Competing risks analyses: objectives and approaches

Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoint

Lovastatin for adult patients with dengue: protocol for a randomised controlled trial.

BACKGROUND: Dengue is the most important vector-borne viral infection of man, with approximately 2 billion people living in areas at risk. Infection results in a range of manifestations from asymptomatic infection through to life-threatening shock and haemorrhage. One of the hallmarks of severe dengue is vascular endothelial disruption. There is currently no specific therapy and clinical management is limited to supportive care. Statins are a class of drug initially developed for lipid lowering. There has been considerable recent interest in their effects beyond lipid lowering. These include anti-inflammatory effects at the endothelium. In addition, it is possible that lovastatin may have an anti-viral effect against dengue. Observational data suggest that the use of statins may improve outcomes for such conditions as sepsis and pneumonia. This paper describes the protocol for a randomised controlled trial investigating a short course of lovastatin therapy in adult patients with dengue. METHODS/DESIGN: A randomised, double-blind, placebo-controlled trial will investigate the effects of lovastatin therapy in the treatment of dengue. The trial will be conducted in two phases with an escalation of dose between phases if an interim safety review is satisfactory. This is an exploratory study focusing on safety and there are no data on which to base a sample size calculation. A target sample size of 300 patients in the second phase, enrolled over two dengue seasons, was chosen based on clinical judgement and feasibility considerations. In a previous randomised trial in dengue, about 10% and 30% of patients experienced at least one serious adverse event or adverse event, respectively. With 300 patients, we will have 80% power to detect an increase of 12% (from 10% to 22%) or 16% (from 30% to 46%) in the frequency of adverse events. Furthermore, this sample size ensures some power to explore the efficacy of statins. DISCUSSION: The development of a dengue therapeutic that can attenuate disease would be an enormous advance in global health. The favourable effects of statins on the endothelium, their good safety profile and their low cost make lovastatin an attractive therapeutic candidate. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ISRCTN03147572

Principal Stratum Strategy: Potential Role in Drug Development

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called {\it intercurrent events} in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions

Estimation methods for estimands using the treatment policy strategy; a simulation study based on the PIONEER 1 Trial

Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting due to data sparsity. This paper introduces likelihood-based versions of these approaches, investigating and comparing their statistical properties to the existing retrieved dropout approaches, simpler analysis models and reference-based multiple imputation. We use a simulation based upon the data from the PIONEER 1 Phase III clinical trial in Type II diabetics to present complex and relevant estimation challenges. The likelihood-based methods display similar statistical properties to their multiple imputation equivalents, but all retrieved dropout approaches suffer from high variance. Retrieved dropout approaches appear less biased than reference-based approaches, resulting in a bias-variance trade-off, but we conclude that the large degree of variance inflation is often more problematic than the bias. Therefore, only the simpler retrieved dropout models appear appropriate as a primary analysis in a clinical trial, and only where it is believed most data following intercurrent events will be observed. The jump-to-reference approach may represent a more promising estimation approach for symptomatic treatments due to its relatively high power and ability to fit in the presence of much missing data, despite its strong assumptions and tendency towards conservative bias. More research is needed to further develop how to estimate the treatment effect for a treatment policy strategy

Standard and reference-based conditional mean imputation

Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rules for pooling results across multiple imputed data sets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing-at-random assumption as well as for reference-based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error

Prospective evaluation of GeneXpert for the diagnosis of HIV- negative pediatric TB cases

Background The GeneXpertMTB/RIF (Xpert) assay is now recommended by WHO for diagnosis of tuberculosis (TB) in children but evaluation data is limited. Methods One hundred and fifty consecutive HIV negative children (<15 years of age) presenting with suspected TB were enrolled at a TB referral hospital in Ho Chi Minh City, Vietnam. 302 samples including sputum (n = 79), gastric fluid (n = 215), CSF (n = 3), pleural fluid (n = 4) and cervical lymphadenopathic pus (n = 1) were tested by smear, automated liquid culture (Bactec MGIT) and Xpert. Patients were classified retrospectively using the standardised case definition into confirmed, probable, possible, TB unlikely or not TB categories. Test accuracy was evaluated against 2 gold standards: [1] clinical (confirmed, probable and possible TB) and [2] ‘confirmed TB’ alone. Results The median age of participants was 18 months [IQR 5–170]. When test results were aggregated by patient, the sensitivity of smear, Xpert and MGIT against clinical diagnosis as the gold standard were 9.2% (n = 12/131) [95%CI 4.2; 14.1], 20.6% (n = 27/131) [95%CI 13.7; 27.5] and 29.0% (n = 38/131) [21.2;36.8], respectively. Specificity 100% (n = 19/19), 94.7% (n = 18/19), 94.7% (n = 18/19), respectively. Xpert was more sensitive than smear (P = <0.001) and less sensitive than MGIT (P = 0.002). Conclusions The systematic use of Xpert will increase early TB case confirmation in children and represents a major advance but sensitivity of all tests remains unacceptably low. Improved rapid diagnostic tests and algorithm approaches for pediatric TB are still an urgent research priority

The antimicrobial resistance patterns and associated determinants in Streptococcus suis isolated from humans in southern Vietnam, 1997-2008

<p>Abstract</p> <p>Background</p> <p><it>Streptococcus suis </it>is an emerging zoonotic pathogen and is the leading cause of bacterial meningitis in adults in Vietnam. Systematic data on the antimicrobial susceptibility profiles of <it>S. suis </it>strains isolated from human cases are lacking. We studied antimicrobial resistance and associated resistance determinants in <it>S. suis </it>isolated from patients with meningitis in southern Vietnam.</p> <p>Methods</p> <p><it>S. suis </it>strains isolated between 1997 and 2008 were investigated for their susceptibility to six antimicrobial agents. Strains were screened for the presence and expression of tetracycline and erythromycin resistance determinants and the association of <it>tet</it>(M) genes with <it>Tn</it>916- like transposons. The localization of tetracycline resistance gene <it>tet</it>(L) was determined by pulse field gel electrophoresis and Southern blotting.</p> <p>Results</p> <p>We observed a significant increase in resistance to tetracycline and chloramphenicol, which was concurrent with an increase in multi-drug resistance. In tetracycline resistance strains, we identified <it>tet</it>(M), <it>tet</it>(O), <it>tet</it>(W) and <it>tet</it>(L) and confirmed their expression. All <it>tet</it>(M) genes were associated with a <it>Tn</it>916-like transposon. The co-expression of <it>tet</it>(L) and other tetracycline resistance gene(s) encoding for ribosomal protection protein(s) was only detected in strains with a minimum inhibitory concentration (MIC) of tetracycline of ≥ 64 mg/L</p> <p>Conclusions</p> <p>We demonstrated that multi-drug resistance in <it>S. suis </it>causing disease in humans in southern Vietnam has increased over the 11-year period studied. We report the presence and expression of <it>tet</it>(L) in <it>S. suis </it>strains and our data suggest that co-expression of multiple genes encoding distinct mechanism is required for an MIC ≥ 64 mg/L to tetracycline.</p