A sensitivity analysis framework for the treatment effect measure used in the meta‐analysis of comparative binary data from randomised controlled trials

The process of undertaking a meta‐analysis involves a sequence of decisions, one of which is deciding which measure of treatment effect to use. In particular, for comparative binary data from randomised controlled trials, a wide variety of measures are available such as the odds ratio and the risk difference. It is often of interest to know whether important conclusions would have been substantively different if an alternative measure had been used. Here we develop a new type of sensitivity analysis that incorporates standard measures of treatment effect. Thus, rather than examining the implications of a variety of measures in an ad hoc manner, we can simultaneously examine an entire family of possibilities, including the odds ratio, the arcsine difference and the risk difference. Copyright © 2012 John Wiley & Sons, Ltd.     

Label‐invariant models for the analysis of meta‐epidemiological data

Rich meta‐epidemiological data sets have been collected to explore associations between intervention effect estimates and study‐level characteristics. Welton et al proposed models for the analysis of meta‐epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta‐analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label‐invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between‐trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label‐invariant models for meta‐epidemiological data analysis facilitate investigations of between‐study heterogeneity attributable to certain study characteristics.     

Planning future studies based on the conditional power of a meta‐analysis

Systematic reviews often provide recommendations for further research. When meta‐analyses are inconclusive, such recommendations typically argue for further studies to be conducted. However, the nature and amount of future research should depend on the nature and amount of the existing research. We propose a method based on conditional power to make these recommendations more specific. Assuming a random‐effects meta‐analysis model, we evaluate the influence of the number of additional studies, of their information sizes and of the heterogeneity anticipated among them on the ability of an updated meta‐analysis to detect a prespecified effect size. The conditional powers of possible design alternatives can be summarized in a simple graph which can also be the basis for decision making. We use three examples from the Cochrane Database of Systematic Reviews to demonstrate our strategy. We demonstrate that if heterogeneity is anticipated, it might not be possible for a single study to reach the desirable power no matter how large it is. Copyright © 2012 John Wiley & Sons, Ltd.     

A re‐evaluation of the ‘quantile approximation method’ for random effects meta‐analysis

The quantile approximation method has recently been proposed as a simple method for deriving confidence intervals for the treatment effect in a random effects meta‐analysis. Although easily implemented, the quantiles used to construct intervals are derived from a single simulation study. Here it is shown that altering the study parameters, and in particular introducing changes to the distribution of the within‐study variances, can have a dramatic impact on the resulting quantiles. This is further illustrated analytically by examining the scenario where all trials are assumed to be the same size. A more cautious approach is therefore suggested, where the conventional standard normal quantile is used in the primary analysis, but where the use of alternative quantiles is also considered in a sensitivity analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

‘They’re doing surgery on two people’: a meta‐ethnography of the influences on couples’ treatment decision making for prostate cancer

Background Current orthodoxy suggests that patients need to be provided with full information about their care and that treatment options should be discussed with patients and family members. This imperative is especially challenging when there is a lack of consensus about treatment effectiveness and equivocacy over different types of interventions. In the case of prostate cancer, evidence is contested as to the efficacy of different treatments. Thus, involving patients and their family members in treatment choices is complex and little is known about how patients and their partners process these decisions when there is uncertainty about different outcomes. This paper has reviewed the literature on the way couples approach such decision making in relation to treatment for prostate cancer. Objective A meta‐ethnographic synthesis of published qualitative papers that focused on the influences on patients’, and their partners’ treatment decision making for prostate cancer, was conducted in order to identify and understand barriers and facilitators which impact on this process. Results Our synthesis indicates that the couples’ relationship ‘dynamic’ provides a contextual background against which treatment decisions are negotiated and made. Discussion and conclusions We propose that the findings from this synthesis can enhance the potential for shared decision making for patients, and their partners, when facing a treatment decision for prostate cancer. By understanding the couples’ relationship dynamic pre‐diagnosis, clinicians may be able to tailor the communication and information provision to both patients and their partners, providing a personalized approach to treatment decision making.     

A linearization approach for the model‐based analysis of combined aggregate and individual patient data

The application of model‐based meta‐analysis in drug development has gained prominence recently, particularly for characterizing dose‐response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary‐level literature (or aggregate data (AD)). Inferring individual patient‐level relationships from these nonlinear meta‐analysis models leads to aggregation bias. Individual patient‐level data (IPD) are indeed required to characterize patient‐level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP‐4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between‐trial to within‐trial variability) were studied. A dose‐response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between‐trial to within‐trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data. Copyright © 2014 John Wiley & Sons, Ltd.