Information-anchored sensitivity analysis: theory and application

Analysis of longitudinal randomised controlled trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption about post-deviation behaviour in order to perform our primary analysis and estimate the treatment effect. In such settings, it is now widely recognised that we should follow this with sensitivity analyses to explore the robustness of our inferences to alternative assumptions about post-deviation behaviour. Although there has been a lot of work on how to conduct such sensitivity analyses, little attention has been given to the appropriate loss of information due to missing data within sensitivity analysis. We argue more attention needs to be given to this issue, showing it is quite possible for sensitivity analysis to decrease and increase the information about the treatment effect. To address this critical issue, we introduce the concept of information-anchored sensitivity analysis. By this we mean sensitivity analysis in which the proportion of information about the treatment estimate lost due to missing data is the same as the proportion of information about the treatment estimate lost due to missing data in the primary analysis. We argue this forms a transparent, practical starting point for interpretation of sensitivity analysis. We then derive results showing that, for longitudinal continuous data, a broad class of controlled and reference-based sensitivity analyses performed by multiple imputation are information-anchored. We illustrate the theory with simulations and an analysis of a peer review trial, then discuss our work in the context of other recent work in this area. Our results give a theoretical basis for the use of controlled multiple imputation procedures for sensitivity analysis

Standard PK/PD concepts can be applied to determine a dosage regimen for a macrolide: the case of tulathromycin in the calf

The pharmacokinetic (PK) profile of tulathromycin, administered to calves subcutaneously at the dosage of 2.5 mg/kg, was established in serum, inflamed (exudate), and noninflamed (transudate) fluids in a tissue cage model. The PK profile of tulathromycin was also established in pneumonic calves. For Mannheimia haemolytica and Pasteurella multocida, tulathromycin minimum inhibitory concentrations (MIC) were approximately 50 times lower in calf serum than in Mueller–Hinton broth. The breakpoint value of the PK/pharmacodynamic (PD) index (AUC(0–24 h)/MIC) to achieve a bactericidal effect was estimated from in vitro time‐kill studies to be approximately 24 h for M. haemolytica and P. multocida. A population model was developed from healthy and pneumonic calves and, using Monte Carlo simulations, PK/PD cutoffs required for the development of antimicrobial susceptibility testing (AST) were determined. The population distributions of tulathromycin doses were established by Monte Carlo computation (MCC). The computation predicted a target attainment rate (TAR) for a tulathromycin dosage of 2.5 mg/kg of 66% for M. haemolytica and 87% for P. multocida. The findings indicate that free tulathromycin concentrations in serum suffice to explain the efficacy of single‐dose tulathromycin in clinical use, and that a dosage regimen can be computed for tulathromycin using classical PK/PD concepts

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: a practical guide

Missing data due to loss to follow‐up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ ‐ and reference‐based MI procedures. In δ ‐based imputation, an offset term, δ , is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference‐based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ ‐based sensitivity analysis. We also review the debate on variance estimation within reference‐based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference

Longitudinal dose and type of immunosuppression in a national cohort of Australian liver, heart, and lung transplant recipients, 1984-2006

Unconfounded comparative data on the type and dose of immunosuppressive agents among solid organ transplant recipients are sparse, as are data on longitudinal immunosuppressive therapy since transplantation. We addressed this issue in a population-based cohort of Australian liver (n = 1895), heart (n = 1220), and lung (n = 1059) transplant recipients, 1984-2006. Data on immunosuppressive therapy were retrospectively collected at discharge, three months, and one, five, 10, and 15 yr after first transplant. We computed unadjusted and adjusted estimates for the association between the type and dose of immunosuppressive therapy and organ type. After adjustment for confounders, use of induction antibody and maintenance corticosteroids was more common in heart and lung compared to liver recipients (p < 0.001), and antibody therapy for rejection more common in liver recipients (p < 0.001). Liver recipients were more likely to receive calcineurin inhibitor monotherapy, with or without corticosteroids, compared to heart and lung recipients (p < 0.001). Liver recipients consistently received lower doses of azathioprine than heart and lung recipients (p < 0.001). These differences in immunosuppression may partly explain variations in immunosuppression-related morbidity by transplanted organ, for example, malignancy risk. Longitudinal changes in the type and the dose of immunosuppressive therapy over time since transplantation also demonstrate the need for time-dependent data in observational research

Rosuvastatin for primary prevention in patients with European systematic coronary risk evaluation risk ≥5% or Framingham risk >20%: post hoc analyses of the JUPITER trial requested by European health authorities

Aims: On the basis of the JUPITER trial, European health authorities recently approved the use of rosuvastatin to reduce first major cardiovascular events among ‘high' global risk primary prevention patients defined either by Framingham risk score >20% or European systematic coronary risk evaluation (SCORE) ≥5%. However, as these are post hoc analyses, data describing these subgroups have not previously been available to the clinical community. Methods and results: We randomized 17 802 apparently healthy men aged ≥50 and women ≥60 with low-density lipoprotein cholesterol (LDL-C) 20% or SCORE risk ≥5%. During 1.8-year median follow-up (maximum 5 years) of patients with Framingham risk >20%, the rate of myocardial infarction/stroke/cardiovascular death was 9.4 and 18.2 per 1000 person-years in rosuvastatin and placebo-allocated patients, respectively [hazard ratio (HR): 0.50, 95% confidence interval (CI): 0.27–0.93, P = 0.028]. Among patients with SCORE risk ≥5%, the corresponding rates were 6.9 and 12.0 using a model extrapolating risk for age ≥65 years (HR: 0.57, 95% CI: 0.43–0.78, P = 0.0003) and rates were 5.9 and 12.7 when risk for age was capped at 65 years (HR: 0.47, 95% CI: 0.32–0.68, P 20% or SCORE risk ≥5%), but LDL-C levels not requiring pharmacologic treatment, rosuvastatin 20 mg significantly reduced major cardiovascular events. ClinicalTrial.gov Identifier: NCT0023968

Through the looking glass: understanding non-inferiority

Non-inferiority trials test whether a new product is not unacceptably worse than a product already in use. This paper introduces concepts related to non-inferiority, and discusses the regulatory views of both the European Medicines Agency and the United States Food and Drug Administration

Patient reported outcome measures for allergy and asthma in children:PROMS for allergy and asthma in children

There is increasing recognition of the importance of patient's perceptions of disease and their assessments of heathcare processes. Patient‐reported outcome measures (PROMs) are therefore now regarded as at least as important as the traditional objective measures of disease. For minors, parental and, except in the very young and severally cognitively impaired, the child's perspectives are important because they provide unique and complementary information. In this review, we summarize the evidence on PROMs for allergy and asthma for use in children. Overall, there are fewer PROMs available for use in children than in adults. We were able to identify some validated pediatric PROMs that have been developed for use in atopic eczema/dermatitis, food allergy, allergic rhinitis/rhinoconjunctivitis, and asthma. There is very limited evidence on deploying these instruments out with research settings. There is therefore a pressing need to report on the experiences of using PROMs for allergy and asthma in routine clinical care. In particular, there is a need to understand how acceptable these are to children/carers, whether they can be incorporated into routine clinical assessments and if they are responsive to changes in treatment made in routine clinical practice

Combining the in vivo comet and micronucleus assays: a practical approach to genotoxicity testing and data interpretation

Despite regulatory directives requiring the reduction of animal use in safety testing, recent modifications to genotoxicity testing guidelines now propose the use of two in vivo genotoxicity assays as a follow-up to an in vitro positive (International Conference on Harmonization Consensus Draft Guidance S2[R1] released March, 2008). To address both goals, the in vivo comet and micronucleus (MN) assays can be successfully combined into one informative study. Combining these two assays with such differences in sensitivity, endpoints measured and the type of data generated significantly improves upon the current standard capabilities for detecting genotoxicity without requiring additional animals. But to take full advantage of the benefits of incorporating the comet assay in safety testing, these same differences must be recognized and considered. Developed from over 15 years experience using the in vivo comet and MN assays in genotoxicity testing of chemicals and pharmaceuticals, this paper presents guidelines for the appropriate experimental design, dose selection and data interpretation for combined in vivo comet/MN assay studies. To illustrate the approach, data from combined assay studies are presented and discussed

Missing data in trial-based cost-effectiveness analysis: An incomplete journey.

Cost-effectiveness analyses (CEA) conducted alongside randomised trials provide key evidence for informing healthcare decision making, but missing data pose substantive challenges. Recently, there have been a number of developments in methods and guidelines addressing missing data in trials. However, it is unclear whether these developments have permeated CEA practice. This paper critically reviews the extent of and methods used to address missing data in recently published trial-based CEA. Issues of the Health Technology Assessment journal from 2013 to 2015 were searched. Fifty-two eligible studies were identified. Missing data were very common; the median proportion of trial participants with complete cost-effectiveness data was 63% (interquartile range: 47%-81%). The most common approach for the primary analysis was to restrict analysis to those with complete data (43%), followed by multiple imputation (30%). Half of the studies conducted some sort of sensitivity analyses, but only 2 (4%) considered possible departures from the missing-at-random assumption. Further improvements are needed to address missing data in cost-effectiveness analyses conducted alongside randomised trials. These should focus on limiting the extent of missing data, choosing an appropriate method for the primary analysis that is valid under contextually plausible assumptions, and conducting sensitivity analyses to departures from the missing-at-random assumption