BackgroundThe results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated.MethodsWe develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers.ResultsThe validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported.ConclusionThe algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

To inform health-care decision-making, treatments are often compared by synthesizing results from a number of randomized controlled trials. The meta-analysis may not only be focused on a particular pairwise comparison, but can also include multiple treatment comparisons by means of network meta-analysis. For time-to-event outcomes such as survival, pooling is typically based on the hazard ratio (HR). The proportional hazards assumption that underlies current approaches of evidence synthesis is not only often implausible, but can also have a huge impact on decisions based on differences in expected outcomes, such as cost-effectiveness analysis. The application of a constant HR implies the assumption that the treatment only has an effect on one characteristic of the survival distribution, while commonly used survival distributions, like the Weibull distribution, have both a shape and a scale parameter. Instead of using constant HRs, this paper proposes meta-analysis of treatment effects based on the shape and scale parameters of parametric survival curves. The model for meta-analysis is extended for network meta-analysis and illustrated with an example. Copyright © 2011 John Wiley & Sons, Ltd.

Objective To characterise failure of antibiotic treatment in primary care in the United Kingdom in four common infection classes from 1991 to 2012.Design Longitudinal analysis of failure rates for first line antibiotic monotherapies associated with diagnoses for upper and lower respiratory tract infections, skin and soft tissue infections, and acute otitis media. Setting Routine primary care data from the UK Clinical Practice Research Datalink (CPRD).Main outcome measures Adjusted rates of treatment failure defined by standardised criteria and indexed to year 1 (1991=100).Results From 58 million antibiotic prescriptions in CPRD, we analysed 10 967 607 monotherapy episodes for the four indications: 4 236 574 (38.6%) for upper respiratory tract infections; 3 148 947 (28.7%) for lower respiratory tract infections; 2 568 230 (23.4%) for skin and soft tissue infections; and 1 013 856 (9.2%) for acute otitis media. In 1991, the overall failure rate was 13.9% (12.0% for upper respiratory tract infections; 16.9% for lower respiratory tract infections; 12.8% for skin and soft tissue infections; and 13.9% for acute otitis media). By 2012, the overall failure rate was 15.4%, representing an increase of 12% compared with 1991 (adjusted value indexed to first year (1991) 112, 95% confidence interval 112 to 113). The highest rate was seen in lower respiratory tract infections (135, 134 to 136). While failure rates were below 20% for most commonly prescribed antibiotics (amoxicillin, phenoxymethylpenicillin (penicillin-V), and flucloxacillin), notable increases were seen for trimethoprim in the treatment of upper respiratory tract infections (from 29.2% in 1991-95 to 70.1% in 2008-12) and for ciprofloxacin (from 22.3% in 1991-95 to 30.8% in 2008-12) and cefalexin (from 22.0% in 1991-95 to 30.8% in 2008-12) in the treatment of lower respiratory tract infections. Failure rates for broad spectrum penicillins, macrolides, and flucloxacillin remained largely stable.Conclusions From 1991 to 2012, more than one in 10 first line antibiotic monotherapies for the selected infections were associated with treatment failure. Overall failure rates increased by 12% over this period, with most of the increase occurring in more recent years, when antibiotic prescribing in primary care plateaued and then increased.

BackgroundStandard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments.ObjectiveThe aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments.MethodsStandard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation.ResultsA close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO.ConclusionsStandard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed.Electronic supplementary materialThe online version of this article (10.1007/s40273-019-00806-4) contains supplementary material, which is available to authorized users.

In this article, the optimal selection and allocation of time points in repeated measures experiments is considered. D-optimal cohort designs are computed numerically for the first- and second-degree polynomial models with random intercept, random slope, and first-order autoregressive serial correlations. Because the optimal designs are locally optimal, it is proposed to use a maximin criterion. It is shown that, for a large class of symmetric designs, the smallest relative efficiency over the model parameter space is substantial.