Guidelines for best practice in great ape tourism

Tourism based on the viewing of great apes is increasingly promoted as a means of generating revenue for range states, local communities, and the private sector (e.g. GRASP, 2006 ). This is despite known risks from tourism, including disease transmission, which have caused concern among conservationists and prompted the International Union for Conservation of Nature to publish guidelines on best practices for great ape tourism (Macfi e & Williamson, 2010 ). IUCN is one of the world's most respected authorities on species conservation, and brings together governments, UN agencies, and NGOs to conserve biodiversity and to ensure that any use of natural resources is equitable and ecologically sustainable

Variance reduction in randomised trials by inverse probability weighting using the propensity score.

In individually randomised controlled trials, adjustment for baseline characteristics is often undertaken to increase precision of the treatment effect estimate. This is usually performed using covariate adjustment in outcome regression models. An alternative method of adjustment is to use inverse probability-of-treatment weighting (IPTW), on the basis of estimated propensity scores. We calculate the large-sample marginal variance of IPTW estimators of the mean difference for continuous outcomes, and risk difference, risk ratio or odds ratio for binary outcomes. We show that IPTW adjustment always increases the precision of the treatment effect estimate. For continuous outcomes, we demonstrate that the IPTW estimator has the same large-sample marginal variance as the standard analysis of covariance estimator. However, ignoring the estimation of the propensity score in the calculation of the variance leads to the erroneous conclusion that the IPTW treatment effect estimator has the same variance as an unadjusted estimator; thus, it is important to use a variance estimator that correctly takes into account the estimation of the propensity score. The IPTW approach has particular advantages when estimating risk differences or risk ratios. In this case, non-convergence of covariate-adjusted outcome regression models frequently occurs. Such problems can be circumvented by using the IPTW adjustment approach

Trends for prevalence and incidence of resistant hypertension: population based cohort study in the UK 1995-2015.

Objective To estimate the incidence and prevalence of resistant hypertension among a UK population treated for hypertension from 1995 to 2015.Design Cohort study.Setting Electronic health records from the UK Clinical Practice Research Datalink in primary care.Participants 1 317 290 users of antihypertensive drugs with a diagnosis of hypertension.Main outcome measures Resistant hypertension was defined as concurrent use of three antihypertensive drugs inclusive of a diuretic, uncontrolled hypertension (≥140/90 mm Hg), and adherence to the prescribed drug regimen, or concurrent use of four antihypertensive drugs inclusive of a diuretic and adherence to the prescribed drug regimen. To determine incidence, the numerator was new cases of resistant hypertension and the denominator was person years of those with treated hypertension and at risk of developing resistant hypertension. To determine prevalence, the numerator was total number of cases with resistant hypertension and the denominator was those with treated hypertension. Prevalence and incidence were age standardised to the 2015 hypertensive population.Results The age standardised incidence of resistant hypertension increased from 0.93 cases per 100 person years (95% confidence interval 0.87 to 1.00) in 1996 to a peak level of 2.07 cases per 100 person years (2.03 to 2.12) in 2004. Incidence then decreased to 0.42 cases per 100 person years (0.40 to 0.44) in 2015. Age standardised prevalence increased from 1.75% (95% confidence interval 1.66% to 1.83%) in 1995 to a peak of 7.76% (7.70% to 7.83%) in 2007. Prevalence then plateaued and subsequently declined to 6.46% (6.38% to 6.54%) in 2015. Compared with patients aged 65-69 years, those aged 80 or more years were more likely to have prevalent resistant hypertension throughout the study period.Conclusions Prevalent resistant hypertension has plateaued and decreased in recent years, consistent with a decrease in incidence from 2004 onwards. Despite this, resistant hypertension is common in the UK hypertensive population. Given the importance of hypertension as a modifiable risk factor for cardiovascular disease, reducing uncontrolled hypertension should remain a population health focus

Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why.

Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal nonrandomized studies. A common challenge when using MSMs to analyze observational studies is incomplete confounder data, where a poorly informed analysis method will lead to biased estimates of intervention effects. Despite a number of approaches described in the literature for handling missing data in MSMs, there is little guidance on what works in practice and why. We reviewed existing missing-data methods for MSMs and discussed the plausibility of their underlying assumptions. We also performed realistic simulations to quantify the bias of 5 methods used in practice: complete-case analysis, last observation carried forward, the missingness pattern approach, multiple imputation, and inverse-probability-of-missingness weighting. We considered 3 mechanisms for nonmonotone missing data encountered in research based on electronic health record data. Further illustration of the strengths and limitations of these analysis methods is provided through an application using a cohort of persons with sleep apnea: the research database of the French Observatoire Sommeil de la Fédération de Pneumologie. We recommend careful consideration of 1) the reasons for missingness, 2) whether missingness modifies the existing relationships among observed data, and 3) the scientific context and data source, to inform the choice of the appropriate method(s) for handling partially observed confounders in MSMs

Planning a method for covariate adjustment in individually randomised trials: a practical guide

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. // Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. // Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. // Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely

Implementing high-dimensional propensity score principles to improve confounder adjustment in UK electronic health records.

PURPOSE: Recent evidence from US claims data suggests use of high-dimensional propensity score (hd-PS) methods improve adjustment for confounding in non-randomised studies of interventions. However, it is unclear how best to apply hd-PS principles outside their original setting, given important differences between claims data and electronic health records (EHRs). We aimed to implement the hd-PS in the setting of United Kingdom (UK) EHRs. METHODS: We studied the interaction between clopidogrel and proton pump inhibitors (PPIs). Whilst previous observational studies suggested an interaction (with reduced effect of clopidogrel), case-only, genetic and randomised trial approaches showed no interaction, strongly suggesting the original observational findings were subject to confounding. We derived a cohort of clopidogrel users from the UK Clinical Practice Research Datalink linked with the Myocardial Ischaemia National Audit Project. Analyses estimated the hazard ratio (HR) for myocardial infarction (MI) comparing PPI users with non-users using a Cox model adjusting for confounders. To reflect unique characteristics of UK EHRs, we varied the application of hd-PS principles including the level of grouping within coding systems and adapting the assessment of code recurrence. Results were compared with traditional analyses. RESULTS: Twenty-four thousand four hundred and seventy-one patients took clopidogrel, of whom 9111 were prescribed a PPI. Traditional PS approaches obtained a HR for the association between PPI use and MI of 1.17 (95% CI: 1.00-1.35). Applying hd-PS modifications resulted in estimates closer to the expected null (HR 1.00; 95% CI: 0.78-1.28). CONCLUSIONS: hd-PS provided improved adjustment for confounding compared with other approaches, suggesting hd-PS can be usefully applied in UK EHRs