The key role of micronutrients.
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Antioxidants↗
Biomedical subjects
Craig R Ramsay
Publications and source records attributed to Craig R Ramsay.
Effect of multivitamin and multimineral supplements on morbidity from infections in older people (MAVIS trial): pragmatic, randomised, double blind, placebo controlled trial.
OBJECTIVE: To examine whether supplementation with multivitamins and multiminerals influences self reported days of infection, use of health services, and quality of life in people aged 65 or over. DESIGN: Randomised, placebo controlled trial, with blinding of participants, outcome assessors, and investigators. SETTING: Communities associated with six general practices in Grampian, Scotland. PARTICIPANTS: 910 men and women aged 65 or over who did not take vitamins or minerals. INTERVENTIONS: Daily multivitamin and multimineral supplementation or placebo for one year. MAIN OUTCOME MEASURES: Primary outcomes were contacts with primary care for infections, self reported days of infection, and quality of life. Secondary outcomes included antibiotic prescriptions, hospital admissions, adverse events, and compliance. RESULTS: Supplementation did not significantly affect contacts with primary care and days of infection per person (incidence rate ratio 0.96, 95% confidence interval 0.78 to 1.19 and 1.07, 0.90 to 1.27). Quality of life was not affected by supplementation. No statistically significant findings were found for secondary outcomes or subgroups. CONCLUSION: Routine multivitamin and multimineral supplementation of older people living at home does not affect self reported infection related morbidity. TRIAL REGISTRATION: ISRCTN: 66376460.
Aged↗
Sample size calculator for cluster randomized trials.
Cluster randomized trials, where individuals are randomized in groups are increasingly being used in healthcare evaluation. The adoption of a clustered design has implications for design, conduct and analysis of studies. In particular, standard sample sizes have to be inflated for cluster designs, as outcomes for individuals within clusters may be correlated; inflation can be achieved either by increasing the cluster size or by increasing the number of clusters in the study. A sample size calculator is presented for calculating appropriate sample sizes for cluster trials, whilst allowing the implications of both methods of inflation to be considered.
Algorithms↗
The impact of guidelines on mild hypertension in pregnancy: time series analysis.
OBJECTIVE: To determine the impact of a national strategy to promote implementation of a guideline on the management of mild, non-proteinuric hypertension in pregnancy. DESIGN: Simple, interrupted time series analysis. SETTING: Four maternity units in Scotland. POPULATION: Women delivering a live or stillborn baby. METHODS: Dissemination of the guideline under the auspices of a national clinical effectiveness programme, supported by a national launch meeting and feedback from a survey of obstetricians highlighting aspects of care that could be improved. MAIN OUTCOME MEASURES: Appropriateness of initial investigation and subsequent clinical management, and costs of guideline development and implementation activities. DATA COLLECTION: Twenty-four months pre-intervention and 12 months post-intervention data were abstracted from a random sample of case notes. RESULTS: Initial investigation was consistent with recommendations for 59.9% out of 1263 women and subsequent clinical management for 67.6% out of 1081 in whom a diagnosis could be made from available data. There were no significant changes in the appropriateness of initial investigation (10.6%; 95% confidence interval [CI] -0.1% to 19.3%; decreasing by 1.2% per month post-implementation, 95% CI -2.5% to 0.1%) or clinical management (-0.3%; 95% CI -8.7% to 11.2%). Guideline development and implementation cost an estimated pound 2784 per maternity unit in Scotland. CONCLUSIONS: Clinical care of mild hypertension in pregnancy remains highly inconsistent. The lack of the intervention effect may be related to the complexity of the guideline recommendations and the nature of the implementation strategy.
Adult↗
Statistical evaluation of learning curve effects in surgical trials.
Randomized controlled trials (RCTs) in surgery have been impeded by concerns that improvements in the technical performance of a new technique over time (a "learning curve") may distort comparisons. The statistical assessment of learning curves in trials has received little attention. In this paper, we discuss what a learning curve effect is, the factors which effect it, how to display it, and how to incorporate the learning effect into the trial analysis. Bayesian hierarchical models are proposed to adjust the trial results for the existence of a learning curve effect. The implications for trial evaluation and data collection are considered.
Bayes Theorem↗
Unit of analysis errors should be clarified in meta-analyses.
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Data Interpretation, Statistical↗
Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies.
OBJECTIVES: In an interrupted time series (ITS) design, data are collected at multiple instances over time before and after an intervention to detect whether the intervention has an effect significantly greater than the underlying secular trend. We critically reviewed the methodological quality of ITS designs using studies included in two systematic reviews (a review of mass media interventions and a review of guideline dissemination and implementation strategies). METHODS: Quality criteria were developed, and data were abstracted from each study. If the primary study analyzed the ITS design inappropriately, we reanalyzed the results by using time series regression. RESULTS: Twenty mass media studies and thirty-eight guideline studies were included. A total of 66% of ITS studies did not rule out the threat that another event could have occurred at the point of intervention. Thirty-three studies were reanalyzed, of which eight had significant preintervention trends. All of the studies were considered "effective" in the original report, but approximately half of the reanalyzed studies showed no statistically significant differences. CONCLUSIONS: We demonstrated that ITS designs are often analyzed inappropriately, underpowered, and poorly reported in implementation research. We have illustrated a framework for appraising ITS designs, and more widespread adoption of this framework would strengthen reviews that use ITS designs.
Cross-Sectional Studies↗
The method of minimization for allocation to clinical trials. a review.
Minimization is a largely nonrandom method of treatment allocation for clinical trials. We conducted a systematic literature search to determine its advantages and disadvantages compared with other allocation methods. Minimization was originally proposed by Taves and by Pocock and Simon. The latter paper introduces a family of allocation methods of which Taves' method is the simplest example. Minimization aims to ensure treatment arms are balanced with respect to predefined patient factors as well as for the number of patients in each group. Further extensions of the method have also been proposed by other authors. Simulation studies show that minimization provides better balanced treatment groups when compared with restricted or unrestricted randomization and that it can incorporate more prognostic factors than stratified randomization methods such as permuted blocks within strata. Some more computationally complex methods may give an even better performance. Concerns over the use of minimization have centered on the fact that treatment assignments may be predicted with certainty in some situations and on the implications for the analysis methods used. It has been suggested that adjustment should always be made for minimization factors when analyzing trials where minimization is the allocation method used. The use of minimization may sometimes result in added organizational complexity compared with other methods. Minimization has been recommended by many commentators for use in clinical trials. Despite this it is still rarely used in practice. From the evidence presented in this review, we believe minimization to be a highly effective allocation method and recommend its wider adoption in the conduct of randomized controlled trials.
Clinical Trials as Topic↗
Assessing the learning curve effect in health technologies. Lessons from the nonclinical literature.
INTRODUCTION: Many health technologies exhibit some from 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.
Biomedical Technology↗