Multiple imputation in Cox regression when there are time-varying effects of covariates

In Cox regression, it is important to test the proportional hazards assumption and sometimes of interest in itself to study time‐varying effects (TVEs) of covariates. TVEs can be investigated with log hazard ratios modelled as a function of time. Missing data on covariates are common and multiple imputation is a popular approach to handling this to avoid the potential bias and efficiency loss resulting from a “complete‐case” analysis. Two multiple imputation methods have been proposed for when the substantive model is a Cox proportional hazards regression: an approximate method (Imputing missing covariate values for the Cox model in Statistics in Medicine (2009) by White and Royston) and a substantive‐model‐compatible method (Multiple imputation of covariates by fully conditional specification: accommodating the substantive model in Statistical Methods in Medical Research (2015) by Bartlett et al). At present, neither accommodates TVEs of covariates. We extend them to do so for a general form for the TVEs and give specific details for TVEs modelled using restricted cubic splines. Simulation studies assess the performance of the methods under several underlying shapes for TVEs. Our proposed methods give approximately unbiased TVE estimates for binary covariates with missing data, but for continuous covariates, the substantive‐model‐compatible method performs better. The methods also give approximately correct type I errors in the test for proportional hazards when there is no TVE and gain power to detect TVEs relative to complete‐case analysis. Ignoring TVEs at the imputation stage results in biased TVE estimates, incorrect type I errors, and substantial loss of power in detecting TVEs. We also propose a multivariable TVE model selection algorithm. The methods are illustrated using data from the Rotterdam Breast Cancer Study. R code is provided

Investigating the effects of long-term dornase alfa use on lung function using registry data.

BACKGROUND: Dornase alfa (DNase) is one of the commonest cystic fibrosis (CF) treatments and is often used for many years. However, studies have not evaluated the effectiveness of its long-term use. We aimed to use UK CF Registry data to investigate the effects of one-, two-, three-, four- and five-years of DNase use on lung function to see if the benefits of short-term treatment use are sustained long term. METHODS: We analysed data from 4,198 people in the UK CF Registry from 2007 to 2015 using g-estimation. By controlling for time-dependent confounding we estimated the effects of long-term DNase use on percent predicted FEV1 (ppFEV1) and investigated whether the effect differed by ppFEV1 at treatment initiation or by age. RESULTS: Considering the population as a whole, there was no significant effect of one-year's use of DNase; change in ppFEV1 over one year was -0.1% in the treated compared to the untreated (p = 0.51) and this did not change with long-term use. However, treatment was estimated to be more beneficial in people with lower lung function (p  70%

Results from an online survey of adults with cystic fibrosis: Accessing and using life expectancy information

Cystic fibrosis (CF) is the one of the most common inherited diseases. It affects around 10,000 people in the UK, and the median survival age is 47. Recent developments making use of longitudinal patient registry data are producing more detailed and relevant information about predicted life expectancy in CF based on current age and clinical measurements. The objective of this study was to conduct an online survey of adults with CF living in the UK using a web-based questionnaire to investigate: (i) if and how they access information on life expectancy; (ii) what they use it for; (iii) if they want more personalised information on life expectancy or the time until other milestones. The survey was advertised through the Cystic Fibrosis Trust using social media. There were 85 respondents, covering men (39%) and women (61%) aged 16–65. 75% had received information on life expectancy either from their CF care team (34%) or other sources (71%), the most common being the Cystic Fibrosis Trust website and research literature. Most people who received information found it to be beneficial and reported using it in a variety of ways, including to plan strategies for maintaining as best health as possible and to psychologically manage current health status. 82% of respondents were interested in more personalised information about their life expectancy, and participants also noted interest in other outcomes, including time to needing transplant or reaching a low level of lung function. Themes arising in text responses included the importance of good communication of information, the difficulty of relating general information to one’s own circumstances, and a desire for increased information on factors that impact on survival in CF. As an outcome from this work, research is underway to establish how information on life expectancy can be presented to people with CF in an accessible way

In praise of Prais-Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series

Interrupted time series are increasingly being used to assess the population impact of public health interventions. These data are usually correlated over time (auto correlated) and this must be accounted for in the analysis. Typically, this is done using either the Prais-Winsten method, the Newey-West method, or autoregressive-moving-average (ARMA) modeling. In this paper, we illustrate these methods via a study of pneumococcal vaccine introduction and explore their performance under 20 simulated autocorrelation scenarios with sample sizes ranging between 20 and 300. We show that in terms of mean square error, the Prais-Winsten and ARMA methods perform best, while in terms of coverage the Prais-Winsten method generally performs better than other methods. All three methods are unbiased. As well as having good statistical properties, the Prais-Winsten method is attractive because it is decision-free and produces a single measure of autocorrelation that can be compared between studies and used to guide sample size calculations. We would therefore encourage analysts to consider using this simple method to analyze interrupted time series

Longitudinal associations between marine omega-3 supplement users and Coronary Heart Disease in a UK population-based cohort

Objectives: Assess the association between marine omega-3 polyunsaturated fatty acid (n 3 PUFA) intake from supplements, mainly cod liver oil, and coronary heart disease (CHD) mortality. Design: Prospective cohort study, with three exposure measurements over 22 y. Setting: Norfolk-based European Prospective Investigation into Cancer (EPIC-Norfolk, UK). Participants: 22,035 men and women from the general population, 39-79 y at recruitment. Exposure: Supplement use was assessed in three questionnaires (1993-1998; 2002-2004; 2004-2011). Participants were grouped into non-supplement users (NSU), n 3 PUFA supplement users (SU+n3) and non n 3 PUFA supplement users (SU n3). Cox regression adjusted for time-point specific variables: age, smoking, prevalent illnesses, BMI, alcohol consumption, physical activity and season and baseline assessments of sex, social class, education and dietary intake (7-day diet diary). Primary and secondary outcome measures: During a median of 19 y follow-up, 1562 CHD deaths were registered for 22,035 included participants. Results: Baseline supplement use was not associated with CHD mortality, but baseline food and supplement intake of n 3 PUFA was inversely associated with CHD mortality after adjustment for fish consumption. Using time-varying covariate analysis, significant associations were observed for SU+n3 (HR: 0.74, 95%CI: 0.66, 0.84), but not for SU n3 vs. NSU. In further analyses, the association for SU+n3 persisted in those who did not take other supplements (HR: 0.83, 95%CI: 0.71, 0.96) and those who did (HR: 0.74, 95%CI: 0.60, 0.91). Those who became SU+n3 over time or were consistent SU+n3 vs. consistent NSU had a lower hazard of CHD mortality; no association with CHD was observed in those who stopped using n 3 PUFA-containing supplements. Conclusions: Recent use of n 3 PUFA supplements was associated with a lower hazard of CHD mortality in this general population with low fish consumption. Residual confounding cannot be excluded, but the findings observed may be explained by postulated biological mechanisms and the results were specific to SU+n3.All authors report grants from Cancer Research UK programme grants (G0401527, G1000143) and grants from the Medical Research Council (MRC) programme grants (C864/A8257, C864/A14136) during the study. RHK is supported by a MRC Fellowship (MR/M014827/1)

Observational study to estimate the changes in the effectiveness of bacillus Calmette-Guérin (BCG) vaccination with time since vaccination for preventing tuberculosis in the UK.

Until recently, evidence that protection from the bacillus Calmette-Guérin (BCG) vaccination lasted beyond 10 years was limited. In the past few years, studies in Brazil and the USA (in Native Americans) have suggested that protection from BCG vaccination against tuberculosis (TB) in childhood can last for several decades. The UK's universal school-age BCG vaccination programme was stopped in 2005 and the programme of selective vaccination of high-risk (usually ethnic minority) infants was enhanced. To assess the duration of protection of infant and school-age BCG vaccination against TB in the UK. Two case-control studies of the duration of protection of BCG vaccination were conducted, the first on minority ethnic groups who were eligible for infant BCG vaccination 0-19 years earlier and the second on white subjects eligible for school-age BCG vaccination 10-29 years earlier. TB cases were selected from notifications to the UK national Enhanced Tuberculosis Surveillance system from 2003 to 2012. Population-based control subjects, frequency matched for age, were recruited. BCG vaccination status was established from BCG records, scar reading and BCG history. Information on potential confounders was collected using computer-assisted interviews. Vaccine effectiveness was estimated as a function of time since vaccination, using a case-cohort analysis based on Cox regression. In the infant BCG study, vaccination status was determined using vaccination records as recall was poor and concordance between records and scar reading was limited. A protective effect was seen up to 10 years following infant vaccination [< 5 years since vaccination: vaccine effectiveness (VE) 66%, 95% confidence interval (CI) 17% to 86%; 5-10 years since vaccination: VE 75%, 95% CI 43% to 89%], but there was weak evidence of an effect 10-15 years after vaccination (VE 36%, 95% CI negative to 77%; p = 0.396). The analyses of the protective effect of infant BCG vaccination were adjusted for confounders, including birth cohort and ethnicity. For school-aged BCG vaccination, VE was 51% (95% CI 21% to 69%) 10-15 years after vaccination and 57% (95% CI 33% to 72%) 15-20 years after vaccination, beyond which time protection appeared to wane. Ascertainment of vaccination status was based on self-reported history and scar reading. The difficulty in examining vaccination sites in older women in the high-risk minority ethnic study population and the sparsity of vaccine record data in the later time periods precluded robust assessment of protection from infant BCG vaccination > 10 years after vaccination. Infant BCG vaccination in a population at high risk for TB was shown to provide protection for at least 10 years, whereas in the white population school-age vaccination was shown to provide protection for at least 20 years. This evidence may inform TB vaccination programmes (e.g. the timing of administration of improved TB vaccines, if they become available) and cost-effectiveness studies. Methods to deal with missing record data in the infant study could be explored, including the use of scar reading. The National Institute for Health Research Health Technology Assessment programme. During the conduct of the study, Jonathan Sterne, Ibrahim Abubakar and Laura C Rodrigues received other funding from NIHR; Ibrahim Abubakar and Laura C Rodrigues have also received funding from the Medical Research Council. Punam Mangtani received funding from the Biotechnology and Biological Sciences Research Council

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

BACKGROUND: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. METHODS: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. RESULTS: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. CONCLUSIONS: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. TRIAL REGISTRATION: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR

Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK

BACKGROUND: Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. METHODS: In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260. FINDINGS: Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8-32·7) in March 9 to April 26, 2020, to 16·4% (15·0-17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65-0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94-0·95) of the reduction in in-hospital mortality. INTERPRETATION: The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain. FUNDING: National Institute for Health Research and the Medical Research Council

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves

'HepCheck Dublin': An Intensified Hepatitis C Screening Programme in a Homeless Population Demonstrates the Need for Alternative Models of Care

Background: Hepatitis C virus (HCV) is one of the main causes of chronic liver disease worldwide. Prevalence of HCV in homeless populations ranges from 3.9% to 36.2%. The HepCheck study sought to investigate and establish the characterisation of HCV burden among individuals who attended an intensified screening programme for HCV in homeless services in Dublin, Ireland. Methods: The HepCheck study was conducted as part of a larger European wide initiative called HepCare Europe. The study consisted of three phases; 1) all subjects completed a short survey and were offered a rapid oral HCV test; 2) a convenience sample of HCV positive participants from phase 1 were selected to complete a survey on health and social risk factors and 3) subjects were tracked along the referral pathway to identify whether they were referred to a specialist clinic, attended the specialist clinic, were assessed for cirrhosis by transient elastography (Fibroscan) and were treated for HCV. Results: 597 individuals were offered HCV screening, 73% were male and 63% reported having had a previous HCV screening. We screened 538 (90%) of those offered screening, with 37% testing positive. Among those who tested positive, 112 (56%) were ‘new positives’ and 44% were ‘known positives’. Undiagnosed HCV was prevalent in 19% of the study sample. Active past 30-day drug use was common, along with attendance for drug treatment. Unstable accommodation was the most common barrier to attending specialist appointments and accessing treatment. Depression and anxiety, dental problems and respiratory conditions were common reported health problems. 46 subjects were referred to specialised services and two subjects completed HCV treatment. Conclusions: This study demonstrates that the current hospital-based model of care is inadequate in addressing the specific needs of a homeless population and emphasises the need for a community-based treatment approach. Findings are intended to inform HepCare Europe in their development of a community-based model of care in order to engage with homeless individuals with multiple co-morbidities including substance abuse, who are affected by or infected with HCV