Discussion on: Instrumented difference-in-differences, by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy and Dylan S. Small

I discuss the assumptions needed for identification of average treatment effects and local average treatment effects in instrumented difference-in-differences (IDID), and the possible trade-offs between assumptions of standard IV and those needed for the new proposal IDID, in one- and two-sample settings. I also discuss the interpretation of the estimands identified under monotonicity. I conclude by suggesting possible extensions to the estimation method, by outlining a strategy to use data-adaptive estimation of the nuisance parameters, based on recent developments

Methods for estimating complier average causal effects for cost-effectiveness analysis.

In randomized controlled trials with treatment non-compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to cost-effectiveness analyses, where methods need to recognize the correlation between cost and health outcomes. We propose a Bayesian full likelihood approach, which jointly models the effects of random assignment on treatment received and the outcomes, and a three-stage least squares method, which acknowledges the correlation between the end points and the endogeneity of the treatment received. This investigation is motivated by the REFLUX study, which exemplifies the setting where compliance differs between the randomized controlled trial and routine practice. A simulation is used to compare the methods' performance. We find that failure to model the correlation between the outcomes and treatment received correctly can result in poor confidence interval coverage and biased estimates. By contrast, Bayesian full likelihood and three-stage least squares methods provide unbiased estimates with good coverage

Multiple imputation methods for bivariate outcomes in cluster randomised trials.

Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design. Across all the missing data mechanisms considered, the multiple imputation methods provided estimators with negligible bias, while complete case analysis resulted in biased treatment effect estimates in scenarios where the randomised treatment arm was associated with missingness. Confidence interval coverage was generally in excess of nominal levels (up to 99.8%) following fixed-effects multiple imputation and too low following single-level multiple imputation. Multilevel multiple imputation led to coverage levels of approximately 95% throughout. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Estimating cluster-level local average treatment effects in cluster randomised trials with non-adherence

Non-adherence to assigned treatment is a common issue in cluster randomised trials. In these settings, the efficacy estimand may also be of interest. Many methodological contributions in recent years have advocated using instrumental variables to identify and estimate the local average treatment effect. However, the clustered nature of randomisation in cluster randomised trials adds to the complexity of such analyses. In this paper, we show that the local average treatment effect can be estimated via two-stage least squares regression using cluster-level summaries of the outcome and treatment received under certain assumptions. We propose the use of baseline variables to adjust the cluster-level summaries before performing two-stage least squares in order to improve efficiency. Implementation needs to account for the reduced sample size, as well as the possible heteroscedasticity, to obtain valid inferences. Simulations are used to assess the performance of two-stage least squares of cluster-level summaries under cluster-level or individual-level non-adherence, with and without weighting and robust standard errors. The impact of adjusting for baseline covariates and of appropriate degrees of freedom correction for inference is also explored. The methods are then illustrated by re-analysing a cluster randomised trial carried out in a specific UK primary care setting. Two-stage least squares estimation using cluster-level summaries provides estimates with small to negligible bias and coverage close to nominal level, provided the appropriate small sample degrees of freedom correction and robust standard errors are used for inference

Testing effectiveness of the revised Cape Town modified early warning and SBAR systems: a pilot pragmatic parallel group randomised controlled trial

Abstract Background Nurses’ recognition of clinical deterioration is crucial for patient survival. Evidence for the effectiveness of modified early warning scores (MEWS) is derived from large observation studies in developed countries. Methods We tested the effectiveness of the paper-based Cape Town (CT) MEWS vital signs observation chart and situation-background-assessment-recommendation (SBAR) communication guide. Outcomes were: proportion of appropriate responses to deterioration, differences in recording of clinical parameters and serious adverse events (SAEs) in intervention and control trial arms. Public teaching hospitals for adult patients in Cape Town were randomised to implementation of the CT MEWS/SBAR guide or usual care (observation chart without track-and-trigger information) for 31 days on general medical and surgical wards. Nurses in intervention wards received training, as they had no prior knowledge of early warning systems. Identification and reporting of patient deterioration in intervention and control wards were compared. In the intervention arm, 24 day-shift and 23 night-shift nurses received training. Clinical records were reviewed retrospectively at trial end. Only records of patients who had given signed consent were reviewed. Results We recruited two of six CT general hospitals. We consented 363 patients and analysed 292 (80.4%) patient records (n = 150, 51.4% intervention, n = 142, 48.6% control arm). Assistance was summoned for fewer patients with abnormal vital signs in the intervention arm (2/45, 4.4% versus (vs) 11/81, 13.6%, OR 0.29 (0.06–1.39)), particularly low systolic blood pressure. There was a significant difference in recording between trial arms for parameters listed on the MEWS chart but omitted from the standard observations chart: oxygen saturation, level of consciousness, pallor/cyanosis, pain, sweating, wound oozing, pedal pulses, glucose concentration, haemoglobin concentration, and “looks unwell”. SBAR was used twice. There was no statistically significant difference in SAEs (5/150, 3.3% vs 3/143, 2.1% P = 0.72, OR 1.61 (0.38–6.86)). Conclusions The revised CT MEWS observations chart improved recording of certain parameters, but did not improve nurses’ ability to identify early signs of clinical deterioration and to summon assistance. Recruitment of only two hospitals and exclusion of patients too ill to consent limits generalisation of results. Further work is needed on educational preparation for the CT MEWS/SBAR and its impact on nurses’ reporting behaviour. Trial registration Pan African Clinical Trials Registry, PACTR201406000838118. Registered on 2 June 2014, www.pactr.org

Correction to: Evaluating the impact of Marie Stopes International’s digital family planning counselling application on the uptake of long-acting and permanent methods of contraception in Vietnam and Ethiopia: a study protocol for a multi-country cluster randomised controlled trial.

Following publication of the original article [1], the authors requested a correction to be made, indicating L. Bates as the first author only. There is no joint first authorship

Nurse-Led Medicines' Monitoring for Patients with Dementia in Care Homes: A Pragmatic Cohort Stepped Wedge Cluster Randomised Trial

People with dementia are susceptible to adverse drug reactions (ADRs). However, they are not always closely monitored for potential problems relating to their medicines: structured nurse-led ADR Profiles have the potential to address this care gap. We aimed to assess the number and nature of clinical problems identified and addressed and changes in prescribing following introduction of nurse-led medicines' monitoring.Pragmatic cohort stepped-wedge cluster Randomised Controlled Trial (RCT) of structured nurse-led medicines' monitoring versus usual care.Five UK private sector care homes.41 service users, taking at least one antipsychotic, antidepressant or anti-epileptic medicine.Nurses completed the West Wales ADR (WWADR) Profile for Mental Health Medicines with each participant according to trial step.Problems addressed and changes in medicines prescribed.Information was collected from participants' notes before randomisation and after each of five monthly trial steps. The impact of the Profile on problems found, actions taken and reduction in mental health medicines was explored in multivariate analyses, accounting for data collection step and site.Five of 10 sites and 43 of 49 service users approached participated. Profile administration increased the number of problems addressed from a mean of 6.02 [SD 2.92] to 9.86 [4.48], effect size 3.84, 95% CI 2.57-4.11, P <0.001. For example, pain was more likely to be treated (adjusted Odds Ratio [aOR] 3.84, 1.78-8.30), and more patients attended dentists and opticians (aOR 52.76 [11.80-235.90] and 5.12 [1.45-18.03] respectively). Profile use was associated with reduction in mental health medicines (aOR 4.45, 1.15-17.22).The WWADR Profile for Mental Health Medicines can improve the quality and safety of care, and warrants further investigation as a strategy to mitigate the known adverse effects of prescribed medicines.ISRCTN 48133332