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In order to avoid certain difficulties with the conventional randomized clinical trial design, the expertise-based design has been proposed as an alternative. In the expertise-based design, patients are randomized to clinicians (e.g. surgeons), who then treat all their patients with their preferred intervention. This design recognizes individual clinical preferences and so may reduce the rates of procedural crossovers compared with the conventional design. It may also facilitate recruitment of clinicians, because they are always allowed to deliver their therapy of choice, a feature that may also be attractive to patients. The expertise-based design avoids the possibility of so-called differential expertise bias. If a standard treatment is generally more familiar to clinicians than a new experimental treatment, then in the conventional design, more patients randomized to the standard treatment will have an expert clinician, compared with patients randomized to the experimental treatment. If expertise affects the study outcome, then a biased comparison of the treatment groups will occur. We examined the relative efficiency of estimating the treatment effect in the expertise-based and conventional designs. We recognize that expected patient outcomes may be better in the expertise-based design, which in turn may modify the estimated treatment effect. In particular, a larger treatment effect in the expertise-based design can sometimes offset a higher standard error arising from the confounding of clinician effects with treatments. These concepts are illustrated with data taken from a randomized trial of two alternative surgical techniques for tibial fractures. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We are grateful to Dr Etienne Kaelin for pointing out four typographic errors in the equations for this paper. These are as follows: [ABSTRACT FROM AUTHOR]

Stopping rules for clinical trials are primarily intended to control Type I error rates if interim analyses are planned, but less is known about the impact that potential stopping has on estimating treatment benefit. In this paper, we derive analytic expressions for (1) the over-estimation of benefit in studies that stop early, (2) the under-estimation of benefit in completed studies, and (3) the overall bias in studies with a stopping rule. We also examine the probability of stopping early and the situation in meta-analyses. Numerical evaluations show that the greatest concern is with over-estimation of benefit in stopped studies, especially if the probability of stopping early is small. The overall bias is usually less than 10% of the true benefit, and under-estimation in completed studies is also typically small. The probability of stopping depends on the true treatment effect and sample size. The magnitude of these effects depends on the particular rule adopted, but we show that the maximum overall bias is the same for all stopping rules. We also show that an essentially unbiased meta-analysis estimate of benefit can be recovered, even if some component studies have stopping rules. We illustrate these methods using data from three clinical trials. The results confirm our earlier empirical work on clinical trials. Investigators may consult our numerical results for guidance on potential mis-estimation and bias in the treatment effect if a stopping rule is adopted. Particular concern is warranted in studies that actually stop early, where interim results may be quite misleading., (© 2019 John Wiley & Sons, Ltd.)

This paper develops a model for cancer screening and cancer incidence data, accommodating the partially unobserved disease status, clustered data structures, general covariate effects, and dependence between exams. The true unobserved cancer and detection status of screening participants are treated as latent variables, and a Markov Chain Monte Carlo algorithm is used to estimate the Bayesian posterior distributions of the diagnostic error rates and disease prevalence. We show how the Bayesian approach can be used to draw inferences about screening exam properties and disease prevalence while allowing for the possibility of conditional dependence between two exams. The techniques are applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using data from the Ontario Breast Screening Program in Canada., (Copyright © 2015 John Wiley & Sons, Ltd.)

Outcomes in clinical trials may be affected by the choice of treatment that participants might make, if they were indeed allowed to choose (a so-called selection effect), and by whether they actually receive their preferred treatment (a preference effect). Selection and preference effects can be important, but they cannot be estimated in the conventional trial design. An alternative approach is the two-stage randomised trial, in which participants are first randomly divided into two subgroups. In one subgroup, participants are randomly assigned to treatments, while in the other, participants are allowed to choose their own treatment. This approach yields estimates of the direct treatment effect, and of the preference and selection effects. The latter two provide insight that goes considerably beyond what is possible in the standard randomised trial. In this paper, we determine the optimal proportion of participants who should be allocated to the choice subgroup. The precision of the estimated selection, preference and treatment effects are functions of: the total sample size; the proportion of participants allocated to choose their treatment; the variances of the outcome; the proportions of participants who select each treatment in the choice group; and the selection, preference and treatment effects themselves. We develop general expressions for the optimum proportion of participants in the choice group, depending on which effects are of primary interest. We illustrate the results with trial data comparing alternative clinical management strategies for women with abnormal results on cervical screening., (Copyright © 2012 John Wiley & Sons, Ltd.)

When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography., (Copyright © 2012 John Wiley & Sons, Ltd.)

Meta-analyses are subject to bias for many of reasons, including publication bias. Asymmetry in a funnel plot of study size against treatment effect is often used to identify such bias. We compare the performance of three simple methods of testing for bias: the rank correlation method; a simple linear regression of the standardized estimate of treatment effect on the precision of the estimate; and a regression of the treatment effect on sample size. The tests are applied to simulated meta-analyses in the presence and absence of publication bias. Both one-sided and two-sided censoring of studies based on statistical significance was used. The results indicate that none of the tests performs consistently well. Test performance varied with the magnitude of the true treatment effect, distribution of study size and whether a one- or two-tailed significance test was employed. Overall, the power of the tests was low when the number of studies per meta-analysis was close to that often observed in practice. Tests that showed the highest power also had type I error rates higher than the nominal level. Based on the empirical type I error rates, a regression of treatment effect on sample size, weighted by the inverse of the variance of the logit of the pooled proportion (using the marginal total) is the preferred method., (Copyright 2001 John Wiley & Sons, Ltd.)

The Canadian Cancer Incidence Atlas is among recent national atlases using incidence rather than mortality data. Methods used to assess the significance and spatial correlation of the age-standardized rates (ASIRs) for the 290 census divisions are described. The expected number of cases by area was used to determine cancer sites with sufficient cases to be mapped. ASIR significance was assessed using a simulation based on a Poisson distribution. The consistency of the observed case distributions with the Poisson distribution was examined. The bootstrap confidence interval (CI) for the ASIR developed by Swift was used in the atlas. Spatial correlation was assessed with Moran's I/I(max) and the significance determined by a simulation in order to allow for area population variation. Data quality indicators typically used for cancer registries were presented, supplemented by a registry questionnaire., (Copyright 2000 John Wiley & Sons, Ltd.)

A relationship of baseline risk to treatment effect size has been suggested as a possible explanation of between-study heterogeneity in meta-analyses. To address this question, we develop regression models to examine the relationship between the logits (or other response measure) in the intervention and control groups. A weighted least squares (WLS) approach is described that allows for the heterogeneous sampling variation in the two groups, together with a correction of the coefficients for sampling error. Two approximate maximum likelihood (ML) solutions are also obtained, with or without an assumption of equal variances between groups within studies. A closed form ML solution exists with the assumption of equal variances. Both methods appear preferable to a previously suggested regression model of the log odds ratio on the control event rate; the methods proposed here use the same scale of measurement for both study groups, and eliminate an artifactual correlation in the regression error structure. The ML approach may be preferable because of its symmetric treatment of study groups, but WLS is more easily implemented with standard software. The methods are illustrated with data from meta-analyses on pre-term delivery and on therapies to lower serum cholesterol.