Analysing randomised controlled trials with missing data : Choice of approach affects conclusions

Copyright © 2012 Elsevier Inc. All rights reserved. PMID: 22265924 [PubMed - indexed for MEDLINE]Peer reviewedPostprin

Using the literature to quantify the learning curve: a case study

Objective: To assess whether a literature review of a technology can allow a learning curve to be quantified. Methods: The literature for fibreoptic intubation was searched for studies reporting information relevant to the learning curve. The Cochrane Librar y, Medline, Embase and Science Citation index were searched. Studies that reported a procedure time were included. Data were abstracted on the three features of learning: initial level, rate of learning and asymptote level. Random effect meta-analysis was performed. Results: Only 21 studies gave explicit information concerning the previous experience of the operator(s). There were 32 different definitions of procedure time. From 4 studies of fibreoptic nasotracheal intubation, the mean starting level and time for the 10th procedure (95% confidence interval) was estimated to be 133s (113, 153) and 71s (62, 79) respectively. Conclusions: The review approach allowed learning to be quantified for our example technology. Poor and insufficient reporting constrained formal statistical estimation. Standardised reporting of non-drug techniques with adequate learning curve details is needed to inform trial design and costeffectiveness analysis

Methodology and reporting characteristics of studies using interrupted time series design in healthcare

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. The Health Services Research Unit, University of Aberdeen, is core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates.Peer reviewedPublisher PD

Determining information for inclusion in a decision-support intervention for clinical trial participation : A modified Delphi approach

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Developing the Intervention and Outcome Components of a Proposed Randomised Controlled trial (RCT) of a National Screening Programme for Open Angle Glaucoma (OAG) : Medical Research Council funded trial platform study (G0701759): Study protocol

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The use of segmented regression in analysing interrupted time series studies : an example in pre-hospital ambulance care

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Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Systematic review of antimicrobial drug prescribing in hospitals.

Prudent antibiotic prescribing to hospital inpatients has the potential to reduce the incidences of antimicrobial resistance and healthcare-associated infection. We reviewed the literature from January 1980 to November 2003 to identify rigorous evaluations of interventions to improve hospital antibiotic prescribing. We identified 66 studies with interpretable data of which 16 reported 20 microbiological outcomes: Gram negative resistant bacteria (GNRB), 10 studies; Clostridium difficile associated diarrhoea (CDAD), 5 studies; vancomycin resistant enterococci (VRE), 3 studies and methicillin resistant Staphylococcus aureus (MRSA), 2 studies. Four studies provide good evidence that the intervention changed microbial outcomes with low risk of alternative explanations, eight studies provide less convincing evidence and four studies were negative. The strongest and most consistent evidence was for CDAD but we were able to analyse only the immediate impact of interventions because of nonstandardised durations of follow up. The ability to compare results of studies could be substantially improved by standardising methodology and reporting

Recruitment to publicly funded trials - are surgical trials really different?

Good recruitment is integral to the conduct of a high-quality randomised controlled trial. It has been suggested that recruitment is particularly difficult for evaluations of surgical interventions, a field in which there is a dearth of evidence from randomised comparisons. While there is anecdotal speculation to support the inference that recruitment to surgical trials is more challenging than for medical trials we are unaware of any formal assessment of this. In this paper, we compare recruitment to surgical and medical trials using a cohort of publicly funded trials. Data: Overall recruitment to trials was assessed using of a cohort of publicly funded trials (n = 114). Comparisons were made by using the Recruitment Index, a simple measure of recruitment activity for multicentre randomised controlled trials. Recruitment at the centre level was also investigated through three example surgical trials. Results: The Recruitment Index was found to be higher, though not statistically significantly, in the surgical group (n = 18, median = 38.0 IQR (10.7, 77.4)) versus (n = 81, median = 34.8 IQR (11.7, 98.0)) days per recruit for the medical group (median difference 1.7 (− 19.2, 25.1); p = 0.828). For the trials where the comparison was between a surgical and a medical intervention, the Recruitment Index was substantially higher (n = 6, 68.3 (23.5, 294.8)) versus (n = 93, 34.6 (11.7, 90.0); median difference 25.9 (− 35.5, 221.8); p = 0.291) for the other trials. Conclusions: There was no clear evidence that surgical trials differ from medical trials in terms of recruitment activity. There was, however, support for the inference that medical versus surgical trials are more difficult to recruit to. Formal exploration of the recruitment data through a modelling approach may go some way to tease out where important differences exist.The first author was supported by a Medical Research Council UK Fellowship.Peer reviewedAuthor versio