, England and Finland. The study data (n = 835) contained information on social care-related quality of life (SCRQoL) associated with the ASCOT-Carer measure, and characteristics of carers and care recipients from each country. We applied risk-adjustment methods using a fractional regression model to produce risk-adjusted SCRQoL scores for the comparative analysis. In a sensitivity analysis, we applied multiple imputation to missing data to validate our findings.We found that the mean values of the risk-adjusted SCRQoL of informal carers in England were 1.4–2.9% and 0.3–0.5% higher than in Finland and Austria, and the mean values of the risk-adjusted SCRQoL of carers in Austria were 0.8–2.7% higher than in Finland. Differences in the mean values of the country-specific risk-adjusted SCRQoL scores were small and statistically non-significant. English informal carers were less healthy and co-resided with care resipients more often than carers in Austria or Finland.Small differences between the risk-adjusted SCRQoL scores between Austria, England and Finland are consistent with the observation that the countries provide different types of support for informal carers. Our results help local and national decision-makers in these countries to benchmark their informal care support systems. [ABSTRACT FROM AUTHOR]

Background: Unemployment is highly prevalent in populations with alcohol and drug dependence and the employment support offered in addiction-treatment programmes is ineffective. Individual Placement and Support (IPS) is an evidence-based intervention for competitive employment. IPS has been extensively studied in severe mental illness and physical disabilities, but there have been no formal randomised controlled trials (RCTs) in alcohol and drug dependence. The Individual Placement and Support for Alcohol and Drug Dependence (IPS-AD) study should determine whether IPS for patients with alcohol use disorder (AUD), opioid use disorder (OUD) and other drug use disorder is effective.Design/methods: The IPS-AD study is a seven-site, pragmatic, two-arm, parallel-group, superiority RCT. IPS-AD includes a realist process evaluation. Eligible patients (adult, unemployed or economically inactive for at least 6 months and wishing to obtain open job market employment and enrolled in ongoing community treatment-as-usual (TAU; the control condition) in England for AUD, OUD and other drug use disorders) will be randomised (1:1) to receive TAU and any standard employment support, or TAU plus IPS (the experimental condition) for 9 months with up to 4 months of in-work support. The primary outcome measure will be competitive employment status (at least 1 day (7 h)) during an 18-month follow-up, determined by patient-level, trial-data-linkage with national tax and state benefit databases. From meta-analysis, an 18% target difference on this measure of vocational effectiveness (for the experimental intervention) and a two-sided 5% level of statistical significance, will require a minimum target sample of 832 participants to achieve 90% power for a pre-registered, mixed-effects, multi-variable logistic regression model. A maximum-likelihood multiple-imputation approach will manage missing outcome data. IPS-AD has six vocational secondary outcome measures during the 18-month follow-up: (1) total time in competitive employment (and corresponding National Insurance contributions and tax paid); (2) time from randomisation to first competitive employment; (3) number of competitive job appointments; (4) job tenure (length of longest held competitive employment); (5) sustained employment (tenure in a single appointment for at least 13 weeks); and (6) job search self-efficacy. A primary cost-benefit analysis and a secondary cost-effectiveness analysis will be done using the primary outcome and secondary vocational outcomes, respectively and will include addiction treatment and social and health outcomes and their associated reference costs. The process evaluation will address IPS implementation and delivery.Discussion: The IPS-AD study is the first large-scale, multi-site, definitive, superiority RCT of IPS for people with alcohol and drug dependence. Findings from the study will have substantial implications for service delivery.Trial Registration: ISRCTN Registry, ID: ISRCTN24159790. Registered on 1 February 2018. [ABSTRACT FROM AUTHOR]

Focus and outcomes for participants Missing data are ubiquitous in observational studies, and the simple solution of restricting the analyses to the subset with complete records will often result in bias and loss of power. The seriousness of these issues for resulting inferences depends on both the mechanism causing the missing data and the form of the substantive question and associated model. The methodological literature on methods for the analysis of partially observed data has grown substantially over the last twenty years, and although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions regarding the analysis is threatening the validity and reproducibility of modern research. In this symposium leading researchers in missing data methodology will present practical guidance on how to select an appropriate method to handle missing data, describe how to report the results from such an analysis and describe how to conduct sensitivity analyses in the multiple imputation framework. Rationale for the symposium, including for its inclusion in the Congress One of the sub-themes of WCE 2021 is "Translation from research to policy and practice". Although there is a growing body of literature surrounding missing data methodology, evidence from systematic reviews suggests that missing data is still often not handled appropriately. If practice is to change, it is important to educate applied researchers regarding the available methodology and provide practical guidance on how to determine the best method for handling missing data. An important part of this is providing guidance on the reporting of results from analyses with missing data. This is particularly pertinent given the current emphasis on reproducibility of research findings. In this symposium we bring some of the latest research from the Missing Data Topic Group of the STRengthening Analytical Thinking for Observational Studies (STRATOS) initiative whose aim is to provide accessible and accurate guidance in the design and analysis of observational studies in order to increasie the reliability and validity of observational research. Presentation program Causal Diagrams to guide the treatment of missing data in epidemiological studies – With incomplete data, the "missing at random" (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. However, MAR is difficult to define in the context of multivariate missingness. In this talk, causal diagrams are introduced as an alternative framework for specifying practically accessible assumptions regarding missing data. The concept of recoverability is also introduced, which reflects whether a given parameter can be expressed as functions of the available data distribution and thus estimated consistently using the data alone. This concept can be used to guide the choice of method for handling missing data, and in particular whether sensitivity analyses allowing for data to be missing not at random are necessary. A comparison of three popular methods for handling missing data: complete case analysis, weighting and multiple imputation – In this talk the three most common methods for handling missing data are compared and contrasted. In particular, the strengths and weakness of each approach is discussed, and guidelines are provided regarding when each of the methods might be adopted over the others. The methods are illustrated on data from the Youth Cohort Study (YCS) of England, Wales and Scotland, 1984-2002. Sensitivity analyses using the Not at Random Fully Conditional Specification (NARFCS) procedure – Standard implementations of multiple imputation are only warranted to yield unbiased estimates under the missing at random (MAR) assumption, but its plausibility can only be assessed using subject-matter knowledge, not data. Therefore, it is important to perform sensitivity analyses to explore the robustness of results to assumptions concerning the missingness mechanism. In this talk we present the NARFCS procedure for conducting multivariate imputation under missing not at random (MNAR) assumptions. Using a case study from the Longitudinal Study of Australian Children, we illustrate a sensitivity analysis using the NARFCS procedure. We work through the steps involved, from deciding to perform the sensitivity analysis, and specifying the sensitivity parameters, through to carrying out the sensitivity analysis using NARFCS, which is implemented using multiple imputation by chained equation procedures in R and Stata. Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies (TARMOS) framework – This talk outlines a practical framework for handling and reporting the analysis of incomplete data in observational studies, illustrated using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records' analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings. Names of presenters Katherine Lee Roderick Little Cattram Nguyen Rosie Cornish [ABSTRACT FROM AUTHOR]

Multiple imputation is increasingly recommended in epidemiology to adjust for the bias and loss of information that may occur in analyses restricted to study participants with complete data (“complete-case analyses”). However, little guidance is available on applying the method, including which variables to include in the imputation model and the number of imputations needed. Here, the authors used multiple imputation to analyze the prevalence of wheeze among 81-month-old children in the Avon Longitudinal Study of Parents and Children (Avon, United Kingdom; 1991–1999) and the association of wheeze with gender, maternal asthma, and maternal smoking. The authors examined how inclusion of different types of variables in the imputation model affected point estimates and precision, and assessed the impact of number of imputations on Monte Carlo variability. Inclusion of variables associated with the outcome in the imputation model increased odds ratios and reduced standard errors. When only 5 or 10 imputations were used, variability due to the imputation procedure was substantial enough to affect conclusions. Careful preliminary analysis identified the scope for multiple imputation to reduce bias and improve efficiency and provided guidance for building the imputation model. When data are missing, such preliminary analyses should be routinely undertaken and reported, regardless of whether multiple imputation is used in the final analysis. [ABSTRACT FROM PUBLISHER]

There is strong evidence that colorectal cancer survival differs between socioeconomic groups. We analysed data on 2481 patients diagnosed during 1989-1997 and recruited to a randomised controlled clinical trial (AXIS, ISRCTN32414363) of chemotherapy and radiotherapy for colorectal cancer. Crude and relative survival at 1 and 5 years was estimated in five categories of socioeconomic deprivation. Multiple imputation was used to account for missing data on tumour stage. A multivariable fractional polynomial model was fitted to estimate the excess hazard of death in each deprivation category, adjusting for the confounding effects of age, stage, cancer site (colon, rectum) and sex, using generalised linear models. Relative survival in the trial patients was higher than in the general population of England and Wales. The socioeconomic gradient in survival was much smaller than that seen for colorectal cancer patients in the general population, both at 1 year -3.2% (95% CI -7.3 to 1.0%, P=0.14) and at 5 years -1.7% (95% CI -8.3 to 4.9%, P=0.61). Given equal treatment, colorectal cancer survival in England and Wales does not appear to depend on socioeconomic status, suggesting that the socioeconomic gradient in survival in the general population could well be due to health-care system factors. [ABSTRACT FROM AUTHOR]