Randomized trials and observational studies, such as case-control studies, are often seen as opposing approaches. However, in many instances results obtained by different designs may complement each other. For instance, case-control studies on aetiology of disease may help to give the direction of future trials. In this commentary, the author discusses the purpose of randomization and observation, and under which conditions one design may be preferred to another. Randomization is useful to combat 'confounding by indication', and is therefore the design of choice for most therapeutic trials. When this confounding is not an issue, as in studies of genetic risk factors or side-effects, then case-control studies are preferred.

Background Many randomized trials involve measuring a continuous outcome - such as pain, body weight or blood pressure - at baseline and after treatment. In this paper, I compare four possibilities for how such trials can be analyzed: post-treatment; change between baseline and post-treatment; percentage change between baseline and post-treatment and analysis of covariance (ANCOVA) with baseline score as a covariate. The statistical power of each method was determined for a hypothetical randomized trial under a range of correlations between baseline and post-treatment scores. Results ANCOVA has the highest statistical power. Change from baseline has acceptable power when correlation between baseline and post-treatment scores is high;when correlation is low, analyzing only post-treatment scores has reasonable power. Percentage change from baseline has the lowest statistical power and was highly sensitive to changes in variance. Theoretical considerations suggest that percentage change from baseline will also fail to protect from bias in the case of baseline imbalance and will lead to an excess of trials with non-normally distributed outcome data. Conclusions Percentage change from baseline should not be used in statistical analysis. Trialists wishing to report this statistic should use another method, such as ANCOVA, and convert the results to a percentage change by using mean baseline scores.

Background Practice guidelines have been developed in response to the observation that variations exist in clinical medicine that are not related to variations in the clinical presentation and severity of the disease. Despite their widespread use, however, practice guideline evaluation lacks a rigorous scientific methodology to support its development and application. Discussion Firstly, we review the major epidemiological foundations of practice guideline development. Secondly, we propose a chronic disease epidemiological model in which practice patterns are viewed as the exposure and outcomes of interest such as quality or cost are viewed as the disease. Sources of selection, information, confounding and temporal trend bias are identified and discussed. Summary The proposed methodological framework for outcomes research to evaluate practice guidelines reflects the selection, information and confounding biases inherent in its observational nature which must be accounted for in both the design and the analysis phases of any outcomes research study.

Analysis of subgroup results in a clinical trial is surprisingly unreliable, even in a large trial. This is the result of a combination of reduced statistical power, increased variance and the play of chance. Reliance on such analyses is likely to be more erroneous, and hence harmful, than application of the overall proportional (or relative) result in the whole trial to the estimate of absolute risk in that subgroup. Plausible explanations can usually be found for effects that are, in reality, simply due to the play of chance. When clinicians believe such subgroup analyses, there is a real danger of harm to the individual patient.

Background The benefit of regular multidimensional assessment of older people remains controversial. The majority of trials have been too small to produce adequate evidence to inform policy. Despite the lack of a firm evidence base, UK primary care practitioners (general practitioners) are required to offer an annual health check to patients aged 75 years and over. Design Cluster-randomised factorial trial in primary care comparing a package of assessments (i) universal versus targeted assessment and (ii) management by the primary care team (PC) or a multidisciplinary geriatric assessment team (GM). The unit of randomization is the general practice. Methods Older people aged 75 and over eligible for the over 75s health check and excluding those in nursing homes or terminally ill were invited to participate. All participants receive a brief assessment covering all areas of the over 75s check. In the universal arm all participants also receive a detailed health and social assessment by a study nurse while in the targeted arm only participants with a pre-determined number and range of problems at the brief assessment go on to have the detailed assessment. The study nurse follows a standard protocol based on results and responses in the detailed assessment to make referrals to (i) the randomised management team (PC or GM) (ii) other medical services, health care workers or agencies (iii) emergency referrals to the GP. The main outcomes are mortality, hospital and institutional admissions and quality of life. 106 practices and 33,000 older people have been recruited to the trial.

Background Published formulas for case-control designs provide sample sizes required to determine that a given disease-exposure odds ratio is significantly different from one, adjusting for a potential confounder and possible interaction. Results The formulas are extended from one control per case to F controls per case and adjusted for a potential multi-category confounder in unmatched or matched designs. Interactive FORTRAN programs are described which compute the formulas. The effect of potential disease-exposure-confounder interaction may be explored. Conclusions Software is now available for computing adjusted sample sizes for case-control designs.

Background The study of cost-effectiveness comparisons between competing medical interventions has led to a variety of proposals for quantifying cost-effectiveness. The differences between the various approaches can be subtle, and one purpose of this article is to clarify some important distinctions. Discussion We discuss alternative measures in the framework of individual, patient-level, incremental net benefits. In particular we examine the probability of cost-effectiveness for an individual, proposed by Willan. Summary We argue that this is a useful addition to the range of cost-effectiveness measures, but will be of secondary interest to most decision makers. We also demonstrate that Willan's proposed estimate of this probability is logically flawed.

Some problems with the quality of controlled clinical trials can be addressed by following these procedures: registering all trials at inception; using systematic reviews to inform the design of new studies; posting and obtaining feedback on preprints; reporting all well conducted trials, regardless of their results; reducing biased and inefficient assessment of reports submitted for publication; publishing sufficiently detailed reports; linking trial reports to relevant external information; providing readier access to reports; and reviewing and amending reports after initial publication. The launch of a new range of electronic journals by Current Controlled Trials offers an opportunity to contribute to progress in these ways.

Background Appropriate information flow is crucial to the care of patients, particularly at the interface between primary and secondary care. Communication problems can result from inadequate organisation and training, There is a major expectation that information and communication technologies may offer solutions, but little reliable evidence. This paper reports the design and performance of a multi-centre randomised controlled trial (RCT), unparalleled in telemedicine research in either scale or range of outcomes. The study investigated the effectiveness and cost implications in rural and inner-city settings of using videoconferencing to perform joint tele-consultations as an alternative to general practitioner referral to the hospital specialist in the outpatient clinic. Methods Joint tele-consultation services were established in both the Royal Free Hampstead NHS Trust in inner London, and the Royal Shrewsbury Hospitals Trust, in Shropshire. All the patients who gave consent to participate were randomised either to joint tele-consultation or to a routine outpatients appointment. The principal outcome measures included the frequency of decision by the specialist to offer a follow-up outpatient appointment, patient satisfaction (Ware Specific Questionnaire), wellbeing (SF12) and enablement (PEI), numbers of tests, investigations, procedures and treatments. Results A total of 134 general practitioners operating from 29 practices participated in the trial, referring a total of 3170 patients to 20 specialists in ENT medicine, general medicine (including endocrinology, and rheumatology), gastroenterology, orthopaedics, neurology and urology. Of these, 2094 patients consented to participate in the study and were correctly randomised. There was a 91% response rate to the initial assessment questionnaires, and analysis showed equivalence for all key characteristics between the treatment and control groups. Conclusion We have designed and performed a major multi-centre trial of teleconsultations in two contrasting centres. Many problems were overcome to enable the trial to be carried out, with a considerable development and learning phase. A lengthier development phase might have enabled us to improve the patient selection criteria, but there is a window of opportunity for these developments, and we believe that our approach was appropriate, allowing the evaluation of the technology before its widespread implementation.

Background Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). Methods An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. Results Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. Conclusions The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis.