Right needle, right patient, right time? A national flash-mob audit of thromboprophylaxis in palliative care.

BACKGROUND: The prevention of hospital associated thrombosis in palliative care remains controversial yet many countries recommend the documented risk assessment and where appropriate pharmacological prophylaxis of inpatients with advanced cancer. AIM: To audit adherence to national guidelines which require hospitalised patients to be risk assessed and receive appropriate thromboprophylaxis. DESIGN: A one day "flash-mob" audit across multiple clinical inpatient sites across the United Kingdom. SETTING/PARTICIPANTS: Inpatients receiving palliative care within hospitals, hospices and specialist palliative care units across the United Kingdom. RESULTS: Data were collected from 1125 patients (514 hospital and 611 hospice/specialist palliative care units). Appropriate thromboprophylaxis was observed in 90 % of hospital and 90 % hospice/specialist palliative care units. Documented risk assessment was only found in 79 % and 71 % of patient notes respectively. Pharmacological thromboprophylaxis was contraindicated in 88 % of hospice/specialist palliative care unit patients due to bleeding risk or receiving end-of-life care. Twenty-four percent of patients in hospital had contraindications due to receiving end of life care, bleeding risk and thrombocytopenia. Patients in hospice/specialist palliative care units were of poorer performance status prior to admission with a history of gradual deterioration. Hospitalised patients were more likely to have been admitted following an acute deterioration of previous good performance status. CONCLUSION: Thromboprophylaxis guidelines were followed correctly for the majority of patients. There were considerable differences in the demographics of patients according to place of admission. Patients admitted to hospice/specialist palliative care units were sicker and had more contraindications to prophylaxis than those admitted to hospital. Thromboprophylaxis focused research data conducted in hospices is unlikely to be applicable to the care of palliative care patients admitted acutely to hospital.

Mediation of the total effect of cystic fibrosis-related diabetes on mortality: A UK Cystic Fibrosis Registry cohort study.

AIM: To investigate whether the effect of cystic fibrosis-related diabetes (CFRD) on the composite outcome of mortality or transplant could act through lung function, pulmonary exacerbations and/or nutritional status. METHODS: A retrospective cohort of adult cystic fibrosis (CF) patients who had not been diagnosed with CFRD were identified from the UK Cystic Fibrosis Registry (n = 2750). Rate of death or transplant was compared between patients who did and did not develop CFRD (with insulin use) during follow-up using Poisson regression, separately by sex. Causal mediation methods were used to investigate whether lung function, pulmonary exacerbations and nutritional status lie on the causal pathway between insulin-treated CFRD and mortality/transplant. RESULTS: At all ages, the mortality/transplant rate was higher in both men and women diagnosed with CFRD. Pulmonary exacerbations were the strongest mediator of the effect of CFRD on mortality/transplant, with an estimated 15% [95% CI: 7%, 28%] of the effect at 2 years post-CFRD diagnosis attributed to exacerbations, growing to 24% [95% CI: 9%, 46%] at 4 years post-diagnosis. Neither lung function nor nutritional status were found to be significant mediators of this effect. Estimates were similar but with wider confidence intervals in a cohort that additionally included people with CFRD but not using insulin. CONCLUSION: There is evidence that pulmonary exacerbations mediate the effect of CFRD on mortality but, as they are estimated to mediate less than one-quarter of the total effect, the mechanism through which CFRD influences survival may involve other factors.

Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.

Mediation analysis is a useful tool to illuminate the mechanisms through which an exposure affects an outcome but statistical challenges exist with time-to-event outcomes and longitudinal observational data. Natural direct and indirect effects cannot be identified when there are exposure-induced confounders of the mediator-outcome relationship. Previous measurements of a repeatedly-measured mediator may themselves confound the relationship between the mediator and the outcome. To overcome these obstacles, two recent methods have been proposed, one based on path-specific effects and one based on an additive hazards model and the concept of exposure splitting. We investigate these techniques, focusing on their application to observational datasets. We apply both methods to an analysis of the UK Cystic Fibrosis Registry dataset to identify how much of the relationship between onset of cystic fibrosis-related diabetes and subsequent survival acts through pulmonary function. Statistical properties of the methods are investigated using simulation. Both methods produce unbiased estimates of indirect and direct effects in scenarios consistent with their stated assumptions but, if the data are measured infrequently, estimates may be biased. Findings are used to highlight considerations in the interpretation of the observational data analysis.

Using Negative Control Outcomes and Difference-in-Differences Analysis to Estimate Treatment Effects in an Entirely Treated Cohort: The Effect of Ivacaftor in Cystic Fibrosis.

When an entire cohort of patients receives a treatment, it is difficult to estimate the treatment effect in the treated because there are no directly comparable untreated patients. Attempts can be made to find a suitable control group (e.g., historical controls), but underlying differences between the treated and untreated can result in bias. Here we show how negative control outcomes combined with difference-in-differences analysis can be used to assess bias in treatment effect estimates and obtain unbiased estimates under certain assumptions. Causal diagrams and potential outcomes are used to explain the methods and assumptions. We apply the methods to UK Cystic Fibrosis Registry data to investigate the effect of ivacaftor, introduced in 2012 for a subset of the cystic fibrosis population with a particular genotype, on lung function and annual rate (days/year) of receiving intravenous (IV) antibiotics (i.e., IV days). We consider 2 negative control outcomes: outcomes measured in the pre-ivacaftor period and outcomes among persons ineligible for ivacaftor because of their genotype. Ivacaftor was found to improve lung function in year 1 (an approximately 6.5-percentage-point increase in ppFEV1), was associated with reduced lung function decline (an approximately 0.5-percentage-point decrease in annual ppFEV1 decline, though confidence intervals included 0), and reduced the annual rate of IV days (approximately 60% over 3 years).

Psychosocial determinants of quit motivation in older smokers from deprived backgrounds: a cross-sectional survey.

OBJECTIVES: To identify psychosocial determinants of quit motivation in older deprived smokers. The evidence may be used to optimise smoking cessation interventions for the target population. DESIGN: Cross-sectional survey using online recruitment methods including Facebook-targeted advertising. SETTING: UK, 2019. PARTICIPANTS: Current smokers aged 50 years or older and from a socioeconomically deprived background. MAIN OUTCOME MEASURES: Measures included motivation to stop smoking, smoking history, perceived social support, self-efficacy for quitting, self-exempting beliefs and lung cancer risk perception. Multivariable regression was used to analyse factors associated with quit motivation. RESULTS: Of a total 578 individuals who consented to take part, 278 (48.1%) did not meet the inclusion criteria. Of the 300 eligible participants, most were recruited using Facebook (94.0%), were aged 50-64 years (83.7%) and women (85.7%). Most participants were renting from a housing association (72.0%) and had low education (61.0%). Higher motivation to quit was statistically significantly associated with a higher intensity of previous quit attempts (p=0.03), higher quit confidence (p=0.01), higher smoking self-efficacy (p=0.01), a lower risk-minimising beliefs score (p=0.01) and using traditional nicotine replacement therapy (NRT) when trying to stop smoking or cut down (p<0.001). CONCLUSION: Older smokers from deprived backgrounds face complex barriers to quitting smoking. Interventions are needed to increase self-efficacy for quitting, modify risk-minimising beliefs and target elements of previous quit attempts (ie, the use of NRT) that are associated with motivation to stop smoking.

Marginal structural models for repeated measures where intercept and slope are correlated: An application exploring the benefit of nutritional supplements on weight gain in HIV-infected children initiating antiretroviral therapy.

BACKGROUND: The impact of nutritional supplements on weight gain in HIV-infected children on antiretroviral treatment (ART) remains uncertain. Starting supplements depends upon current weight-for-age or other acute malnutrition indicators, producing time-dependent confounding. However, weight-for-age at ART initiation may affect subsequent weight gain, independent of supplement use. Implications for marginal structural models (MSMs) with inverse probability of treatment weights (IPTW) are unclear. METHODS: In the ARROW trial, non-randomised supplement use and weight-for-age were recorded monthly from ART initiation. The effect of supplements on weight-for-age over the first year was estimated using generalised estimating equation MSMs with IPTW, both with and without interaction terms between baseline weight-for-age and time. Separately, data were simulated assuming no supplement effect, with use depending on current weight-for-age, and weight-for-age trajectory depending on baseline weight-for-age to investigate potential bias associated with different MSM specifications. RESULTS: In simulations, despite correctly specifying IPTW, omitting an interaction in the MSM between baseline weight-for-age and time produced increasingly biased estimates as associations between baseline weight-for-age and subsequent weight trajectory increased. Estimates were unbiased when the interaction between baseline weight-for-age and time was included, even if the data were simulated with no such interaction. In ARROW, without an interaction the estimated effect was +0.09 (95%CI +0.02,+0.16) greater weight-for-age gain per month's supplement use; this reduced to +0.03 (-0.04,+0.10) including the interaction. DISCUSSION: This study highlights a specific situation in which MSM model misspecification can occur and impact the resulting estimate. Since an interaction in the MSM (outcome) model does not bias the estimate of effect if the interaction does not exist, it may be advisable to include such a term when fitting MSMs for repeated measures.

Diffusion of effects of the ASSIST school-based smoking prevention intervention to non-participating family members: a secondary analysis of a randomized controlled trial.

AIMS: To investigate whether effects of the ASSIST (A Stop Smoking In Schools Trial) school-based smoking prevention intervention diffused from students to the people they lived with. DESIGN: Secondary analysis of a cluster-randomized control trial (cRCT). SETTING: England and Wales. PARTICIPANTS: A total of 10 730 students aged 12-13 years in 59 schools assigned using stratified block randomization to the control (29 schools, 5372 students) or intervention (30 schools, 5358 students) condition. INTERVENTION AND COMPARATOR: The ASSIST intervention involves 2 days of off-site training of influential students to encourage their peers not to smoke during a 10-week period. The control group continued with their usual education. MEASUREMENTS: The outcomes were the proportion of students who self-reported living with a smoker and the smoking status of each resident family member/caregiver. Follow-up assessments were immediately after the intervention and at 1 and 2 years post-intervention. FINDINGS: The odds ratio (OR) for living with a smoker in the intervention compared with the control groups was 0.86 [95% confidence interval (CI) = 0.72, 1.03] immediately after the intervention, OR = 0.84 (95% CI = 0.72, 0.97) at a 1-year follow-up and OR = 0.86 (95% CI = 0.75, 0.99) at 2-year follow-up. In a three-tier multi-level model with data from all three follow-ups, student-reported smoking by fathers (OR = 0.90, 95% CI = 0.80, 1.00), brothers (OR = 0.78, 95% CI = 0.67, 0.92) and sisters (OR = 0.80, 95% CI = 0.69, 0.92) was lower in the intervention compared with control group. Subgroup analyses by baseline smoking status suggested that these effects were more consistent with prevention of uptake than prompting cessation. CONCLUSIONS: A Stop Smoking In Schools Trial (ASSIST) school-based smoking prevention intervention may have reduced the prevalence of smoking in people who lived with ASSIST-trained students. This indirect transmission is consistent with the predictions of diffusion of innovations theory which underpins the design of ASSIST.

Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.

In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models. This work is motivated by an investigation of the effects of treatments in cystic fibrosis using the UK Cystic Fibrosis Registry data focussing on two outcomes: lung function (continuous outcome) and annual number of days receiving intravenous antibiotics (count outcome). We identified five features of this data that may affect the performance of the methods: misspecification of the causal null, long-term treatment effects, effect modification by time-varying covariates, misspecification of the direction of causal pathways, and censoring. In simulation studies, under ideal settings, all five methods provide consistent estimates of the treatment effect with little difference between methods. However, all methods performed poorly under some settings, highlighting the importance of using appropriate methods based on the data available. Furthermore, with the count outcome, the issue of non-collapsibility makes comparison between methods delivering marginal and conditional effects difficult. In many situations, we would recommend using more than one of the available methods for analysis, as if the effect estimates are very different, this would indicate potential issues with the analyses.

An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.

Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl's front-door criterion; this approach ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort, and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we demonstrate possible pitfalls that users of the mechanism-based approach may encounter under realistic conditions: namely, when (1) the set of available intermediate variables is incomplete, (2) intermediate variables are affected by two or more of the APC variables (while this feature is not acknowledged in the analysis), and (3) unaccounted confounding is present between intermediate variables and the outcome. Furthermore, we show how the mechanism-based approach can be extended beyond the originally proposed linear and probit regression models to incorporate all generalized linear models, as well as nonlinearities in the predictors, using Monte Carlo simulation. Based on the observed biases resulting from departures from underlying assumptions, we formulate guidelines for the application of the mechanism-based approach (extended or not).

How much do tumor stage and treatment explain socioeconomic inequalities in breast cancer survival? Applying causal mediation analysis to population-based data.

Substantial socioeconomic inequalities in breast cancer survival persist in England, possibly due to more advanced cancer at diagnosis and differential access to treatment. We aim to disentangle the contributions of differential stage at diagnosis and differential treatment to the socioeconomic inequalities in cancer survival. Information on 36,793 women diagnosed with breast cancer during 2000-2007 was routinely collected by an English population-based cancer registry. Deprivation was determined for each patient according to her area of residence at the time of diagnosis. A parametric implementation of the mediation formula using Monte Carlo simulation was used to estimate the proportion of the effect of deprivation on survival mediated by stage and by treatment. One-third (35 % [23-48 %]) of the higher mortality experienced by most deprived patients at 6 months after diagnosis, and one tenth (14 % [-3 to 31 %]) at 5 years, was mediated by adverse stage distribution. We initially found no evidence of mediation via differential surgical treatment. However, sensitivity analyses testing some of our study limitations showed in particular that up to thirty per cent of the higher mortality in most deprived patients could be mediated by differential surgical treatment. This study illustrates the importance of using causal inference methods with routine medical data and the need for testing key assumptions through sensitivity analyses. Our results suggest that, although effort for earlier diagnosis is important, this would reduce the cancer survival inequalities only by a third. Because of data limitations, role of differential surgical treatment may have been under-estimated.

A comparison of methods to adjust for continuous covariates in the analysis of randomised trials.

BACKGROUND: Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy. METHODS: We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets. RESULTS: Methods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall. CONCLUSIONS: For the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.

Which factors account for the ethnic inequalities in stage at diagnosis and cervical cancer survival in New Zealand?

OBJECTIVE: There are substantial ethnic inequalities in stage at diagnosis and cervical cancer survival in New Zealand. We assessed what proportions of these differences were due to screening history (for the analyses of late stage diagnosis), stage at diagnosis (for the analyses of survival), comorbid conditions (for the analyses of survival), and travel time to the nearest General Practitioner and cancer centre. METHODS: The study involved 1594 cervical cancer cases registered during 1994-2005. We used G-computation to assess the validity of the estimates obtained by standard logistic regression methods. RESULTS: Maori women had a higher risk of late stage diagnosis compared with 'Other' (mainly European) women (odds ratio (OR) = 2.71; 95% confidence interval 1.98, 3.72); this decreased only slightly (OR 2.39; 1.72, 3.30) after adjustment for screening history, and travel time to the nearest General Practitioner and cancer centre. In contrast, the (non-significantly) elevated risk in Pacific women (1.39; 0.76, 2.54) disappeared almost completely when adjusted for the same factors (1.06; 0.57, 1.96). The hazard ratio of mortality for cervical cancer for Maori women was 2.10 (1.61, 2.73) and decreased to 1.45 (1.10, 1.92) after adjustment for stage at diagnosis, comorbid conditions, and travel time to the nearest General Practitioner and cancer centre; the corresponding estimates for Pacific women were 1.96 (1.23, 3.13) and 1.55 (0.93, 2.57). The G-computation analyses gave similar findings. CONCLUSIONS: The excess relative risk of late stage diagnosis in Maori women remains largely unexplained, while more than half of the excess relative risk of mortality in Maori and Pacific women is explained by differences in stage at diagnosis and comorbid conditions.

Using causal diagrams to guide analysis in missing data problems.

Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR)) and the circumstances in which causal effects can be estimated without bias by analysing only the subjects with complete data. In doing so, we formally extend the back-door criterion of Pearl and others for use in incomplete data examples. These ideas are illustrated with an example drawn from an occupational cohort study of the effect of cosmic radiation on skin cancer incidence.

Response and non-response to a quality-of-life question on sexual life: a case study of the simple mean imputation method.

We investigated the non-response rates to the question "I am satisfied with my sex life" in the Functional Assessment of Cancer Therapy-General questionnaire in Chinese (n = 769), Malay (n = 41) and Indian (n = 33) patients in Singapore, a multi-ethnic society whose residents are said to have a conservative sexual attitude. Non-response rates to the question were 44%, 22% and 24% in the three groups respectively. The rates were much higher than that reported previously in a US study (7%) and used in the associated simulation study of the simple mean imputation method. We further examined the Chinese respondents in detail. The odds of non-response and the scores among the responders were associated with several demographic and clinical characteristics. Using the checklist proposed by Fayers et al. [Stat Med 1998; 17: 679-696] to assess the data patterns, we found that the application of the simple mean imputation is questionable. We employed an alternative (multiple) imputation procedure that took into account covariates that predicted the odds of non-response and the observed response scores. We compared the analytic results based on different approaches to handling missing values, and found that analysis based on the simple mean imputation gave results similar to that based on multiply imputed data even in this quite extreme example.