Anne Whitehead

Meta-Analysis of Controlled Clinical Trials.

Introduction Protocol development Estimating the treatment difference in an individual trial Combining estimates of a treatment difference across trials Meta--analysis using individual patient data Dealing with heterogeneity Presentation and interpretation of results Selection bias Dealing with non--standard datasets Inclusion of trials with different study designs A Bayesian approach to meta--analysis Sequential methods for meta--analysis Appendix Methods of estimation and hypothesis testing

Strategies to prevent falls and fractures in hospitals and care homes and effect of cognitive impairment: systematic review and meta-analyses

Objectives To evaluate the evidence for strategies to prevent falls or fractures in residents in care homes and hospital inpatients and to investigate the effect of dementia and cognitive impairment. Design Systematic review and meta-analyses of studies grouped by intervention and setting (hospital or care home). Meta-regression to investigate the effects of dementia and of study quality and design. Data sources Medline, CINAHL, Embase, PsychInfo, Cochrane Database, Clinical Trials Register, and hand searching of references from reviews and guidelines to January 2005. Results 1207 references were identified, including 115 systematic reviews, expert reviews, or guidelines. Of the 92 full papers inspected, 43 were included. Meta-analysis for multifaceted interventions in hospital (13 studies) showed a rate ratio of 0.82 (95% confidence interval 0.68 to 0.997) for falls but no significant effect on the number of fallers or fractures. For hip protectors in care homes (11 studies) the rate ratio for hip fractures was 0.67 (0.46 to 0.98), but there was no significant effect on falls and not enough studies on fallers. For all other interventions (multifaceted interventions in care homes; removal of physical restraints in either setting; fall alarm devices in either setting; exercise in care homes; calcium/vitamin D in care homes; changes in the physical environment in either setting; medication review in hospital) meta-analysis was either unsuitable because of insufficient studies or showed no significant effect on falls, fallers, or fractures, despite strongly positive results in some individual studies. Meta-regression showed no significant association between effect size and prevalence of dementia or cognitive impairment. Conclusion There is some evidence that multifaceted interventions in hospital reduce the number of falls and that use of hip protectors in care homes prevents hip fractures. There is insufficient evidence, however, for the effectiveness of other single interventions in hospitals or care homes or multifaceted interventions in care homes.

BORROWING STRENGTH FROM EXTERNAL TRIALS IN A META‐ANALYSIS

There exists a variety of situations in which a random effects meta-analysis might be undertaken using a small number of clinical trials. A problem associated with small meta-analyses is estimating the heterogeneity between trials. To overcome this problem, information from other related studies may be incorporated into the meta-analysis. A Bayesian approach to this problem is presented using data from previous meta-analyses in the same therapeutic area to formulate a prior distribution for the heterogeneity. The treatment difference parameters are given non-informative priors. Further, related trials which compare one or other of the treatments of interest with a common third treatment are included in the model to improve inference on both the heterogeneity and the treatment difference. Two approaches to estimating relative efficacy are considered, namely a general parametric approach and a method explicit to binary data. The methodology is illustrated using data from 26 clinical trials which investigate the prevention of cirrhosis using beta-blockers and sclerotherapy. Both sources of external information lead to more precise posterior distributions for all parameters, in particular that representing heterogeneity.

Sequential methods for random-effects meta-analysis.

Although meta-analyses are typically viewed as retrospective activities, they are increasingly being applied prospectively to provide up-to-date evidence on specific research questions. When meta-analyses are updated account should be taken of the possibility of false-positive findings due to repeated significance tests. We discuss the use of sequential methods for meta-analyses that incorporate random effects to allow for heterogeneity across studies. We propose a method that uses an approximate semi-Bayes procedure to update evidence on the among-study variance, starting with an informative prior distribution that might be based on findings from previous meta-analyses. We compare our methods with other approaches, including the traditional method of cumulative meta-analysis, in a simulation study and observe that it has Type I and Type II error rates close to the nominal level. We illustrate the method using an example in the treatment of bleeding peptic ulcers. Copyright

Manual vs. integrated automatic load-distributing band CPR with equal survival after out of hospital cardiac arrest. The randomized CIRC trial

OBJECTIVE To compare integrated automated load distributing band CPR (iA-CPR) with high-quality manual CPR (M-CPR) to determine equivalence, superiority, or inferiority in survival to hospital discharge. METHODS Between March 5, 2009 and January 11, 2011 a randomized, unblinded, controlled group sequential trial of adult out-of-hospital cardiac arrests of presumed cardiac origin was conducted at three US and two European sites. After EMS providers initiated manual compressions patients were randomized to receive either iA-CPR or M-CPR. Patient follow-up was until all patients were discharged alive or died. The primary outcome, survival to hospital discharge, was analyzed adjusting for covariates, (age, witnessed arrest, initial cardiac rhythm, enrollment site) and interim analyses. CPR quality and protocol adherence were monitored (CPR fraction) electronically throughout the trial. RESULTS Of 4753 randomized patients, 522 (11.0%) met post enrollment exclusion criteria. Therefore, 2099 (49.6%) received iA-CPR and 2132 (50.4%) M-CPR. Sustained ROSC (emergency department admittance), 24h survival and hospital discharge (unknown for 12 cases) for iA-CPR compared to M-CPR were 600 (28.6%) vs. 689 (32.3%), 456 (21.8%) vs. 532 (25.0%), 196 (9.4%) vs. 233 (11.0%) patients, respectively. The adjusted odds ratio of survival to hospital discharge for iA-CPR compared to M-CPR, was 1.06 (95% CI 0.83-1.37), meeting the criteria for equivalence. The 20 min CPR fraction was 80.4% for iA-CPR and 80.2% for M-CPR. CONCLUSION Compared to high-quality M-CPR, iA-CPR resulted in statistically equivalent survival to hospital discharge.

Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials

Background In clinical trials following individuals over a period of time, the same assessment may be made at a number of time points during the course of the trial. Our review of current practice for handling longitudinal data in Cochrane systematic reviews shows that the most frequently used approach is to ignore the correlation between repeated observations and to conduct separate meta-analyses at each of a number of time points. Purpose The purpose of this paper is to show the link between repeated measurement models used with aggregate data and those used when individual patient data (IPD) are available, and provide guidance on the methods that practitioners might use for aggregate data meta-analyses, depending on the type of data available. Methods We discuss models for the meta-analysis of longitudinal continuous outcome data when IPD are available. In these models time is included either as a factor or as a continuous variable, and account is taken of the correlation between repeated observations. The meta-analysis of IPD can be conducted using either a one-step or a two-step approach: the latter involves analysing the IPD separately in each study and then combining the study estimates taking into account their covariance structure. We discuss the link between models for use with aggregate data and the two-step IPD approach, and the problems which arise when only aggregate data are available. The methods are applied to IPD from 5 trials in Alzheimers disease. Results Two major issues for the meta-analysis of aggregate data are the lack of information about correlation coefficients and the effect of missing data at the patient-level. Application to the Alzheimers disease data set shows that ignoring correlation can lead to different pooled estimates of the treatment difference and their standard errors. Furthermore, the amount of missing data at the patient level can affect these estimates. Limitations The models assume fixed treatment effects across studies, and that any missing data is missing at random, both at the patient-level and the study level. Conclusions It is preferable to obtain IPD from all studies to correctly account for the correlation between repeated observations. When IPD are not available, the ideal aggregate data are model-based estimates of treatment difference and their variance and covariance estimates. If covariance estimates are not available, sensitivity analyses should be undertaken to investigate the robustness of the results to different amounts of correlation. Clinical Trials 2009; 6: 16—27. http:// ctj.sagepub.com

A prospectively planned cumulative meta-analysis applied to a series of concurrent clinical trials

Sequential designs are now a familiar part of clinical trial methodology. In particular, the triangular test has been used in several individual studies. Methods of combining studies are also well-known from the literature on meta-analysis. However, the combination of the two approaches is new. Consider the situation where a series of studies is to be conducted, following broadly similar protocols comparing a new treatment with a control treatment. In order to obtain an answer as quickly as possible to an efficacy or safety question it may be desirable to perform a cumulative meta-analysis on one particular variable. This could, for example, be the primary efficacy variable, an expensive assessment conducted in only a subgroup of patients, or a serious side-effect. To allow for the size of the treatment difference varying from study to study we might wish to provide a global estimate. Hence a random effects combined analysis, within a sequential framework, would appear to be appropriate. A methodology which utilizes efficient score statistics and Fishers information is presented. Simulations show that the proposed methodology will achieve the specified error probabilities with reasonable accuracy provided that any random effect is relatively small. Ignoring random effects when they are present can lead to inaccuracies. A simulated example illustrates a number of practical issues.

Mid-trial design reviews for sequential clinical trials

When sequential clinical trials are conducted by plotting a statistic measuring treatment difference against another measuring information, power is guaranteed regardless of nuisance parameters. However, values need to be assigned to nuisance parameters in order to gain an impression of the sample size distribution. Each interim analysis provides an opportunity to re-evaluate the relationship between sample size and information. In this paper we discuss such mid-trial design reviews. In the special cases of trials with a relatively short recruitment phase followed by a longer period of follow-up, and of normally distributed responses, mid-trial design reviews are particularly important. Examples are given of the various situations considered, and extensive simulations are reported demonstrating the validity of the review procedure in the case of normally distributed responses.

Combining summaries of binary outcomes with those of continuous outcomes in a meta-analysis.

We present a methodology for combining trials some of which report continuous outcome measures and others binary outcomes created by a dichotomy of the continuous measurement. This was motivated by a series of controlled clinical trials investigating the effect of prophylactic use of oxytocics on postpartum blood loss during labor. Data were available in the form of summary statistics from published papers. The log-odds ratio is used as a common measure of treatment difference across all trials. We discuss the general applicability of this approach.

A tool to assess the quality of a meta-analysis

BACKGROUND Because meta-analyses are increasingly prevalent and cited in the medical literature, it is important that tools are available to assess their methodological quality. When performing an empirical study of the quality of published meta-analyses, we found that existing tools did not place a strong emphasis on statistical and interpretational issues. METHODS We developed a quality-assessment tool using existing materials and expert judgment as a starting point, followed by multiple iterations of input from our working group, piloting, and discussion. After having used the tool for our empirical study, agreement for four key items in the tool was measured using weighted kappa coefficients. RESULTS Our tool contained 43 items divided into four key areas (data sources, analysis of individual studies, meta-analysis methods, and interpretation), and each area ended with a summary question. We also produced guidance for completing the tool. Agreement between raters was fair to moderate. CONCLUSIONS The tool should usefully inform subsequent initiatives to develop quality-assessment tools for meta-analysis. We advocate use of consensus between independent raters when assessing statistical appropriateness and adequacy of interpretation in meta-analyses.