An investigation of minimisation criteria

Minimisation can be used within treatment trials to ensure that prognostic factors are evenly distributed between treatment groups. The technique is relatively straightforward to apply but does require running tallies of patient recruitments to be made and some simple calculations to be performed prior to each allocation. As computing facilities have become more widely available, minimisation has become a more feasible option for many. Although the technique has increased in popularity, the mode of application is often poorly reported and the choice of input parameters not justified in any logical way

Simpson's paradox and calculation of number needed to treat from meta-analysis

BACKGROUND: Calculation of numbers needed to treat (NNT) is more complex from meta-analysis than from single trials. Treating the data as if it all came from one trial may lead to misleading results when the trial arms are imbalanced. DISCUSSION: An example is shown from a published Cochrane review in which the benefit of nursing intervention for smoking cessation is shown by formal meta-analysis of the individual trial results. However if these patients were added together as if they all came from one trial the direction of the effect appears to be reversed (due to Simpson's paradox). Whilst NNT from meta-analysis can be calculated from pooled Risk Differences, this is unlikely to be a stable method unless the event rates in the control groups are very similar. Since in practice event rates vary considerably, the use a relative measure, such as Odds Ratio or Relative Risk is advocated. These can be applied to different levels of baseline risk to generate a risk specific NNT for the treatment. SUMMARY: The method used to calculate NNT from meta-analysis should be clearly stated, and adding the patients from separate trials as if they all came from one trial should be avoided

Selection of confounding variables should not be based on observed associations with exposure

In observational studies, selection of confounding variables for adjustment is often based on observed baseline incomparability. The aim of this study was to evaluate this selection strategy. We used clinical data on the effects of inhaled long-acting beta-agonist (LABA) use on the risk of mortality among patients with obstructive pulmonary disease to illustrate the impact of selection of confounding variables for adjustment based on baseline comparisons. Among 2,394 asthma and COPD patients included in the analyses, the LABA ever-users were considerably older than never-users, but cardiovascular co-morbidity was equally prevalent (19.9% vs. 19.9%). Adjustment for cardiovascular co-morbidity status did not affect the crude risk ratio (RR) for mortality: crude RR 1.19 (95% CI 0.93–1.51) versus RR 1.19 (95% CI 0.94–1.50) after adjustment for cardiovascular co-morbidity. However, after adjustment for age (RR 0.95, 95% CI 0.76–1.19), additional adjustment for cardiovascular co-morbidity status did affect the association between LABA use and mortality (RR 1.01, 95% CI 0.80–1.26). Confounding variables should not be discarded based on balanced distributions among exposure groups, because residual confounding due to the omission of confounding variables from the adjustment model can be relevant

Current sample size conventions: Flaws, harms, and alternatives

<p>Abstract</p> <p>Background</p> <p>The belief remains widespread that medical research studies must have statistical power of at least 80% in order to be scientifically sound, and peer reviewers often question whether power is high enough.</p> <p>Discussion</p> <p>This requirement and the methods for meeting it have severe flaws. Notably, the true nature of how sample size influences a study's projected scientific or practical value precludes any meaningful blanket designation of <80% power as "inadequate". In addition, standard calculations are inherently unreliable, and focusing only on power neglects a completed study's most important results: estimates and confidence intervals. Current conventions harm the research process in many ways: promoting misinterpretation of completed studies, eroding scientific integrity, giving reviewers arbitrary power, inhibiting innovation, perverting ethical standards, wasting effort, and wasting money. Medical research would benefit from alternative approaches, including established <it>value of information </it>methods, simple choices based on cost or feasibility that have recently been justified, sensitivity analyses that examine a meaningful array of possible findings, and following previous analogous studies. To promote more rational approaches, research training should cover the issues presented here, peer reviewers should be extremely careful before raising issues of "inadequate" sample size, and reports of completed studies should not discuss power.</p> <p>Summary</p> <p>Common conventions and expectations concerning sample size are deeply flawed, cause serious harm to the research process, and should be replaced by more rational alternatives.</p

Reporting on covariate adjustment in randomised controlled trials before and after revision of the 2001 CONSORT statement: a literature review

<p>Abstract</p> <p>Objectives</p> <p>To evaluate the use and reporting of adjusted analysis in randomised controlled trials (RCTs) and compare the quality of reporting before and after the revision of the CONSORT Statement in 2001.</p> <p>Design</p> <p>Comparison of two cross sectional samples of published articles.</p> <p>Data Sources</p> <p>Journal articles indexed on PubMed in December 2000 and December 2006.</p> <p>Study Selection</p> <p>Parallel group RCTs with a full publication carried out in humans and published in English</p> <p>Main outcome measures</p> <p>Proportion of articles reported adjusted analysis; use of adjusted analysis; the reason for adjustment; the method of adjustment and the reporting of adjusted analysis results in the main text and abstract.</p> <p>Results</p> <p>In both cohorts, 25% of studies reported adjusted analysis (84/355 in 2000 vs 113/422 in 2006). Compared with articles reporting only unadjusted analyses, articles that reported adjusted analyses were more likely to specify primary outcomes, involve multiple centers, perform stratified randomization, be published in general medical journals, and recruit larger sample sizes. In both years a minority of articles explained why and how covariates were selected for adjustment (20% to 30%). Almost all articles specified the statistical methods used for adjustment (99% in 2000 vs 100% in 2006) but only 5% and 10%, respectively, reported both adjusted and unadjusted results as recommended in the CONSORT guidelines.</p> <p>Conclusion</p> <p>There was no evidence of change in the reporting of adjusted analysis results five years after the revision of the CONSORT Statement and only a few articles adhered fully to the CONSORT recommendations.</p

IMPLEmenting a clinical practice guideline for acute low back pain evidence-based manageMENT in general practice (IMPLEMENT) : cluster randomised controlled trial study protocol

Background: Evidence generated from reliable research is not frequently implemented into clinical practice. Evidence-based clinical practice guidelines are a potential vehicle to achieve this. A recent systematic review of implementation strategies of guideline dissemination concluded that there was a lack of evidence regarding effective strategies to promote the uptake of guidelines. Recommendations from this review, and other studies, have suggested the use of interventions that are theoretically based because these may be more effective than those that are not. An evidencebased clinical practice guideline for the management of acute low back pain was recently developed in Australia. This provides an opportunity to develop and test a theory-based implementation intervention for a condition which is common, has a high burden, and for which there is an evidence-practice gap in the primary care setting. Aim: This study aims to test the effectiveness of a theory-based intervention for implementing a clinical practice guideline for acute low back pain in general practice in Victoria, Australia. Specifically, our primary objectives are to establish if the intervention is effective in reducing the percentage of patients who are referred for a plain x-ray, and improving mean level of disability for patients three months post-consultation. Methods/Design: This study protocol describes the details of a cluster randomised controlled trial. Ninety-two general practices (clusters), which include at least one consenting general practitioner, will be randomised to an intervention or control arm using restricted randomisation. Patients aged 18 years or older who visit a participating practitioner for acute non-specific low back pain of less than three months duration will be eligible for inclusion. An average of twenty-five patients per general practice will be recruited, providing a total of 2,300 patient participants. General practitioners in the control arm will receive access to the guideline using the existing dissemination strategy. Practitioners in the intervention arm will be invited to participate in facilitated face-to-face workshops that have been underpinned by behavioural theory. Investigators (not involved in the delivery of the intervention), patients, outcome assessors and the study statistician will be blinded to group allocation. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN012606000098538 (date registered 14/03/2006).The trial is funded by the NHMRC by way of a Primary Health Care Project Grant (334060). JF has 50% of her time funded by the Chief Scientist Office3/2006). of the Scottish Government Health Directorate and 50% by the University of Aberdeen. PK is supported by a NHMRC Health Professional Fellowship (384366) and RB by a NHMRC Practitioner Fellowship (334010). JG holds a Canada Research Chair in Health Knowledge Transfer and Uptake. All other authors are funded by their own institutions

Design, analysis, and presentation of crossover trials

OBJECTIVE: Although crossover trials enjoy wide use, standards for analysis and reporting have not been established. We reviewed methodological aspects and quality of reporting in a representative sample of published crossover trials. METHODS: We searched MEDLINE for December 2000 and identified all randomized crossover trials. We abstracted data independently, in duplicate, on 14 design criteria, 13 analysis criteria, and 14 criteria assessing the data presentation. RESULTS: We identified 526 randomized controlled trials, of which 116 were crossover trials. Trials were drug efficacy (48%), pharmacokinetic (28%), and nonpharmacologic (30%). The median sample size was 15 (interquartile range 8-38). Most (72%) trials used 2 treatments and had 2 periods (64%). Few trials reported allocation concealment (17%) or sequence generation (7%). Only 20% of trials reported a sample size calculation and only 31% of these considered pairing of data in the calculation. Carry-over issues were addressed in 29% of trial's methods. Most trials reported and defended a washout period (70%). Almost all trials (93%) tested for treatment effects using paired data and also presented details on by-group results (95%). Only 29% presented CIs or SE so that data could be entered into a meta-analysis. CONCLUSION: Reports of crossover trials frequently omit important methodological issues in design, analysis, and presentation. Guidelines for the conduct and reporting of crossover trials might improve the conduct and reporting of studies using this important trial design

Guidelines for the Content of Statistical Analysis Plans in Clinical Trials.

While guidance on statistical principles for clinical trials exists, there is an absence of guidance covering the required content of statistical analysis plans (SAPs) to support transparency and reproducibility.To develop recommendations for a minimum set of items that should be addressed in SAPs for clinical trials, developed with input from statisticians, previous guideline authors, journal editors, regulators, and funders.Funders and regulators (n = 39) of randomized trials were contacted and the literature was searched to identify existing guidance; a survey of current practice was conducted across the network of UK Clinical Research Collaboration-registered trial units (n = 46, 1 unit had 2 responders) and a Delphi survey (n = 73 invited participants) was conducted to establish consensus on SAPs. The Delphi survey was sent to statisticians in trial units who completed the survey of current practice (n = 46), CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guideline authors (n = 16), pharmaceutical industry statisticians (n = 3), journal editors (n = 9), and regulators (n = 2) (3 participants were included in 2 groups each), culminating in a consensus meeting attended by experts (N = 12) with representatives from each group. The guidance subsequently underwent critical review by statisticians from the surveyed trial units and members of the expert panel of the consensus meeting (N = 51), followed by piloting of the guidance document in the SAPs of 5 trials.No existing guidance was identified. The registered trials unit survey (46 responses) highlighted diversity in current practice and confirmed support for developing guidance. The Delphi survey (54 of 73, 74% participants completing both rounds) reached consensus on 42% (n = 46) of 110 items. The expert panel (N = 12) agreed that 63 items should be included in the guidance, with an additional 17 items identified as important but may be referenced elsewhere. Following critical review and piloting, some overlapping items were combined, leaving 55 items.Recommendations are provided for a minimum set of items that should be addressed and included in SAPs for clinical trials. Trial registration, protocols, and statistical analysis plans are critically important in ensuring appropriate reporting of clinical trials

Conducting research in individual patients: lessons learnt from two series of N-of-1 trials

BACKGROUND: Double-blind randomised N-of-1 trials (N-of-1 trials) may help with decisions concerning treatment when there is doubt regarding the effectiveness and suitability of medication for individual patients. The patient is his or her own control, and receives the experimental and the control treatment during several periods of time in random order. Reports of N-of-1 trials are still relatively scarce, and the research methodology is not as firmly established as that of RCTs. Recently, we have conducted two series of N-of-1 trials in general practice. Before, during, and after data-collection, difficulties regarding outcome assessment, analysis of the results, the withdrawal of patients, and the follow-up had to be dealt with. These difficulties are described and our solutions are discussed. DISCUSSION: To prevent or anticipate difficulties in N-of-1 trials, we argue that that it is important to individualise the outcome measures, and to carefully consider the objective, type of randomisation and the analysis. It is recommended to use the same dosages and dosage forms that the patient used before the trial, to start the trial with a run-in period, to formulate both general and individualised decision rules regarding the efficacy of treatment, to adjust treatment policies immediately after the trial, and to provide adequate instructions and support if treatment is adjusted. SUMMARY: Because of the specific characteristics of N-of-1 trials it is difficult to formulate general 'how to do it' guidelines for designing N-of-1 trials. However, when the design of each N-of-1 trial is tailored to the specific characteristics of each individual patient and the underlying medical problem, most difficulties in N-of-1 trials can be prevented or overcome. In this way, N-of-1 trials may be of help when deciding on drug treatment for individual patients