There is considerable debate as to the relative merits of using randomised controlled trial (RCT) data as opposed to observational data in systematic reviews of adverse effects. This meta-analysis of meta-analyses aimed to assess the level of agreement or disagreement in the estimates of harm derived from meta-analysis of RCTs as compared to meta-analysis of observational studies.
Methods and Findings
Searches were carried out in ten databases in addition to reference checking, contacting experts, citation searches, and hand-searching key journals, conference proceedings, and Web sites. Studies were included where a pooled relative measure of an adverse effect (odds ratio or risk ratio) from RCTs could be directly compared, using the ratio of odds ratios, with the pooled estimate for the same adverse effect arising from observational studies. Nineteen studies, yielding 58 meta-analyses, were identified for inclusion. The pooled ratio of odds ratios of RCTs compared to observational studies was estimated to be 1.03 (95% confidence interval 0.93–1.15). There was less discrepancy with larger studies. The symmetric funnel plot suggests that there is no consistent difference between risk estimates from meta-analysis of RCT data and those from meta-analysis of observational studies. In almost all instances, the estimates of harm from meta-analyses of the different study designs had 95% confidence intervals that overlapped (54/58, 93%). In terms of statistical significance, in nearly two-thirds (37/58, 64%), the results agreed (both studies showing a significant increase or significant decrease or both showing no significant difference). In only one meta-analysis about one adverse effect was there opposing statistical significance.
Empirical evidence from this overview indicates that there is no difference on average in the risk estimate of adverse effects of an intervention derived from meta-analyses of RCTs and meta-analyses of observational studies. This suggests that systematic reviews of adverse effects should not be restricted to specific study types.
Please see later in the article for the Editors' Summary
Citation: Golder S, Loke YK, Bland M (2011) Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview. PLoS Med 8(5): e1001026. doi:10.1371/journal.pmed.1001026
Academic Editor: Jan P. Vandenbroucke, Leiden University Hospital, The Netherlands
Received: October 6, 2010; Accepted: March 15, 2011; Published: May 3, 2011
Copyright: © 2011 Golder et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was undertaken by Su Golder as part of an MRC fellowship. The views expressed in this presentation are those of the authors and not necessarily those of the MRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The Academic Editor, Jan P. Vandenbroucke, has disclosed that he has worked with authors SG and YKL on another project unrelated to the study reported in this paper. He also discloses an intellectual competing interest in that he has previously published the theoretical viewpoint that estimates of harms outcomes in observational studies may be as valid as results of randomized trials, and may be more generalizable. The authors have no competing interests to declare.
Abbreviations: CI, confidence interval; DARE, Database of Abstracts of Reviews of Effects; HRT, hormone replacement therapy; OR, odds ratio; RCT, randomised controlled trial; ROR, ratio of odds ratios; RR, risk ratio; SE, standard error; VTE, venous thromboembolism
Whenever patients consult a doctor, they expect the treatments they receive to be effective and to have minimal adverse effects (side effects). To ensure that this is the case, all treatments now undergo exhaustive clinical research—carefully designed investigations that test new treatments and therapies in people. Clinical investigations fall into two main groups—randomized controlled trials (RCTs) and observational, or non-randomized, studies. In RCTs, groups of patients with a specific disease or condition are randomly assigned to receive the new treatment or a control treatment, and the outcomes (for example, improvements in health and the occurrence of specific adverse effects) of the two groups of patients are compared. Because the patients are randomly chosen, differences in outcomes between the two groups are likely to be treatment-related. In observational studies, patients who are receiving a specific treatment are enrolled and outcomes in this group are compared to those in a similar group of untreated patients. Because the patient groups are not randomly chosen, differences in outcomes between cases and controls may be the result of a hidden shared characteristic among the cases rather than treatment-related (so-called confounding variables).
Why Was This Study Done?
Although data from individual trials and studies are valuable, much more information about a potential new treatment can be obtained by systematically reviewing all the evidence and then doing a meta-analysis (so-called evidence-based medicine). A systematic review uses predefined criteria to identify all the research on a treatment; meta-analysis is a statistical method for combining the results of several studies to yield “pooled estimates” of the treatment effect (the efficacy of a treatment) and the risk of harm. Treatment effect estimates can differ between RCTs and observational studies, but what about adverse effect estimates? Can different study designs provide a consistent picture of the risk of harm, or are the results from different study designs so disparate that it would be meaningless to combine them in a single review? In this methodological overview, which comprises a systematic review and meta-analyses, the researchers assess the level of agreement in the estimates of harm derived from meta-analysis of RCTs with estimates derived from meta-analysis of observational studies.
What Did the Researchers Do and Find?
The researchers searched literature databases and reference lists, consulted experts, and hand-searched various other sources for studies in which the pooled estimate of an adverse effect from RCTs could be directly compared to the pooled estimate for the same adverse effect from observational studies. They identified 19 studies that together covered 58 separate adverse effects. In almost all instances, the estimates of harm obtained from meta-analyses of RCTs and observational studies had overlapping 95% confidence intervals. That is, in statistical terms, the estimates of harm were similar. Moreover, in nearly two-thirds of cases, there was agreement between RCTs and observational studies about whether a treatment caused a significant increase in adverse effects, a significant decrease, or no significant change (a significant change is one unlikely to have occurred by chance). Finally, the researchers used meta-analysis to calculate that the pooled ratio of the odds ratios (a statistical measurement of risk) of RCTs compared to observational studies was 1.03. This figure suggests that there was no consistent difference between risk estimates obtained from meta-analysis of RCT data and those obtained from meta-analysis of observational study data.
What Do These Findings Mean?
The findings of this methodological overview suggest that there is no difference on average in the risk estimate of an intervention's adverse effects obtained from meta-analyses of RCTs and from meta-analyses of observational studies. Although limited by some aspects of its design, this overview has several important implications for the conduct of systematic reviews of adverse effects. In particular, it suggests that, rather than limiting systematic reviews to certain study designs, it might be better to evaluate a broad range of studies. In this way, it might be possible to build a more complete, more generalizable picture of potential harms associated with an intervention, without any loss of validity, than by evaluating a single type of study. Such a picture, in combination with estimates of treatment effects also obtained from systematic reviews and meta-analyses, would help clinicians decide the best treatment for their patients.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001026.
- The US National Institutes of Health provide information on clinical research; the UK National Health Service Choices Web site also has a page on clinical trials and medical research
- The Cochrane Collaboration produces and disseminates systematic reviews of health-care interventions
- Medline Plus provides links to further information about clinical trials (in English and Spanish)
There is considerable debate regarding the relative utility of different study designs in generating reliable quantitative estimates for the risk of adverse effects. A diverse range of study designs encompassing randomised controlled trials (RCTs) and non-randomised studies (such as cohort or case-control studies) may potentially record adverse effects of interventions and provide useful data for systematic reviews and meta-analyses ,. However, there are strengths and weaknesses inherent to each study design, and different estimates and inferences about adverse effects may arise depending on study type .
In theory, well-conducted RCTs yield unbiased estimates of treatment effect, but there is often a distinct lack of RCT data on adverse effects ,–. It is often impractical, too expensive, or ethically difficult to investigate rare, long-term adverse effects with RCTs ,–. Empirical studies have shown that many RCTs fail to provide detailed adverse effects data, that the quality of those that do report adverse effects is poor ,–, and that the reporting may be strongly influenced by expectations of investigators and patients .
In general RCTs are designed and powered to explore efficacy ,,,,. As the intended effects of treatment are more likely to occur than adverse effects and to occur within the trial time frame, RCTs may not be large enough, or have a sufficient follow-up to identify rare, long-term adverse effects, or adverse effects that occur after the drug has been discontinued –,,,,,,,,,,–. Moreover, generalisability of RCT data may be limited if, as is often the case, trials specifically exclude patients at high risk of adverse effects, such as children, the elderly, pregnant women, patients with multiple comorbidities, and those with potential drug interactions –,,,,,,,–.
Given these limitations it may be important to evaluate the use of data from non-randomised studies in systematic reviews of adverse effects. Owing to the lack of randomisation, all types of observational studies are potentially afflicted by an increased risk of bias (particularly from confounding) , and may therefore be a much weaker study design for establishing causation . Nevertheless, observational study designs may sometimes be the only available source of data for a particular adverse effect, and are commonly used in evaluating adverse effects ,,,,,. It is also debatable how important it is to control for confounding by indication for unanticipated adverse effects. Authors have argued that confounding is less likely to occur when an outcome is unintended or unanticipated than when the outcome is an intended effect of the exposure. This is because the potential for that adverse effect is not usually associated with the reasons for choosing a particular treatment, and therefore does not influence the prescribing decision ,–. For instance, in considering the risk of venous thrombosis from oral contraceptives in healthy young women, the choice of contraceptive may not be linked to risk factors for deep venous thrombosis (an adverse effect that is not anticipated). Thus, any difference in rates of venous thrombosis may be due to a difference in the risk of harm between contraceptives ,.
As both RCTs and observational studies are potentially valuable sources of adverse effects data for meta-analysis, the extent of any discrepancy between the pooled risk estimates from different study designs is a key concern for systematic reviewers. Previous research has tended to focus on differences in treatment effect between RCTs and observational studies –. However, estimates of beneficial effects may potentially be prone to different biases to estimates of adverse effects amongst the different study designs. Can the different study designs provide a consistent picture on the risk of harm, or are the results from different study designs so disparate that it would not be meaningful to combine them in a single review? This uncertainty has not been fully addressed in current methodological guidance on systematic reviews of harms , probably because the existing research has so far been inconclusive, with examples of both agreement and disagreement in the reported risk of adverse effects between RCTs and observational studies ,,,,,–. In this meta-analysis of meta-analyses, we aimed to compare the estimates of harm (for specific adverse effects) reported in meta-analysis of RCTs with those reported in meta-analysis of observational studies for the same adverse effect.
Broad, non-specific searches were undertaken in ten electronic databases to retrieve methodology papers related to any aspect of the incorporation of adverse effects into systematic reviews. A list of the databases and other sources searched is given in Text S1. In addition, the bibliographies of any eligible articles identified were checked for additional references, and citation searches were carried out for all included references using ISI Web of Knowledge. The search strategy used to identify relevant methodological studies in the Cochrane Methodology Register is described in full in Text S2. This strategy was translated as appropriate for the other databases. No language restrictions were applied to the search strategies. However, because of logistical constraints, only non-English papers for which a translation was readily available were retrieved.
Because of the limitations of searching for methodological papers, it was envisaged that relevant papers may be missed by searching databases alone. We therefore undertook hand-searching of selected key journals, conference proceedings, and Web sources, and made contact with other researchers in the field. In particular, one reviewer (S. G.) undertook a detailed hand search focusing on the Cochrane Database of Systematic Reviews and the Database of Abstracts of Reviews of Effects (DARE) to identify systematic reviews that had evaluated adverse effects as a primary outcome. A second reviewer (Y. K. L.) checked the included and excluded papers that arose from this hand search.
A meta-analysis or evaluation study was considered eligible for inclusion in this review if it evaluated studies of more than one type of design (for example, RCTs versus cohort or case-control studies) on the identification and/or quantification of adverse effects of health-care interventions. We were principally interested in meta-analyses that reported pooled estimates of the risk of adverse effects according to study designs that the authors stated as RCTs, as opposed to analytic epidemiologic studies such as case-control and controlled cohort studies (which authors may have lumped together as a single “observational” category). Our review focuses on the meta-analyses where it was possible to compare the pooled risk ratios (RRs) or odds ratios (ORs) from RCTs against those from other study designs.
Information was collected on the primary objective of the meta-analyses; the adverse effects, study designs, and interventions included; the number of included studies and number of patients by study design; the number of adverse effects in the treatment and control arm or comparator group; and the type of outcome statistic used in evaluating risk of harm.
We relied on the categorisation of study design as specified by the authors of the meta-analysis. For example, if the author stated that they compared RCTs with cohort studies, we assumed that the studies were indeed RCTs and cohort studies.
Validity assessment and data extraction were carried out by one reviewer (S. G.), and checked by a second reviewer (Y. K. L.). All discrepancies were resolved after going back to the original source papers, with full consensus reached after discussion.
The following criteria were used to consider the validity of comparing risk estimates across different study designs. (1) Presence of confounding factors: Discrepancies between the results of RCTs and observational studies may arise because of factors (e.g., differences in population, administration of intervention, or outcome definition) other than study design. We recorded whether the authors of the meta-analysis checked if the RCTs and observational studies shared similar features in terms of population, interventions, comparators, and measurement of outcomes and whether they used methods such as restriction or stratification by population, intervention, comparators, or outcomes to improve the comparability of pooled risk estimates arising from different groups of studies. (2) Heterogeneity by study design: We recorded whether the authors of the meta-analysis explored heterogeneity of the pooled studies by study design (using measures such as Chi2 or I2). We assessed the extent of heterogeneity of each meta-analysis using a cut-off point of p < 0.10 for Chi2 test results, and we specifically looked for instances where I2 was reported as above 50%. In the few instances where both statistics were presented, the results of I2 were given precedence . (3) Statistical analysis comparing study designs: We recorded whether the authors of the meta-analysis described the statistical methods by which the magnitude of the difference between study designs was assessed.
A descriptive summary of the data in terms of confidence interval (CI) overlap between pooled sets of results by study design, and any differences in the direction of effect between study designs, were presented. The results were said to agree if both study designs identified a significant increase, a significant decrease, or no significant difference in the adverse effects under investigation.
Quantitative differences or discrepancies between the pooled estimates from the respective study designs for each adverse effect were illustrated by taking the ratio of odds ratios (ROR) from meta-analysis of RCTs versus meta-analysis of observational studies. We calculated ROR by using the pooled OR for the adverse outcome from RCTs divided by the pooled OR for the adverse outcome from observational studies. If the meta-analysis of RCTs for a particular adverse effect yielded exactly the same OR as the meta-analysis of observational studies (i.e., complete agreement, or no discrepancy between study designs), then the ROR would be 1.0 (and ln ROR = 0). Because adverse events are rare, ORs and RRs were treated as equivalent .
The estimated ROR from each “RCT versus observational study” comparison was then used in a meta-analysis (random effects inverse variance method; RevMan 5.0.25) to summarize the overall ROR between RCTs and observational studies across all the included reviews. The standard error (SE) of ROR can be estimated using the SEs for the RCT and observational estimates:(1)
SEs pertaining to each pooled OR(RCT) and OR(Observ) were calculated from the published 95% CI.
Statistical heterogeneity was assessed using I2 statistic, with I2 values of 30%–60% representing a moderate level of heterogeneity .
In total, 52 articles were identified as potentially eligible for this review. On further detailed evaluation, 33 of these articles either compared different types of observational studies to one another (for example, cohort studies versus case control studies) or compared only the incidence of adverse effects (without reporting the RR/OR) in those receiving the intervention according to type of study ,–.
We finally selected 19 eligible articles that compared the relative risk or ORs from RCTs and observational studies (Figure 1) ,–. These 19 articles covering meta-analysis of 58 separate adverse effects will be the focus of this paper. The 58 meta-analyses included a total of over 311 RCTs and over 222 observational studies (comprising 57 cohort studies, 75 case-control studies, and at least 90 studies described as “observational” by the authors without specifying the exact type) (Table S1). (Exact numbers of RCTs and observational studies cannot be calculated as overlap in the included studies in McGettigan and Henry  could not be ascertained.)
Two of the 19 articles were methodological evaluations with the main aim of assessing the influence of study characteristics (including study design) on the measurement of adverse effects ,, whereas the remaining 17 were systematic reviews within which subgroup analysis by study design was embedded –,– (Table S1).
The majority of the articles compared the results from RCTs and observational studies using only one adverse effect (11/19, 58%) ,,–,,,,,,, whilst three included one type of adverse effect (such as cancer, gastrointestinal complications, or cardiovascular events) ,,, and five articles included a number of specified adverse effects (ranging from two to nine effects) or any adverse effects ,,,,.
Most (17/19, 89%) of the articles included only one type of intervention (such as hormone replacement therapy [HRT] or nonsteroidal anti-inflammatory drugs) –,–, whilst one article looked at two interventions (HRT and oral contraceptives)  and another included nine interventions . Most of the analyses focused on the adverse effects of pharmacological interventions; however, other topics assessed were surgical interventions (such as bone marrow transplantation and hernia operations) , and a diagnostic test (ultrasonography) .
Text S3 lists the 67 studies that were excluded from this systematic review during the screening and data extraction phases, with the reasons for exclusion.
Summary of Methodological Quality
Role of confounding factors.
Although many of the meta-analyses acknowledged the potential for confounding factors that might yield discrepant findings between study designs, no adjustment for confounding factors was reported in most instances ,–,–,–,–. However, a few authors did carry out subgroup analysis stratified for factors such as population characteristics, drug dose, or duration of drug exposure.
There were two instances where the authors of the meta-analysis performed some adjustment for potential confounding factors: one carried out meta-regression , and in the other methodological evaluation the adjustment method carried out was unclear .
Heterogeneity by study design.
Thirteen meta-analyses measured the heterogeneity of at least one set of the included studies grouped by study design using statistical analysis such as Chi2 or I2 ,–,,,–,,–.
The pooled sets of RCTs were least likely to exhibit any strong indication of heterogeneity; only five (15%) ,,, of the 33 ,–,,,,,– sets of pooled RCTs were significantly heterogeneous, and in two of these sets of RCTs the heterogeneity was only moderate, with I2 = 58.9%  and I2 = 58.8% .
Three of the four case-control studies, one of the four cohort studies, and 14 of the 25 studies described as “observational studies” also exhibited substantial heterogeneity.
Statistical analysis comparing study designs.
Authors of one meta-analysis explicitly tested for a difference between the results of the different study designs . Two other analyses reported on the heterogeneity of the pooled RCTs, the pooled observational studies, and the pooled RCTs and observational studies, which can indicate statistical differences where the pooled study designs combined are significantly heterogenous but no significant heterogeneity is seen when the study designs are pooled separately.
Text S4 documents the decisions made in instances where the same data were available in more than one format.
Size of studies.
In ten methodological evaluations the total number of participants was reported in each set of pooled studies by study design ,,–,,,,,, and in another five methodological evaluations the pooled number of participants was reported for at least one type of study design ,,–. Studies described as “observational” by the authors contained the highest number of participants per study, 34,529 (3,798,154 participants/110 studies), followed by cohort studies, 33,613 (1,378,131 participants/41 studies). RCTs and case-control studies had fewer participants, 2,228 (821,954 participants/369 studies) and 2,144 (105,067 participants/49 studies), respectively.
Confidence interval overlap.
In almost all instances the CIs for the pooled results from the different study designs overlapped (Table 1). However, there were four pooled sets of results in three methodological evaluations where the CIs did not overlap ,,.
Agreement and disagreement of results.
In most of the methodological evaluations the results of the treatment effect agreed between types of study design ,,,,,–. Most studies that showed agreement between study designs did not find a significant increase or significant decrease in the adverse effects under investigation (Table 1).
There were major discrepancies in one pooled set of results. Col et al.  found an increase in breast cancer with menopausal hormone therapy in RCTs but a decrease in observational studies.
There were other instances where although the direction of the effect was not in opposing directions, apparently different conclusions may have been reached had a review been restricted to either RCTs or observational studies, and undue emphasis was placed on statistical significance tests. For instance, a significant increase in an adverse effect could be identified in an analysis of RCT data, yet pooling the observational studies may have identified no significant difference in adverse effects between the treatment and control group. Table 1 shows that the most common discrepancy between study types occurred when one set of studies identified a significant increase whilst another study design found no statistically significant difference. Given the imprecision in deriving estimates of rare events, this may not reflect any real difference between the estimates from RCTs and observational studies, and it would be more sensible to concentrate on the overlap of CIs rather than the variation in size of the p-values from significance testing.
Ratio of risk ratio or odds ratios estimates.
RRs or ORs from the RCTs were compared to those from the observational studies by meta-analysis of the respective ROR for each adverse effect.
RCTs versus all “observational” studies.
The overall ROR from meta-analysis using the data from all the studies that compared RCTs with either cohort studies or case-control studies, or that grouped studies under the umbrella of “observational” studies was estimated to be 1.03 (95% CI 0.93–1.15) with moderate heterogeneity (I2 = 56%, 95% CI 38%–67%) (Figure 2).
Studies are listed by first author's last name and year of publication (Loke 2008 is ; AHRQ 2002 is ). In some studies more than one outcome or intervention was assessed. In these instances, indicated by the lowercase letters after the study year, the data were entered in the meta-analysis separately. Other studies compared RCTs to cohort studies and case-control studies separately and therefore are listed twice (with no lowercase letter after the study year).
In Figure 3 we plotted the magnitude of discrepancy (ROR) from each meta-analysis against the precision of its estimates (1/SE), with the contour lines showing the extent of statistical significance for the discrepancy. Values on the x-axis show the magnitude of discrepancy, with the central ln ROR of zero indicating no discrepancy, or complete agreement between the pooled OR estimated from RCTs and observational studies. The y-axis illustrates the precision of the estimates (1/SE), with the data points at the top end having greater precision. This symmetrical distribution of the RORs of the various meta-analyses around the central ln ROR value of zero illustrates that random variation may be an important factor accounting for discrepant findings between meta-analyses of RCTs versus observational studies. If there had been any systematic and consistent bias that drove the results in a particular direction for certain study designs, the plot of RORs would likely be asymmetrical. The vertically tapering shape of the funnel also suggests that the discrepancies between RCTs and observational studies are less apparent when the estimates have greater precision. This may support the need for larger studies to assess adverse effects, whether they are RCTs or observational studies.
Both figures can be interpreted as demonstrating that there are no consistent systematic variations in pooled risk estimates of adverse effects from RCTs versus observational studies.
Sensitivity analysis: limiting to one review per adverse effect examined.
There are no adverse effects for which two or more separate meta-analyses have used exactly the same primary studies (i.e., had complete overlap of RCTs and observational studies) to generate the pooled estimates. This reflects the different time periods, search strategies, and inclusion and exclusion criteria that have been used by authors of these meta-analyses such that even though they were looking at the same adverse effect, they used data from different studies in generating pooled overall estimates. As it turns out, the only adverse effect that was evaluated in more than one review was venous thromboembolism (VTE). There was some, but not complete, overlap of primary studies in three separate reviews of VTE with HRT (involving three overlapping case-control studies from a total of 18 observational studies analysed) and two separate reviews of VTE with oral contraceptives (one overlapping RCT, six [of 13] overlapping cohort studies, and two [of 20] overlapping case-control studies).
For the sensitivity analysis, we removed the three older meta-analyses pertaining to VTE so that the modest overlap could be further reduced, with only one review per specific adverse effect for the sensitivity analysis. The most recent meta-analyses for VTE (Canonico et al.  for VTE with HRT, Douketis et al.  for VTE with oral contraceptives) were used for analysis of the RORs. This yields RORs that are very similar to the original estimates: 1.06 (95% CI 0.96–1.18) for the overall analysis RCTs versus all observational studies, 1.00 (95% CI 0.71–1.42) for RCTs versus case-control studies and 1.07 (95% CI 0.86–1.34) for RCTs versus cohort studies.
Subgroup analysis for comparison of RCTs against specific types of “observational” studies was carried out and is summarised in Table 2. Forest plots for each of these comparisons can be viewed in Figure S1.
Our analyses found little evidence of systematic differences in adverse effect estimates obtained from meta-analysis of RCTs and from meta-analysis of observational studies. Figure 3 shows that discrepancies may arise not just from differences in study design or systematic bias, but possibly because of the random variation, fluctuations or noise, and imprecision in attempting to derive estimates of rare events. There was less discrepancy between the study designs in meta-analyses that generated more precise estimates from larger studies, either because of better quality, or because the populations were more similar (perhaps because large, long-term RCTs capture a broad population similar to observational studies). Indeed, the adverse effects with discrepant results between RCTs and observational studies were distributed symmetrically to the right and left of the line of no difference, meaning that neither study design consistently over- or underestimates risk of harm as compared to the other. It is likely that other important factors such as population and delivery of intervention are at play here—for instance, the major discrepancy identified in Col et al.  for HRT and breast cancer is already well documented. This discrepancy has also been explained by the timing of the start of treatment relative to menopause, which was different between trials and observational studies. After adjustment, the results from the different study designs have been found to no longer differ ,.
Most of the pooled results from the different study designs concurred in terms of identifying a significant increase or decrease, or no significant difference in risk of adverse effects. On the occasions where a discrepancy was found, the difference usually arose from a finding of no significant risk of adverse effects with one study design, in contrast to a significant increase in adverse effects from the other study design. This may reflect the limited size of the included studies to identify significant differences in rare adverse effects.
The increased risk in adverse effects in some studies was not consistently related to any particular study design—RCTs found a significant risk of adverse effects associated with the intervention under investigation in eight instances, while observational studies showed a significantly elevated risk in 11 cases.
Although reasons for discrepancies are unclear, specific factors which may have led to differences in adverse effect estimates were discussed by the respective authors. The differences between observational studies and RCTs in McGettigan and Henry's meta-analysis of cardiovascular risk were thought to be attributable to different dosages of anti-inflammatory drugs used . Differences in Papanikolaou et al.  and Col et al.  were attributed to differing study populations. Other methodological evaluations discussed the nature of the study designs themselves being a factor that may have led to differences in estimates. For example, some stated that RCTs may record a higher incidence of adverse effects because of closer monitoring of patients, longer duration of treatment and follow-up, and more thorough recording, in line with regulatory requirements ,. Where RCTs had a lower incidence of adverse effects, it was suggested that this could be attributed to the exclusion of high-risk patients  and possibly linked to support by manufacturers .
The overall ROR did not suggest any consistent differences in adverse effects estimates from meta-analysis of RCTs versus meta-analysis of observational studies. This interpretation is supported by the funnel plot in Figure 3, which shows that differences between the results of the two study designs are equally distributed across the range. Some discrepancies may arise by chance, or through lack of precision from limited sample size for detecting rare adverse effects. While there are a few instances of sizeable discrepancies, the pooled estimates in Figure 2 and Table 2 indicate that in the scheme of things (particularly where larger, more precise primary studies are available), meta-analysis of observational studies yield adverse effects estimates that broadly match those from meta-analysis of RCTs.
This systematic review of reviews and methodological evaluations has a number of limitations. When comparing the pooled results from different study designs it is important to consider any confounding factors that may account for any differences identified. For instance, if one set of studies was carried out on a younger cohort of patients, with a lower drug dosage, or with shorter duration of use, or relied on passive ascertainment of adverse effects data ,,,, it might be expected that the magnitude of any adverse effects recorded would be lower. However, most of the methodological evaluations were not conducted with the primary aim of assessing differences in study design, but were systematic reviews with some secondary comparative evaluation of study design embedded.
Another constraint of our overview is that we accepted information and data as reported by the authors of the included meta-analyses. We did not attempt to source the primary studies contained in each meta-analysis, as this would have required extracting data from more than 550 papers. For instance, we relied on the authors' categorisation of study design but are aware that authors may not all have used the same definitions. This is a particular problem with observational studies, where it is often difficult to determine the methodology used in the primary study and categorise it appropriately. In order to overcome this limitation, we chose to base our analysis on RCTs compared to “all” observational studies (either cohort studies, case-control studies, or “observational” studies as defined by the author), with a subgroup analysis based on different types of observational designs.
Another important limitation to this review is the potentially unrepresentative sample used. Systematic reviews with embedded data comparing different study designs may have been missed. The search strategy used was limited to a literature search to identify methodological papers whose primary aim was to assess the influence of study design on adverse effects and to a sift of the full text of systematic reviews of adverse effects (as a primary outcome) from the Cochrane Database of Systematic Reviews and DARE. Nevertheless, it should be noted that the Cochrane Database of Systematic Reviews and DARE databases cover a large proportion of all systematic reviews and that systematic reviews in which adverse effects are included as a secondary aim are unlikely to present subgroup analysis by study design for the adverse effects data.
There was considerable heterogeneity between the comparisons of different studies, suggesting that any differences may be specific to particular types of interventions or adverse effects. It may be that particular types of adverse effects can be identified more easily via particular types of study designs ,,,. However, it was difficult to assess the methodological evaluations by type of adverse effects. This would be of interest, given that the literature suggests that RCTs may be better at identifying some types of adverse effects (such as common, anticipated, and short-term) than observational studies.
Where no randomized data exist, observational studies may be the only recourse . However, the potential value of observational data needs to be further demonstrated, particularly in specific situations where existing RCTs are short-term or based on highly selected populations. Comparisons of risk estimates from different types of observational studies (e.g., case-control as opposed to cohort) merit further assessment.
In addition, it would be useful (based on a case-control type of design) to carry out an in-depth examination of the meta-analyses (and their included primary studies) with substantial discrepancy amongst the RCTs and observational studies, as compared to other meta-analyses where RCTs and observational studies had close agreement. Any future research in this area should look into the role of confounding factors (such as different population selection and duration of drug exposure) between studies, and lack of precision in point estimates of risk for rare events that could have accounted for discrepant findings amongst RCTs and observational studies.
Our findings have important implications for the conduct of systematic reviews of harm, particularly with regards to selection of a broad range of relevant studies. Although there are strengths and weaknesses to each study design, empirical evidence from this overview indicates that there is no difference on average between estimates of the risk of adverse effects from meta-analyses of RCTs and of observational studies. Instead of restricting the analysis to certain study designs, it may be preferable for systematic reviewers of adverse effects to evaluate a broad range of studies that can help build a complete picture of any potential harm and improve the generalisability of the review without loss of validity.
Meta-analysis of RORs from RCTs versus cohort studies, case-control studies and studies described as “observational.”
Characteristics of included studies.
Sources searched for included studies.
Example search strategy.
Duplicate data decisions.
We thank Lindsey Myers of the Centre for Reviews and Dissemination (CRD) for her peer review comments on the literature searches and Jane Burch of CRD for her kind assistance in screening the titles and abstracts in the Endnote library. We would also like to thank Lesley Stewart of CRD for comments on an earlier draft.
Conceived and designed the experiments: SG. Performed the study: SG YL. Analyzed the data: SG YL MB. Wrote the paper: SG YL MB. ICMJE criteria for authorship read and met: SG YL MB. Agree with the manuscript's results and conclusions: SG YL MB. Wrote the first draft of the paper: SG.
- 1. Chou R, Helfand M (2005) Challenges in systematic reviews that assess treatment harms. Ann Intern Med 142: 1090–1099.
- 2. Mittmann N, Liu BA, Knowles SR, Shear NH (1999) Meta-analysis and adverse drug reactions. CMAJ 160: 987–988.
- 3. Ioannidis JP, Mulrow CD, Goodman SN (2006) Adverse events: the more you search, the more you find. Ann Intern Med 144: 298–300.
- 4. Levine M, Walter S, Lee H, Haines T, Holbrook A, et al. (1994) User's guides to the medical literature, IV: how to use an article about harm. JAMA 271: 1615–1619.
- 5. Meade MO, Cook DJ, Kernerman P, Bernard G (1997) How to use articles about harm: the relationship between high tidal volumes, ventilating pressures, and ventilator-induced lung injury. Crit Care Med 25: 1915–1922.
- 6. Papanikolaou PN, Christidi GD, Ioannidis JPA (2006) Comparison of evidence on harms of medical interventions in randomized and nonrandomized studies. CMAJ 174: 635–641.
- 7. Price D, Jefferson T, Demicheli V (2004) Methodological issues arising from systematic reviews of the evidence of safety of vaccines. Vaccine 22: 2080–2084.
- 8. Anello C (1996) Does research synthesis have a place in drug regulatory policy? synopsis of issues: assessment of safety and postmarketing surveillance. Clin Res Reg Aff 13: 13–21.
- 9. Jacob RF, Lloyd PM (2000) How to evaluate a dental article about harm. J Prosthet Dent 84: 8–16.
- 10. Kallen BAJ (2005) Methodological issues in the epidemiological study of the teratogenicity of drugs. Congenit Anom (Kyoto) 45: 44–51.
- 11. Pedersen AT, Ottesen B (2003) Issues to debate on the Women's Health Initiative (WHI) study. Epidemiology or randomized clinical trials—time out for hormone replacement therapy studies? Hum Reprod 18: 2241–2244.
- 12. Sackett DL, Haynes RB, Guyatt GH, Tugwell P (1991) Clinical epidemiology. London: Little, Brown and Company.
- 13. Skegg DC (2001) Evaluating the safety of medicines, with particular reference to contraception. Stat Med 20: 3557–3569.
- 14. Brewer T, Colditz GA (1999) Postmarketing surveillance and adverse drug reactions: current perspectives and future needs. JAMA 281: 824–829.
- 15. Egger M, Schneider M, Davey Smith G (1998) Spurious precision? Meta-analysis of observational studies. BMJ 316: 140–144.
- 16. Straus SE, Richardson WS, Glasziou P, Haynes RB (2005) Evidence-based medicine: how to practice and teach EBM. London: Elsevier.
- 17. Rothwell PM (2005) External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet 365: 82–93.
- 18. Edwards JE, McQuay HJ, Moore RA, Collins SL (1999) Reporting of adverse effects in clinical trials should be improved: lessons from acute postoperative pain. J Pain Symptom Manage 18: 427–437.
- 19. Papanikolaou PN, Ioannidis JP (2004) Availability of large-scale evidence on specific harms from systematic reviews of randomized trials. Am J Med 117: 582–589.
- 20. Ioannidis JP, Lau J (2001) Completeness of safety reporting in randomized trials: an evaluation of 7 medical areas. JAMA 285: 437–443.
- 21. Loke YK, Derry S (2001) Reporting of adverse drug reactions in randomised controlled trials—a systematic survey. BMC Clin Pharmacol 1: 3.
- 22. Ioannidis JP, Evans SJ, Gotzsche PC, O'Neill RT, Altman DG, et al. (2004) Better reporting of harms in randomized trials: an extension of the CONSORT statement. Ann Intern Med 141: 781–788.
- 23. Levy JH (1999) Adverse drug reactions in hospitalized patients. The Adverse Reactions Source. Available: http://adversereactions.com/reviewart.htm. Accessed 17 March 2011.
- 24. Papanikolaou PN, Churchill R, Wahlbeck K, Ioannidis JP (2004) Safety reporting in randomized trials of mental health interventions. Am J Psychiatry 161: 1692–1697.
- 25. Ioannidis JP, Contopoulos-Ioannidis DG (1998) Reporting of safety data from randomised trials. Lancet 352: 1752–1753.
- 26. Cuervo GL, Clarke M (2003) Balancing benefits and harms in health care. BMJ 327: 65–66.
- 27. Nuovo J, Sather C (2007) Reporting adverse events in randomized controlled trials. Pharmacoepidemiol Drug Saf 16: 349–351.
- 28. Ethgen M, Boutron I, Baron G, Giraudeau B, Sibilia J, et al. (2005) Reporting of harm in randomized, controlled trials of nonpharmacologic treatment for rheumatic disease. Ann Intern Med 143: 20–25.
- 29. Lee PE, Fischerb HD, Rochonb PA, Gillb SS, Herrmanng N, et al. (2008) Published randomized controlled trials of drug therapy for dementia often lack complete data on harm J Clin Epidemiol 61: 1152–1160.
- 30. Yazici Y (2008) Some concerns about adverse event reporting in randomized clinical trials. Bull NYU Hosp Jt Dis 66: 143–145.
- 31. Gartlehner G, Thieda P, Hansen RA, Morgan LC, Shumate JA, et al. (2009) Inadequate reporting of trials compromises the applicability of systematic reviews. Int J Technol Assess Health Care 25: 323–330.
- 32. Rief W, Nestoriuc Y, von Lilienfeld-Toal A, Dogan I, Schreiber F, et al. (2009) Differences in adverse effect reporting in placebo groups in SSRI and tricyclic antidepressant trials: a systematic review and meta-analysis. Drug Saf 32: 1041–1056.
- 33. Henry D, Hill S (1999) Meta-analysis-its role in assessing drug safety. Pharmacoepidemiol Drug Saf 8: 167–168.
- 34. Buekens P (2001) Invited commentary: rare side effects of obstetric interventions: are observational studies good enough? [comment] Am J Epidemiol 153: 108–109.
- 35. Clarke A, Deeks JJ, Shakir SA (2006) An assessment of the publicly disseminated evidence of safety used in decisions to withdraw medicinal products from the UK and US markets. Drug Saf 29: 175–181.
- 36. Dieppe P, Bartlett C, Davey P, Doyal L, Ebrahim S (2004) Balancing benefits and harms: the example of non-steroidal anti-inflammatory drugs. BMJ 329: 31–34.
- 37. Etminan M, Carleton B, Rochon PA (2004) Quantifying adverse drug events: are systematic reviews the answer? Drug Saf 27: 757–761.
- 38. Gutterman EM (2004) Pharmacoepidemiology in safety evaluations of newly approved medications. Drug Inf J 38: 61–67.
- 39. Hall WD, Lucke J (2006) How have the selective serotonin reuptake inhibitor antidepressants affected suicide mortality? Aust N Z J Psychiatry 40: 941–950.
- 40. Hughes MD, Williams PL (2007) Challenges in using observational studies to evaluate adverse effects of treatment. New Engl J Med 356: 1705–1707.
- 41. Hyrich KL (2005) Assessing the safety of biologic therapies in rheumatoid arthritis: the challenges of study design. J Rheumatol: Suppl 7248–50.
- 42. Jefferson T, Demicheli V (1999) Relation between experimental and non-experimental study designs. HB vaccines: a case study. J Epidemiol Comm Health 53: 51–54.
- 43. Jefferson T, Traversa G (2002) Hepatitis B vaccination: risk-benefit profile and the role of systematic reviews in the assessment of causality of adverse events following immunisation. J Med Virol 67: 451–453.
- 44. Kaufman DW, Shapiro S (2000) Epidemiological assessment of drug-induced disease. Lancet 356: 1339–1343.
- 45. Beard K, Lee A (2006) Introduction. In: Lee A, editor. Adverse drug reactions, 2nd ed. London: Pharmaceutical Press.
- 46. Loke YK, Price D, Herxheimer A, Cochrane Adverse Effects Methods Group (2007) Systematic reviews of adverse effects: framework for a structured approach. BMC Med Res Methodol 7: 32.
- 47. Olsson S, Meyboom R (2006) Pharmacovigilance. In: Mulder GJ, Dencker L, editors. Pharmaceutical toxicology: safety sciences of drugs. London: Pharmaceutical Press. pp. 229–241.
- 48. Ray WA (2003) Population-based studies of adverse drug effects. N Engl J Med 349: 1592–1594.
- 49. Vitiello B, Riddle MA, Greenhill LL, March JS, Levine J, et al. (2003) How can we improve the assessment of safety in child and adolescent psychopharmacology? J Am Acad Child Adolesc Psychiatry 42: 634–641.
- 50. Vandenbroucke JP (2004) Benefits and harms of drug treatments. BMJ 329: 2–3.
- 51. Vandenbroucke JP (2004) When are observational studies as credible as randomised trials? Lancet 363: 1728–1731.
- 52. Vandenbroucke JP (2006) What is the best evidence for determining harms of medical treatment? CMAJ 174: 645–646.
- 53. Aagaard L, Hansen EH (2009) Information about ADRs explored by pharmacovigilance approaches: a qualitative review of studies on antibiotics, SSRIs and NSAIDs. BMC Clin Pharmacol 9: 1–31.
- 54. Ahmad SR (2003) Adverse drug event monitoring at the food and drug administration: your report can make a difference. J Gen Intern Med 18: 57–60.
- 55. Ravaud P, Tubach F (2005) Methodology of therapeutic trials: lessons from the late evidence of the cardiovascular toxicity of some coxibs. Joint Bone Spine 72: 451–455.
- 56. Hordijk-Trion M, Lenzen M, Wijns W, de Jaegere P, Simoons ML, et al. (2006) Patients enrolled in coronary intervention trials are not representative of patients in clinical practice: results from the Euro Heart Survey on Coronary Revascularization. Eur Heart J 27: 671–678.
- 57. Tramer MR, Moore RA, Reynolds DJ, McQuay HJ (2000) Quantitative estimation of rare adverse events which follow a biological progression: a new model applied to chronic NSAID use. Pain 85: 169–182.
- 58. McDonagh M, Peterson K, Carson S (2006) The impact of including non-randomized studies in a systematic review: a case study [abstract]. Abstract P084. 14th Cochrane Colloquium; 2006 October 23-26; Dublin, Ireland.
- 59. Psaty BM, Koepsell TD, Lin D, Weiss NS, Siscovick DS, et al. (1999) Assessment and control for confounding by indication in observational studies. J Am Geriatr Soc 47: 749–754.
- 60. Reeves BC (2008) Principles of research: limitations of non-randomized studies. Surgery 26: 120–124.
- 61. Stricker BH, Psaty BM (2004) Detection, verification, and quantification of adverse drug reactions. BMJ 329: 44–47.
- 62. Vandenbroucke JP (2008) Observational research, randomised trials, and two views of medical science. PLoS Med 5: e67. doi:10.1371/journal.pmed.0050067.
- 63. Britton A, McKee M, Black N, McPherson K, Sanderson C, et al. (1998) Choosing between randomised and non-randomised studies: a systematic review. Health Technol Assess 2: 1–124.
- 64. Concato J, Shah N, Horwitz RI (2000) Randomised, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342: 1887–1892.
- 65. Ioannidis JP, Haidich AB, Pappa M, Pantazis N, Kokori SI, et al. (2001) Comparison of evidence of treatment effects in randomised and non-randomised studies. JAMA 286: 821–830.
- 66. MacLehose RR, Reeves BC, Harvey IM, Sheldon TA, Russell IT, et al. (2000) A systematic review of comparisons of effect sizes derived from randomised and non-randomised studies. Health Technol Assess 4:
- 67. Shepherd J, Bagnall A-M, Colquitt J, Dinnes J, Duffy S, et al. (2006) Sometimes similar, sometimes different: a systematic review of meta-analyses of random and non-randomized policy intervention studies. Abstract P085. 14th Cochrane Colloquium; 2006 October 23-26; Dublin, Ireland.
- 68. Shikata S, Nakayama T, Noguchi Y, Taji Y, Yamagishi H (2006) Comparison of effects in randomized controlled trials with observational studies in digestive surgery. Ann Surg 244: 668–676.
- 69. Oliver S, Bagnall AM, Thomas J, Shepherd J, Sowden A, et al. (2010) Randomised controlled trials for policy interventions: a review of reviews and meta-regression. Health Technol Assess 14: 1–192.
- 70. Berlin JA, Colditz GA (1999) The role of meta-analysis in the regulatory process for foods, drugs and devices. JAMA 28: 830–834.
- 71. Kramer BS, Wilentz J, Alexander D, Burklow J, Friedman LM, et al. (2006) Getting it right: being smarter about clinical trials. PLoS Med 3: e144. doi:10.1371/journal.pmed.0030144.
- 72. Henry D, Moxey A, O'Connell D (2001) Agreement between randomized and non-randomized studies: the effects of bias and confounding [poster presentation]. 9th Chochrane Colloqium. 2001 October 9-13; Lyon, France.
- 73. Martin G (2005) Conflicting clinical trial data: a lesson from albumin. Crit Care 9: 649–650.
- 74. Kim J, Evans S, Smeeth L, Pocock S (2006) Hormone replacement therapy and acute myocardial infarction: a large observational study exploring the influence of age. Int J Epidemiol 35: 731–738.
- 75. McPherson K, Hemminki E (2004) Synthesising licensing data to assess drug safety. BMJ 328: 518–520.
- 76. Petitti DB (1994) Coronary heart disease and estrogen replacement therapy. Can compliance bias explain the results of observational studies? Ann Epidemiol 4: 115–118.
- 77. Posthuma WF, Westendorp RG, Vandenbroucke JP (1994) Cardioprotective effect of hormone replacement therapy in postmenopausal women: is the evidence biased? [comment]. BMJ 308: 1268–1269.
- 78. Jick H, Rodriguez G, Perez-Guthann S (1998) Principles of epidemiological research on adverse and beneficial drug effects. Lancet 352: 1767–1770.
- 79. Perera R, Heneghan C (2008) Interpretating meta-analysis in systematic reviews. Evid Based Med 13: 67–69.
- 80. Davies HTO (1998) Interpreting measures of treatment effect. Hosp Med 59: 499–501.
- 81. Deeks J, Higgins J, Altman DG (2008) Analysing data and undertaking meta-analyses. In: Higgins JP, Green S, editors. Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley and Sons.
- 82. Bager P, Whohlfahrt J, Westergaard T (2008) Caesarean delivery and risk of atopy and allergic disease: meta-analysis. Clin Exp Allergy 38: 634–642.
- 83. Bergendal A, Odlind V, Persson I, Kieler H (2009) Limited knowledge on progestogen-only contraception and risk of venous thromboembolism. Acta Obstet Gynecol Scand 88: 261–266.
- 84. Bollini P, Garcia RLA, Pérez GS, Walker AM (1992) The impact of research quality and study design on epidemiologic estimates of the effect of nonsteroidal anti-inflammatory drugs on upper gastrointestinal tract disease. Arch Int Med 152: 1289–1295.
- 85. Chan WS, Ray J, Wai EK, Ginsburg S, Hannah ME, et al. (2004) Risk of stroke in women exposed to low-dose oral contraceptives: a critical evaluation of the evidence. Arch Intern Med 164: 741–747.
- 86. Chou R, Fu R, Carson S, Saha S, Helfand M (2006) Empirical evaluation of the association between methodological shortcomings and estimates of adverse events. Rockville (Maryland): Agency for Healthcare Research and Quality. Technical Reviews, No. 13.
- 87. Chou R, Fu R, Carson S, Saha S, Helfand M (2007) Methodological shortcomings predicted lower harm estimates in one of two sets of studies of clinical interventions. J Clin Epidemiol 60: 18–28.
- 88. Cosmi B, Castelvetri C, Milandri M, Rubboli A, Conforti A (2000) The evaluation of rare adverse drug events in Cochrane reviews: the incidence of thrombotic thrombocytopenic purpura after ticlopidine plus aspirin for coronary stenting [abstract]. Abstract PA13. 8th Cochrane Colloquium; 2000 October; Cape Town, South Africa.
- 89. Dolovich LR, Addis A, Vaillancourt JM, Power JD, Koren G, et al. (1998) Benzodiazepine use in pregnancy and major malformations or oral cleft: meta-analysis of cohort and case-control studies. BMJ 317: 839–843.
- 90. Garg PP, Kerlikowske K, Subak L, Grady D (1998) Hormone replacement therapy and the risk of epithelial ovarian carcinoma: a meta-analysis. Obstet Gynecol 92: 472–479.
- 91. Gillum LA, Mamidipudi SK, Johnston SC (2000) Ischemic stroke risk with oral contraceptives: a meta-analysis. JAMA 284: 72–78.
- 92. Grady D, Gebretsadik T, Kerlikowske K, Ernster V, Petitti D (1995) Hormone replacement therapy and endometial cancer risk: a meta-analysis. Obstet Gynecol 85: 304–313.
- 93. Henry D, McGettigan P (2003) Epidemiology overview of gastrointestinal and renal toxicity of NSAIDs. Int J Clin Pract: Suppl 13543–49.
- 94. Jensen P, Mikkelsen T, Kehlet H (2002) Postherniorrhaphy urinary retention—effect of local, regional, and general anesthesia: a review. Reg Anesth Pain Med 27: 612–617.
- 95. Johnston SC, Colford JM Jr, Gress DR (1998) Oral contraceptives and the risk of subarachnoid hemorrhage. Neurology 51: 411–418.
- 96. Jones G, Riley M, Couper D, Dwyer T (1999) Water fluoridation, bone mass and fracture: a quantitative overview of the literature. Aust N Z J Public Health 23: 34–40.
- 97. Leipzig RM, Cumming RG, Tinetti ME (1999) Drugs and falls in older people: a systematic review and meta-analysis: I. Psychotropic drugs. J Am Geriatr Soc 47: 30–39.
- 98. Leipzig RM, Cumming RG, Tinetti ME (1999) Drugs and falls in older people: a systematic review and meta-analysis: II. Cardiac and analgesic drugs. J Am Geriatr Soc 47: 40–50.
- 99. Loke YK, Derry S, Aronson JK (2004) A comparison of three different sources of data in assessing the frequencies of adverse reactions to amiodarone. Br J Clin Pharmacol 57: 616–621.
- 100. McGettigan P, Henry D (2006) Cardiovascular risk and inhibition of cyclooxygenase: a systematic review of the observational studies of selective and nonselective inhibitors of cyclooxygenase 2. JAMA 296: 1633–1644.
- 101. Nalysnyk L, Fahrbach K, Reynolds MW, Zhao SZ, Ross S (2003) Adverse events in coronary artery bypass graft (CABG) trials: a systematic review and analysis. Heart 89: 767–772.
- 102. Oger E, Scarabin PY (1999) Assessment of the risk for venous thromboembolism among users of hormone replacement therapy. Drugs Aging 14: 55–61.
- 103. Ross SD, DiGeorge A, Connelly JE, Whitting GW, McDonnell N (1998) Safety of GM-CSF in patients with AIDS: A review of the literature. Pharmacotherapy 18: 1290–1297.
- 104. Salhab M, Al Sarakbi W, Mokbel K (2005) In vitro fertilization and breast cancer risk: a review. Int J Fertil Womens Med 50: 259–266.
- 105. Schwarz EB, Moretti ME, Nayak S, Koren G (2008) Risk of hypospadias in offspring of women using loratadine during pregnancy: a systematic review and meta-analysis. Drug Saf 31: 775–788.
- 106. Scott PA, Kingsley GH, Smith CM, Choy EH, Scott DL (2007) Non-steroidal anti-inflammatory drugs and myocardial infarctions: comparative systematic review of evidence from observational studies and randomised controlled trials. Ann Rheum Dis 66: 1296–1304.
- 107. Siegel CA, Marden SM, Persing SM, Larson RJ, Sands BE (2009) Risk of lymphoma associated with combination anti-tumor necrosis factor and immunomodulator therapy for the treatment of Crohn's disease: a meta-analysis. Clin Gastroenterol Hepatol 7: 874–881.
- 108. Smith JS, Green J, Berrington de Gonzalez A, Appleby P, Peto J, et al. (2003) Cervical cancer and use of hormonal contraceptives: a systematic review. Lancet 36: 1159–1167.
- 109. Takkouche B, Montes-Martínez A, Gill SS, Etminan M (2007) Psychotropic medications and the risk of fracture: a meta-analysis. Drug Saf 30: 171–184.
- 110. Tramer MR, Moore RA, McQuay HJ (1997) Propofol and bradycardia: causation, frequency and severity. Br J Anaesth 78: 642–651.
- 111. Vohra S, Johnston BC, Cramer K, Humphreys K (2007) Adverse events associated with pediatric spinal manipulation: a systematic review. Pediatrics 119: e275–e283.
- 112. Wang T, Collet JP, Shapiro S, Ware MA (2008) Adverse effects of medical cannabinoids: a systematic review. CMAJ 178: 1669–1678.
- 113. Woolcott JC, Richardson KJ, Wiens MO, Patel B, Marin J, et al. (2009) Meta-analysis of the impact of 9 medication classes on falls in elderly persons. Arch Int Med 169: 1952–1960.
- 114. Agency for Healthcare Research and Quality (2002) Hormone replacement therapy and risk of venous thromboembolism. Rockville (Maryland): Agency for Healthcare Research and Quality.
- 115. Alghamdi AA, Moussa F, Fremes SE (2007) Does the use of preoperative aspirin increase the risk of bleeding in patients undergoing coronary artery bypass grafting surgery? Systematic review and meta-analysis. J Card Surg 22: 247–256.
- 116. Browning DR, Martin RM (2007) Statins and risk of cancer: a systematic review and metaanalysis. Int J Cancer 120: 833–843.
- 117. Canonico M, Plu-Bureau G, Lowe GDO, Scarabin PY (2008) Hormone replacement therapy and risk of venous thromboembolism in postmenopausal women: systematic review and meta-analysis. BMJ 336: 1227–1231.
- 118. Capurso G, Schünemann HJ, Terrenato I, Moretti A, Koch M, et al. (2007) Meta-analysis: the use of non-steroidal anti-inflammatory drugs and pancreatic cancer risk for different exposure categories. Aliment Pharmacol Ther 26: 1089–1099.
- 119. Col NF, Kim JA, Chlebowski RT (2005) Menopausal hormone therapy after breast cancer: a meta-analysis and critical appraisal of the evidence. Breast Cancer Res 7: R535–R540.
- 120. Cutler C, Giri S, Jeyapalan S, Paniagua D, Viswanathan A, et al. (2001) Acute and chronic graft-versus-host disease after allogeneic peripheral-blood stem-cell and bone marrow transplantation: a meta-analysis. J Clin Oncol 19: 3685–3691.
- 121. Douketis JD, Ginsberg JS, Holbrook A, Crowther M, Duku EK, et al. (1997) A reevaluation of the risk for venous thromboembolism with the use of oral contraceptives and hormone replacement therapy. Arch Int Med 157: 1522–1530.
- 122. Koster T, Small RA, Rosendaal FR, Helmerhorst FM (1995) Oral contraceptives and venous thromboembolism: a quantitative discussion of the uncertainties. J Intern Med 238: 31–37.
- 123. Loe SM, Sanchez-Ramos L, Kaunitz AM (2005) Assessing the neonatal safety of indomethacin tocolysis: a systematic review with meta-analysis. Obstet Gynecol 106: 173–179.
- 124. Loke YK, Singh S, Furberg CD (2008) Long-term use of thiazolidinediones and fractures in type 2 diabetes: A meta-analysis. CMAJ 180: 32–39.
- 125. MacLennan SC, MacLennan AH, Ryan P (1995) Colorectal cancer and oestrogen replacement therapy. A meta-analysis of epidemiological studies. Med J Aust 162: 491–493.
- 126. McAlister FA, Clark HD, Wells PS, Laupacis A (1998) Perioperative allogenic blood transfusion does not cause adverse sequelae in patients with cancer: a meta-analysis of unconfounded studies. Br J Surg 85: 171–178.
- 127. McGettigan P, Henry D (2008) Cardiovascular ischaemia with anti-inflammarory drugs [oral presentation].
- 128. Ofman JJ, MacLean CH, Straus WL, Morton SC, Berger ML, et al. (2002) A meta-analysis of severe upper gastrointestinal complications of non-steroidal anti-inflammatory drugs. J Rheumatol 29: 804–812.
- 129. Scott PA, Kingsley GH, Scott DL (2008) Non-steroidal anti-inflammatory drugs and cardiac failure: meta-analysis of observational studies and randomised controlled trials. Eur J Heart Fail 10: 1102–1107.
- 130. Singh S, Loke YK, Furberg CD (2007) Thiazolidinediones and heart failure: A teleo-analysis. Diabetes Care 30: 2148–2153.
- 131. Torloni MR, Vedmedovska N, Merialdi M, Betran AP, Allen T, et al. (2009) Safety of ultrasonography in pregnancy: WHO systematic review of the literature and meta-analysis. Ultrasound Obstet Gynecol 33: 599–608.
- 132. Prentice R, Chlebowski R, Stefanick M, Manson J, Langer R, et al. (2008) Conjugated equine estrogens and breast cancer risk in the Women's Health Initiative clinical trial and observational study. Am J Epidemiol 167: 1407–1415.
- 133. Vandenbroucke J (2009) The HRT controversy: observational studies and RCTs fall in line. Lancet 373: 1233–1235.
- 134. Levine MA, Hamet P, Novosel S, Jolain B (1997) A prospective comparison of four study designs used in assessing safety and effectiveness of drug therapy in hypertension management. Am J Hypertens 10: 1191–1200.
- 135. Ross SD (2001) Drug-related adverse events: a readers' guide to assessing literature reviews and meta-analyses. Arch Int Med 161: 1041–1046.
- 136. Sutton AJ, Cooper NJ, Lambert PC, Jones DR, Abrams KR, et al. (2002) Meta-analysis of rare and adverse event data. Expert Rev Pharmacoecon Outcomes Res 2: 367–379.
- 137. Loke YK, Trivedi AN, Singh S (2008) Meta-analysis: gastrointestinal bleeding due to interaction between selective serotonin uptake inhibitors and non-steroidal anti-inflammatory drugs. Aliment Pharmacol Ther 27: 31–40.