Skip to main content
Download PDF

Quantitative assessment of baseline imbalances in evolocumab and alirocumab trials: a meta-epidemiological study

BMC Medical Research Methodology volume 24, Article number: 137 (2024) Cite this article

Abstract

Background

Baseline imbalances have been identified in randomized trials of evolocumab and alirocumab. Our aim was to quantitatively assess (1) the presence of systematic baseline differences, and (2) the relationship of baseline differences with effects on low-density lipoprotein-cholesterol (LDL-c) and clinical outcomes in the trials.

Methods

We performed a meta-epidemiological study. PubMed, Embase, regulatory reports, ClinicalTrials.gov and company websites were searched for trials. Seven baseline characteristics (mean age, LDL-c, BMI, percentage males, diabetics, smokers, and hypertensives) and five outcomes (LDL-c, major adverse cardiac events, serious adverse events, any adverse events, all-cause mortality) were extracted. We calculated (1) range and distribution of baseline imbalances (sign-test), (2) pooled baseline differences and heterogeneity (meta-analysis), (3) differences in SDs around continuous variables (sign-test and pooling), and (4) the relationship of baseline differences with outcomes (meta-regression). The comparisons of PCSK9-inhibitor groups with either placebo or ezetimibe were analysed separately and combined.

Results

We identified 43 trials with 63,193 participants. Baseline characteristics were frequently missing. Many trials showed small baseline imbalances, but some large imbalances. Only baseline BMI showed a statistically significant lower pooled mean for the drug versus placebo groups (MD -0.16; 95% CI -0.24 to -0.09). Heterogeneity in baseline imbalances was present in six placebo- and five ezetimibe-comparisons. Heterogeneity was statistically significant for BMI, males, diabetics and hypertensives in the combined comparisons. There was a statistically significant preponderance for larger SDs in the PCSK9-inhibitor versus control groups (sign-test age 0.014; LDL-c 0.014; BMI 0.049). Meta-regression showed clinically relevant relationships of baseline imbalances in age, BMI and diabetics with the risk of any adverse events and the risk of mortality. Two relationships were statistically significant: A higher mean BMI in the drug versus control group with a decreased risk of mortality (beta − 0.56; 95% CI -1.10 to -0.02), and a higher proportion of diabetics with an increased risk of any adverse events (beta 0.02; 95% 0.01 to 0.04).

Conclusions

Heterogeneous baseline imbalances and systematically different SDs were present in evolocumab and alirocumab trials, so study groups cannot be assumed to be comparable. These findings raise concerns about the design and conduct of the randomization procedures.

Peer Review reports

Background

Randomization is a core feature of a clinical trial. The goal of randomization is to balance known and unknown prognostic patient characteristics across the intervention and control group. If the groups are incomparable at baseline, a difference between the groups in outcomes at the end of the trial cannot be attributed to the intervention. Despite adequate randomization procedures, baseline imbalances may nevertheless occur due to chance especially in small trials. Also, inadequate design or application of randomization procedures may lead to systematic baseline imbalances [1, 2].

Baseline imbalances have been observed in trials of evolocumab and alirocumab [3]. These PCSK9 inhibitors were registered in 2015 when multiple lipid-lowering trials had shown that they reduce cholesterol effectively. Subsequently, clinical outcomes trials were performed to test whether the drugs decrease the risk of myocardial infarction, stroke, and death. The FOURIER trial tested evolocumab in 27,564 patients, and the ODYSSEY OUTCOMES trial tested alirocumab in 18,924 patients [4, 5].

A Cochrane review of these trials has shown small absolute effects of PCSK9 inhibitors on clinical outcomes. Alirocumab reduced the risk of cardiovascular disease compared to placebo (OR 0.87; 95% CI 0.80–0.94; RD -0.02; 95% CI -0.02 to -0.01), and the risk of all-cause mortality (OR 0.83; 95% CI 0.72–0.96; RD -0.01; 95% CI -0.01 to 0.00) [6]. Evolocumab also reduced the risk of cardiovascular disease compared to placebo (OR 0.84; 95% CI 0.78–0.91; RD -0.02; 95% CI -0.02 to -0.01) but not the risk of all-cause mortality risk (OR 1.04; 95% CI 0.91–1.19; RD 0.00; 95% CI -0.00 to 0.01). Neither drug affected the risk of cardiovascular disease or all-cause mortality risk more than ezetimibe. The clinical outcomes trials had a large weight in the meta-analyses of this Cochrane review.

However, the clinical outcomes trials of evolocumab and alirocumab have shown baseline imbalances. FOURIER reported a baseline difference in mean body weight (85.0 kg in the evolocumab group versus 85.5 kg in the placebo group) and in the percentage smokers (28.0% versus 28.5%) [4]. ODYSSEY OUTCOMES reported a baseline difference in the percentage of participants with hypertension (65.6% in the alirocumab group versus 63.9% in the placebo group) [5]. Smaller trials that tested the efficacy of PCSK9 inhibitors in terms of LDL-c lowering also reported baseline imbalances [7, 8]. Baseline differences should be adjusted for in the analyses of trials to avoid biased effects, but this is not commonly done [3, 9, 10].

Although baseline differences in trials are usually small, they may be relevant for several reasons. First, multiple small baseline differences in favor of the intervention group may together result in a biased overestimation of the treatment effect in the same direction, especially if there are few imbalances in favor of placebo. Secondly, pooling results from trials with comparable systematic baseline imbalances might lead to biased pooled effects [11,12,13]. Thirdly, an imbalance of a given absolute size will have a greater effect on the estimated effect in a large than a small study [14]. Fourthly, the presence of baseline imbalances raises the question whether the randomization procedure in the trial was adequately designed and executed.

Quantitative assessment of baseline imbalances might identify systematic baseline imbalances that could have affected the estimated treatment effect [13, 15]. Various methods for evaluating potential baseline imbalances in a set of trials have been documented. These methods involve meta-analysis of baseline differences, evaluation of heterogeneity in baseline differences, and meta-regression [2, 13, 15]. Especially age may be an important baseline variable in evaluating baseline imbalances [2]. The aim of this meta-epidemiological study was to assess the presence of systematic baseline imbalances in trials of evolocumab and alirocumab, and their association with the reduction of LDL-c and the risk of cardiovascular outcomes.

Methods

We performed a meta-epidemiological study of randomized trials that compared evolocumab and alirocumab with placebo or ezetimibe among adult patients with hypercholesterolemia at an increased risk of cardiovascular disease. We reported this study following the guideline for reporting meta-epidemiological studies (see online supplement) [16].

Search and selection

We used three sources to find phase 2 and phase 3 randomized trials of evolocumab and alirocumab. We searched the bibliographies PubMed and Embase with the terms ‘evolocumab, alirocumab and ‘placebo or ezetimibe’. Next, we checked the references of FDA reports, EMA reports, and a Cochrane review [17,18,19,20,21,22]. Cochrane reviews pay particular attention to identifying all available trials, also from the grey literature, to avoid publication bias. Finally, we looked for trials that were registered on ClinicalTrials.gov, and websites of the pharmaceutical companies manufacturing the before mentioned drugs. Our search was rerun for the last time in March 2023.

If a title and abstract suggested an eligible trial, we assessed the full text publication or protocol. We selected trials that were: (1) randomized, (2) placebo- and/or ezetimibe-controlled, and (3) performed among adult patients at an increased risk of cardiovascular disease. Phase 1, head-to-head, and open label (extension) trials were excluded. Language and publication date were not exclusion criteria. No study protocol was registered (see online supplement).

Data extraction

Two independent reviewers (FvB and HJL) extracted the data with a standardized data form. First, we registered general study characteristics including investigated drug, number of participants, randomization procedure (method of random sequence generation and allocation concealment) and whether baseline imbalances had been adjusted for.

Secondly, we extracted the following baseline characteristics per treatment group: mean age (SD), percentage of males, mean LDL-c concentration (SD), body mass index (BMI) (SD), percentage with diabetes mellitus (DM), percentage of smokers, and percentage with hypertension. We chose these parameters because they are associated with the level of LDL-c serum concentration or the occurrence of cardiovascular disease, which were the primary outcomes of the lipid-lowering trials respectively clinical outcomes trials. When a standard deviation (SD) was missing, we used other reported data (e.g. the standard error) if available to calculate it [23].

Thirdly, we extracted various clinical outcomes per treatment group. We extracted change in LDL-c from baseline to the first measurement (usually at 12 weeks) after start of the treatment (in mg/l). When LDL-c change was reported as a percentage, we converted it to the absolute change. We also extracted the number of positively adjudicated major adverse cardiovascular events (MACE) as defined by the trial investigators. Finally, the number of any adverse events, serious adverse events (SAE) and all-cause deaths were extracted. SAE include all serious disease - both targeted and unintended - that is potentially fatal or causes permanent health damage [24, 25].

We included data from all randomized participants, except those in PCSK9 groups that differed from the comparison group in another way than just the allocated intervention. For instance, some trials used different doses of background statin therapy for the PCSK9 inhibitor and the comparison group. Intervention groups that received other active drugs than the PCSK9 inhibitors or ezetimibe were also excluded. If multiple dosages of the PCSK9 inhibitor were investigated in a trial, we combined the groups. The same applied to multiple placebo groups.

Our primary source of information for the baseline and outcome data were the published papers of the trials. If baseline data or outcomes data were not available in published papers, we used the data published at ClinicalTrials.gov. Disagreements about the data were resolved in consensus meetings.

Statistical analysis

We performed three types of analyses to examine the presence of systematic baseline imbalances. First, we described the range and distribution of the baseline imbalances of the PCSK9 inhibitor groups (evolocumab or alirocumab) versus the comparison groups (placebo or ezetimibe). A baseline difference was present if the intervention group differed from the control group in terms of the figures (and number of decimals) presented by the authors. We run a one-sided sign-test for each baseline characteristic to find out if the direction of the baseline imbalance was more often positive or negative than could be expected by chance.

A sign-test takes into account the number of the comparisons and the direction of the baseline differences, but not the absolute size of the differences. The sign-test automatically excludes studies with no baseline imbalance and studies with missing baseline differences.

Secondly, we performed meta-analyses to calculate the pooled mean difference (MD) with a 95% CI for baseline age, LDL-c and BMI, and pooled risk difference (RD) for percentage males, percentage participants with DM, percentage smokers and percentage participants with hypertension between treatment groups. We used fixed effects models, because heterogeneity for pooled baseline differences should be 0% [2]. The analysis generated an I2-statistic for heterogeneity, and a p-value. We calculated 95% confidence intervals around I2 with the direct command heterogi in Stata. Trials with missing data were omitted from the analysis.

Thirdly, we described the range and distribution of the SDs around mean age, LDL-c and BMI of the PCSK9 inhibitor groups versus the comparison group. Again, we run a one-sided sign-test for the direction of imbalances. Next, we calculated pooled SDs with the standard formula to investigate whether SDs of the intervention and control groups differed systematically [26]. We performed a t-test for means between two groups to investigate systematic differences between pooled SDs. In addition, we used Levene’s test for differences between SDs in individual trials and describe the distribution of the generated p-values, which can be expected to vary evenly between 0 and 1.

We ran the abovementioned analyses for the placebo-controlled and ezetimibe-controlled trials separately, because we expected that systematic baseline differences might be different between those types of trials. This assumption was based on the findings of a study on baseline imbalances in placebo-controlled trial testing atypical antipsychotics in dementia. Some of these trials had an additional (third) haloperidol group, and these exhibited larger pooled imbalances than trials without this extra group [15]. If a trial had a placebo and an ezetimibe arm, the placebo arm was used in the analysis of placebo-comparisons and the ezetimibe arm in ezetimibe-comparisons. In addition, we rerun the analyses of the baseline differences for alirocumab and evolocumab trials separately, and we checked whether the pooled baseline differences and pooled SDs of the placebo-controlled trials might have been driven primarily by the (very) large clinical outcomes trials.

Finally, we performed univariate meta-regression analyses to evaluate the relationship of an individual baseline imbalance with the effects on clinical outcomes. We combined the data from all trials because the direction of the effect can be expected to be independent of type of control drug. We used random effects meta-regression as generally recommended [23]. As the SAE data of clinical outcomes trials were not reported according to MedDRA standards [27, 28], they were omitted from these meta-regression analyses. We performed a sensitivity analysis excluding the clinical outcomes trials at the request of a reviewer.

Results

Our search yielded 51 potentially eligible trials (Fig. 1). We found that all the planned trials reported in the FDA reviews have been published. After full-text assessments, we included 43 completed trials with 63,193 participants [4, 5, 7, 8, 28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. Among the included trials, there were 26 placebo-controlled trials, 13 ezetimibe-controlled trials, and 4 had a placebo and ezetimibe group (Table 1). The placebo-controlled trials included two clinical outcomes studies: FOURIER (evolocumab) and ODYSSEY OUTCOMES (alirocumab). Hence, the trials produced 30 placebo comparisons and 13 ezetimibe comparisons.

Fig. 1
figure 1

Flow diagram of literature search and study selection

Full size image

Study characteristics

Twenty trials tested evolocumab in 36,050 patients and 23 alirocumab in 27,143 patients (Table 1). The majority of trials was conducted in patients with primary hypercholesterolemia, homozygous or heterozygous familiar hypercholesterolemia, or a combination. Most patients already used a statin, or were prescribed statins before randomization.

The methods for random sequence generation were reported in 5 of 43 trials, and for allocation concealment in 13 trials (Table 1). If described, the methods mainly concerned an interactive voice response system (IVRS) or interactive web response system (IWRS). Baseline imbalances were not adjusted for in any of the trials. Baseline age, sex and LDL-c was reported for all 30 placebo comparisons, but BMI for just a part of them (22/30), as was DM (24/30), smoking (18/30) and hypertension (17/30). The two clinical outcomes trials reported all seven baseline characteristics. Baseline age, sex, LDL-c and diabetes was reported for all 13 ezetimibe comparisons, but BMI (9/13 trials), smoking (7/13) and hypertension (10/13) for just a part of them.

Range and distribution of baseline differences

Table 2 shows the range and distribution of the imbalances in the seven patient characteristics of interest. Most placebo comparisons showed small imbalances, but some large imbalances, as represented by large ranges for difference in percentage males (-19.6 to 25.8), mean LDL-c (-7.7 to 35.4), mean BMI (-1.5 to 1.5), percentage DM (-12.6 to 13.2), percentage smokers (-12.6 to 7.6) and percentage hypertension (-24.0 to 13.7). The ezetimibe comparisons showed small ranges in imbalances for mean age, mean LDL-c, and percentage smoking, but larger ranges for percentage males (-11.4 to 7.2), mean BMI (-0.7 to 1.7), percentage DM (-9.4 to 17.4), and percentage hypertension (-11.5 to 10.0).

Table 2 also presents the distribution of positive and negative baseline differences for the compared groups. Statistically significantly more trials had a higher percentage of men in the drug group compared to the placebo group (20 versus 9; p = 0.03). Although numerically more trials had a higher mean LDL-c in the drug group compared to the placebo group (18 vs. 10; p = 0.09), no other statistically significantly divergent distributions were found.

Table 1 Characteristics of included alirocumab and evolocumab trials
Full size table
Table 2 Range and direction of baseline differences in evolocumab and alirocumab trials
Full size table

Pooled baseline differences and heterogeneity

Table 3 shows the pooled baseline differences between the drug and control groups. Pooled baseline differences were mostly small. The pooled difference in BMI was statistically significant in the placebo comparisons (-0.16; 95% CI -0.24 to -0.09) and all comparisons (-0.15; 95% CI -0.22 to -0.07). The baseline difference in BMI in the FOURIER trial drove the statistical significance of these differences, but the pooled difference was present in the smaller placebo-controlled lipid lowering trials as well (-0.18; 95% CI -0.42 to 0.06). The difference in proportion of participants with hypertension was statistically significant for all comparisons (0.01; 95% CI 0.00 to 0.02). Again, the pooled difference in proportion of participants with hypertension was statistically significant due to the baseline imbalance in the ODYSSEY OUTCOMES trial (35% of weight), but the smaller lipid-lowering trials showed a similar imbalance (0.01; 95% CI -0.02 to 0.03).

Table 3 also shows the heterogeneity in the baseline imbalances. In the placebo comparisons, six of seven pooled baseline differences had a heterogeneity > 0% when there should be none. The heterogeneity was statistically significant for the difference in proportion of males (I2 41%; 95% CI 9–62; p = 0.011). In the ezetimibe comparisons, five of seven baseline differences showed heterogeneity > 0%. The heterogeneity was statistically significant for difference in the proportion with DM (I2 50%; 95% CI 5–74; p = 0.020). For the combined comparisons, five of seven pooled baseline differences showed heterogeneity. This heterogeneity was statistically significant for imbalances in the proportion of males (I2 31%; 95% CI 0–53; p = 0.029, mean BMI (I2 33%; 95% CI 0–57; p = 0.039), proportion of participants with DM (I2 32%; 95% CI 0–55; p = 0.035) and proportion of participants with hypertension (I2 36%; 95% CI 0–60; p = 0.033).

Imbalances in SDs

All SDs around means of age, LDL-c were reported, but the means of BMI and corresponding SDs were missing for 12 of 43 comparisons. Table 4 shows the range in imbalances in SDs. In some studies, the SDs differed between the drug and control groups: the differences for SDs of age varied between − 1.7 and 3.0 years in the placebo comparisons and − 2.1 to 2.7 years in ezetimibe comparisons. For the differences in SDs of LDL-c the ranges were − 21.2 to 23.7 mg/dl respectively − 15.7 to 15.8 mg/dl, and for differences in SDs of BMI − 0.8 to 1.1 kg/m2 respectively − 1.5 to 1.7 kg/m2.

Table 4 also shows that statistically significantly more often drug groups had a larger SD than the placebo group around mean age (21 versus 8; p = 0.012) and mean LDL-c (20 versus 8; p = 0.018). For ezetimibe comparisons, numerically but not statistically significantly more drug groups had a larger SD for all three variables. All comparisons together showed that the drug groups had a larger SD than the control group more often than expected for age (28 versus 13; p = 0.01), LDL-c (28 versus 13; p = 0.014) and BMI (20 versus 10; p = 0.049).

Table 5 presents the pooled SDs of age, LDL-c and BMI for the drug and control groups. The pooled SD of the drug groups was larger than the pooled SD of the control groups in eight of nine analyses, and smaller in one analysis. None of the differences was statistically significant. The 28 smaller lipid-lowering trials and 2 clinical outcomes trials showed similar differences in SDs between drug and control group, with one exception that the SDs of LDL-c in the groups of the clinical outcomes trials were the same (supplemental Table 5).

Figure 2 shows the distribution of p-values generated by Levene’s test for the difference between SDs of the study groups per trial. The p-values for the SDs of age seemed fairly evenly distributed between 0 and 1. The distributions for SDs of LDL-c and BMI were skewed to the left: the p-value was below 0.05 in 10 of 43 comparisons (23%) for the former, and in 6 of 31 comparisons (19%) for the latter.

Alirocumab and evolocumab separately

The alirocumab and evolocumab trials showed similar patterns for baseline differences and heterogeneity as the main results (supplemental Table 1). Nevertheless, in the alirocumab trials, heterogeneity was statistically significantly high for the baseline difference in the proportion of males (42%; 95% CI 4–65; p = 0.02) and the proportion of DM (I2 45%; 95% CI 6–67; p = 0.02) (supplemental Table 2). The alirocumab and evolocumab trials also showed similar patterns in the directions of differences in SDs and in the pooled SDs as seen in the main results (supplemental Tables 3 and supplemental Table 4).

Table 3 Pooled baseline differences with heterogeneity in evolocumab and alirocumab trials
Full size table
Table 4 Range and direction of differences between standard deviations around baseline means in evolocumab and alirocumab trials
Full size table
Table 5 Pooled standard deviations around baseline means in evolocumab and alirocumab trials
Full size table
Table 6 Relationship of baseline imbalances with clinical outcomes in evolocumab and alirocumab trials
Full size table
Fig. 2
figure 2

Histogram of p-values of Levene’s test for differences in SDs around (A) mean age, (B) mean LDL-c, and (C) mean BMI; left panel represents p-values by control group (ezetimibe or placebo); right panel represents p-values for all comparisons

Full size image

Association with clinical outcomes

Table 6 presents the association between baseline differences and clinical outcomes. Most baseline differences did not show a statistically significant association with the risk of the clinical outcomes, but various associations seemed strong. E.g., a one-year higher mean age in the drug than control group was associated with a 16% higher relative risk of all-cause mortality (beta 0.16; 95% CI -0.25 to 0.58). A one-point higher mean BMI in the drug versus control group was associated with an 8% lower relative risk of any adverse events (beta − 0.08; 95% CI -0.25 to 0.08), and a 56% lower relative risk of all-cause mortality (beta − 0.56; 95% CI -1.10 to -0.02). In addition, 1% more diabetic patients in the drug versus control group was statistically significantly associated with a 2% higher relative risk of any adverse events (beta 0.02; 95% CI 0.01 to 0.04).

When excluding the two clinical outcomes trials from the meta-regression analysis, the effect of a higher BMI in the intervention versus control group became smaller and was not statistically significant anymore (supplemental Table 6). The other results did not change substantially.

Discussion

In our study of 43 randomized trials of evolocumab or alirocumab, many trials showed baseline imbalances, but they were not adjusted for. Pooled baseline imbalances were small but showed high heterogeneity for BMI, DM, smoking and hypertension, when none is expected. In addition, statistically significantly more trials showed PCSK9 inhibitor groups with larger SDs around age, BMI and LDL-c than the control groups. A higher mean BMI in the PCSK9 inhibitor versus control group was associated with a decreased risk of all-cause mortality, and a higher proportion of diabetic patients with an increased risk of any adverse event.

Our study showed that randomisation has not produced comparable groups in alirocumab and evolocumab trials. Some individual trials showed clear clinically relevant imbalances, and in many trials, the drug groups had higher SDs for age, BMI and LCL-c than the control group. The discrepancy in SDs is related to an uneven distribution of participants over the groups, even if the means are (almost) similar. Box 1 illustrates that a somewhat lower mean age and a higher SD in the drug versus control group reflects more young people in the former than is the latter. If the investigated drug is effective in young people and not in older people, this distribution favours the drug group.

figure a

Incomparable groups

To our knowledge, only a few other meta-epidemiological studies have also assessed pooled baseline imbalances and heterogeneity in randomized trials. One study focused on imbalances in age and blood pressure in 391 trials about exercise and antihypertensive medication [67]. There was a statistically significant pooled difference in age favouring the intervention over the control groups. Also, there was heterogeneity for 16 of 29 comparisons. Another study investigated baseline imbalances in age in 503 trials included in 12 published meta-analyses [68]. Two meta-analyses showed a significantly higher mean age in the intervention than the control group, and five heterogeneity in age differences. Yet another study reported small pooled baseline imbalances, but heterogeneity in 4 of 6 investigated imbalances in 23 trials about antipsychotics in dementia [15]. We could not identify other meta-epidemiological studies that examined baseline differences in SDs.

Our study also showed that baseline imbalances may affect the validity of the treatment effect of PCSK9 inhibitors reported in systematic reviews [69,70,71]. In particular, imbalances in BMI and proportion of diabetic patients seemed to have influenced effects on safety outcomes. One or both factors showed imbalances in the clinical outcomes trials, which had very high weights in the meta-analyses [69,70,71]. These factors should have been adjusted for in the original trials [14]. Moreover, as obesity and diabetes are related, they will not have been independently distributed in the trial populations.

Implications

The heterogeneity in baseline imbalances and differences in SDs raise concerns about the adequacy of randomization methods in the included alirocumab and evalocumab trials. In the context of randomization, one does not expect heterogeneity, because the goal of randomization is to not get baseline differences between groups (‘no effect’), let alone differences in those differences. SDs may differ between study groups by chance, but Levene’s test showed that more than 1 in 20 trials had a statistically significant difference in SDs of BMI and LDL-c. This is especially disconcerting because 34 of the 43 trials had more participants in the drug versus control group, often twice as many, and SDs decrease with an increasing number of persons. Only 30% of trials reported the use of a central computerized systems for randomisation, which is currently considered the gold standard. The lack of information about the randomization procedures in the majority of trials hinders a detailed assessment of the quality of the randomisation design and its relation to our findings.

Our findings suggest the need for objective assessment of risk of bias as part of systematic reviews. Published systematic reviews about PCSK9 inhibitors trials used conventional risk of bias tools [69,70,71], and the risk of bias in the randomization domain was considered low in most or all trials. In our study, incomplete or missing information on random sequence generation and allocation concealment in the included trials implies an uncertain risk of bias in the randomization method, leading to an overall uncertain risk of bias.

For objective assessment, imbalances in prognostic factors need to be examined, including age, which is a potent predictor of clinical outcomes in many trials and seldom not reported [2]. Another option is to study baseline p-value distributions in all reported continuous and categorical variables [72]. In addition, methods to adjust for baseline imbalances in meta-analyses have been proposed [13, 73]. To enable the evaluation of the quality of randomization procedures and its association with baseline imbalances, authors need to describe the procedures and all prognostic baseline characteristics per study group according to the CONSORT guidelines for reporting randomized trials.

Strengths and limitations

A strength of this study is that we used an objective method to identify risk of bias due baseline imbalances. This approach may be a valuable addition to standard assessments with a risk of bias tool, which often yield discrepant results [74, 75].

A limitation of our study is that our analyses depended on the availability of baseline data in the publications. This was often lacking for BMI, DM and smoking. Another limitation of our study was the number of included trials. If many more studies had been available, a multivariate meta-regression analysis could have performed to study whether baseline imbalances were related to each other. A final limitation is that we did not correct for multiple-testing. Although some authors have made suggestions to overcome multiple testing in standard reviews [76], no formal guideline for correction of multiple testing in meta-epidemiological studies has been proposed. Consequently, we may have identified statistically significant results by chance, especially in the meta-regression analyses. Nevertheless, the consistency in the statistically significant differences observed for most standard deviations cannot be explained by multiple testing.

Conclusion

Clinically relevant baseline imbalances in evolocumab and alirocumab trials were not adjusted for and may have biased the measured effects on key clinical outcomes. In addition, heterogeneity in baseline imbalances and systematic differences in SDs between study groups in PCSK9 inhibitor trials raise concerns about the effectiveness of the randomization procedures. Baseline characteristics of the study groups can be used to assess risk of bias in trials objectively as part of systematic reviews.

Data availability

All data from the included studies are publicly available.

References

  1. Hróbjartsson A, Boutron I, Turner L, Altman DG, Moher D. Assessing risk of Bias in Randomised clinical trials included in Cochrane Reviews: the why is Easy, the how is a challenge. In: Cochrane Database of Systematic Reviews; 2013.

  2. Clark L, Fairhurst C, Cook E, Torgerson DJ. Important outcome predictors showed greater baseline heterogeneity than age in two systematic reviews. J Clin Epidemiol. 2015;68:175–81.

    Article  PubMed  Google Scholar 

  3. van Bruggen FH, Nijhuis GBJ, Zuidema SU, Luijendijk HJ. Serious adverse events and deaths in PCSK9 inhibitor trials reported on ClinicalTrials.gov: a systematic review. Expert Rev Clin Pharmacol 2020;Jul 13:787–96.

  4. Sabatine MS, Giugliano RP, Keech AC, Honarpour N, Wiviott SD, Murphy SA, et al. Evolocumab and Clinical outcomes in patients with Cardiovascular Disease. N Engl J Med. 2017;376:1713–22.

    Article  CAS  PubMed  Google Scholar 

  5. Schwartz GG, Steg PG, Szarek M, Bhatt DL, Bittner VA, Diaz R, et al. Alirocumab and Cardiovascular outcomes after Acute Coronary Syndrome. N Engl J Med. 2018;379:2097–107.

    Article  CAS  PubMed  Google Scholar 

  6. Schmidt AF, Carter JPL, Pearce LS, Wilkins JT, Overington JP, Hingorani AD et al. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews. 2020;2020.

  7. Cannon CP, Cariou B, Blom D, McKenney JM, Lorenzato C, Pordy R, et al. Efficacy and safety of alirocumab in high cardiovascular risk patients with inadequately controlled hypercholesterolaemia on maximally tolerated doses of statins: the ODYSSEY COMBO II randomized controlled trial. Eur Heart J. 2015;36:1186–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Robinson JG, Nedergaard BS, Rogers WJ, Fialkow J, Neutel JM, Ramstad D, et al. Effect of evolocumab or ezetimibe added to moderate- Or high-intensity statin therapy on LDL-C lowering in patients with hypercholesterolemia: the LAPLACE-2 randomized clinical trial. JAMA. 2014;311:1870–82.

    Article  PubMed  Google Scholar 

  9. European Medicines Agency. Guideline on adjustment for baseline covariates Guideline on adjustment for baseline covariates. Eur Med Agency. 2013;44:1–10. www.ema.europa.eu/contact.

    Google Scholar 

  10. FDA. Guidance for Industry: Adaptive Design clinical trials for drugs and biologics [excerpts]. Biotechnol Law Rep. 2010;29:197–215. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry.

    Article  Google Scholar 

  11. Egger M, Jüni P, Bartlett C, Holenstein F, Sterne J. How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study. Health Technol Assess. 2003;7.

  12. Corbett MS, Higgins JPT, Woolacott NF. Assessing baseline imbalance in randomised trials: implications for the Cochrane risk of bias tool. Res Synth Methods. 2014;5:79–85.

    Article  PubMed  Google Scholar 

  13. Trowman R, Dumville JC, Torgerson DJ, Cranny G. The impact of trial baseline imbalances should be considered in systematic reviews: a methodological case study. J Clin Epidemiol. 2007;60:1229–33.

    Article  PubMed  Google Scholar 

  14. Roberts C, Torgerson DJ. Understanding controlled trials. Baseline imbalance in randomised controlled trials. BMJ. 1999;318:185.

    Article  Google Scholar 

  15. Hulshof TA, Zuidema SU, van Meer PJK, Gispen-de Wied CC, Luijendijk HJ. Baseline imbalances and clinical outcomes of atypical antipsychotics in dementia: a meta-epidemiological study of randomized trials. Int J Methods Psychiatr Res. 2019;28.

  16. Murad MH, Wang Z. Guidelines for reporting meta-epidemiological methodology research. Evid Based Med. 2017;22:139–42.

    Article  PubMed  PubMed Central  Google Scholar 

  17. European Medicines Agency. Assessment Report: Repatha (Evolocumab). London; 2018.

  18. Ema. EPAR Praluent Final Ema. 2013;44 September.

  19. For A, Disclosure P, Redaction W. FDA Endocrinologic and Metabolic Drugs Advisory Committee. Brief Doc. 2015;May:1–152.

    Google Scholar 

  20. FDA, FDA ADVISORY COMMITTEE. BRIEFING DOCUMENT PRALUENT TM (alirocumab). Fda. 2015.

  21. Du H, Li X, Su N, Li L, Hao X, Gao H, et al. Proprotein convertase subtilisin/kexin 9 inhibitors in reducing cardiovascular outcomes: a systematic review and meta-analysis. Heart. 2019;105:1149–59.

    CAS  PubMed  Google Scholar 

  22. Carter JP, Hingorani AD, Wilkins J, Schmidt AF. Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Heart. 2022;108:14–5.

    Article  PubMed  Google Scholar 

  23. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ et al. Cochrane handbook for systematic reviews of interventions. Version 5. The Cochrane Collaboration, 2011.; 2019.

  24. Department of Health and Human Services. CFR Part 11: clinical trials Registration and results Information Submission. Fed Regist. 2016;81:1–177.

    Google Scholar 

  25. MedDra. https://www.meddra.org/.

  26. https://www.statisticshowto.com/pooled-standard-deviation/.

  27. van Bruggen FH, Nijhuis GBJ, Zuidema SU, Luijendijk HJ. Response to letter to the editor re: ‘serious adverse events and deaths in PCSK9 inhibitor trials reported on ClinicalTrials.gov: a systematic review’. Expert Rev Clin Pharmacol. 2021;14:283–4.

    Article  PubMed  Google Scholar 

  28. Sabatine MS, Giugliano RP, Schwartz GG. Letter to the editor re: ‘serious adverse events and deaths in PCSK9 inhibitor trials reported on ClinicalTrials.gov: a systematic review’. Expert Rev Clin Pharmacol. 2021;14:281–2.

    Article  CAS  PubMed  Google Scholar 

  29. Stroes E, Guyton JR, Lepor N, Civeira F, Gaudet D, Watts GF, et al. Efficacy and safety of Alirocumab 150 mg every 4 weeks in patients with hypercholesterolemia not on statin therapy: the ODYSSEY CHOICE II study. J Am Heart Assoc. 2016;5:e003421.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Teramoto T, Kobayashi M, Tasaki H, Yagyu H, Higashikata T, Takagi Y, et al. Efficacy and safety of alirocumab in Japanese patients with heterozygous familial hypercholesterolemia or at high cardiovascular risk with hypercholesterolemia not adequately controlled with statins – ODYSSEY JAPAN randomized controlled trial. Circ J. 2016;80:1980–7.

    Article  CAS  PubMed  Google Scholar 

  31. Ballantyne CM, Neutel J, Cropp A, Duggan W, Wang EQ, Plowchalk D, et al. Results of bococizumab, a monoclonal antibody against proprotein convertase subtilisin/kexin type 9, from a randomized, placebo-controlled, dose-ranging study in statin-treated subjects with hypercholesterolemia. Am J Cardiol. 2015;115:1212–21.

    Article  CAS  PubMed  Google Scholar 

  32. Giugliano RP, Desai NR, Kohli P, Rogers WJ, Somaratne R, Huang F, et al. Efficacy, safety, and tolerability of a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 in combination with a statin in patients with hypercholesterolaemia (LAPLACE-TIMI 57): a randomised, placebo-controlled, dose-ranging, phase 2 stu. Lancet. 2012;380:2007–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Raal F, Scott R, Somaratne R, Bridges I, Li G, Wasserman SM, et al. Low-density lipoprotein cholesterol-lowering effects of AMG 145, a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 serine protease in patients with heterozygous familial hypercholesterolemia: the reduction of LDL-C with PCSK9 inhibiti. Circulation. 2012;126:2408–17.

    Article  CAS  PubMed  Google Scholar 

  34. Hirayama A, Honarpour N, Yoshida M, Yamashita S, Huang F, Wasserman SM, et al. Effects of evolocumab (AMG 145), a monoclonal antibody to PCSK9, in hypercholesterolemic, statin-treated Japanese patients at high cardiovascular risk - primary results from the phase 2 YUKAWA study. Circ J. 2014;78:1073–82.

    Article  CAS  PubMed  Google Scholar 

  35. Blom DJ, Hala T, Bolognese M, Lillestol MJ, Toth PD, Burgess L, et al. A 52-Week placebo-controlled trial of Evolocumab in Hyperlipidemia. N Engl J Med. 2014;370:1809–19.

    Article  CAS  PubMed  Google Scholar 

  36. Raal FJ, Stein EA, Dufour R, Turner T, Civeira F, Burgess L, et al. PCSK9 inhibition with evolocumab (AMG 145) in heterozygous familial hypercholesterolaemia (RUTHERFORD-2): a randomised, double-blind, placebo-controlled trial. Lancet. 2015;385:331–40.

    Article  CAS  PubMed  Google Scholar 

  37. Kiyosue A, Honarpour N, Xue A, Wasserman S, Hirayama A. Effects of Evolocumab (Amg 145) in Hypercholesterolemic, Statin-Treated, Japanese patients at High Cardiovascular Risk: results from the Phase Iii Yukawa 2 study. J Am Coll Cardiol. 2015;65:A1369.

    Article  Google Scholar 

  38. Amgen. FLOREY trial. 2016.

  39. Nicholls SJ, Puri R, Anderson T, Ballantyne CM, Cho L, Kastelein JJP, et al. Effect of evolocumab on progression of coronary disease in statin-treated patients: the GLAGOV randomized clinical trial. JAMA - J Am Med Assoc. 2016;316:2373–84.

    Article  CAS  Google Scholar 

  40. Kastelein JJP, Nissen SE, Rader DJ, Hovingh GK, Wang MD, Shen T, et al. Safety and efficacy of LY3015014, a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9): a randomized, placebo-controlled phase 2 study. Eur Heart J. 2016;37:1360–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Baruch A, Mosesova S, Davis JD, Budha N, Vilimovskij A, Kahn R, et al. Effects of RG7652, a monoclonal antibody against PCSK9, on LDL-C, LDL-C subfractions, and inflammatory biomarkers in patients at high risk of or with established Coronary Heart Disease (from the phase 2 EQUATOR study). Am J Cardiol. 2017;119:1576–83.

    Article  CAS  PubMed  Google Scholar 

  42. Koren MJ, Scott R, Kim JB, Knusel B, Liu T, Lei L, et al. Efficacy, safety, and tolerability of a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 as monotherapy in patients with hypercholesterolaemia (MENDEL): a randomised, double-blind, placebo-controlled, phase 2 study. Lancet. 2012;380:1995–2006.

    Article  CAS  PubMed  Google Scholar 

  43. Sullivan D, Olsson AG, Scott R, Kim JB, Xue A, Gebski V, et al. Effect of a monoclonal antibody to PCSK9 on low-density lipoprotein cholesterol levels in statin-intolerant patients: the GAUSS randomized trial. JAMA. 2012;308:2497–506.

    Article  CAS  PubMed  Google Scholar 

  44. Koren MJ, Lundqvist P, Bolognese M, Neutel JM, Monsalvo ML, Yang J, et al. Anti-PCSK9 monotherapy for hypercholesterolemia: the MENDEL-2 randomized, controlled phase III clinical trial of evolocumab. J Am Coll Cardiol. 2014;63:2531–40.

    Article  CAS  PubMed  Google Scholar 

  45. Bays H, Gaudet D, Weiss R, Ruiz JL, Watts GF, Gouni-Berthold I, et al. Alirocumab as add-on to atorvastatin versus other lipid treatment strategies: ODYSSEY OPTIONS I randomized trial. J Clin Endocrinol Metab. 2015;100:3140–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Roth EM, McKenney JM. ODYSSEY MONO: Effect of alirocumab 75 mg subcutaneously every 2 weeks as monotherapy versus ezetimibe over 24 weeks. Future Cardiol. 2015;11:27–37.

    Article  CAS  PubMed  Google Scholar 

  47. Moriarty PM, Thompson PD, Cannon CP, Guyton JR, Bergeron J, Zieve FJ, et al. Efficacy and safety of alirocumab vs ezetimibe in statin-intolerant patients, with a statin rechallenge arm: the ODYSSEY ALTERNATIVE randomized trial. J Clin Lipidol. 2015;9:758–69.

    Article  PubMed  Google Scholar 

  48. Farnier M, Jones P, Severance R, Averna M, Steinhagen-Thiessen E, Colhoun HM, et al. Efficacy and safety of adding alirocumab to rosuvastatin versus adding ezetimibe or doubling the rosuvastatin dose in high cardiovascular-risk patients: the ODYSSEY OPTIONS II randomized trial. Atherosclerosis. 2016;244:138–46.

    Article  CAS  PubMed  Google Scholar 

  49. Stroes E, Colquhoun D, Sullivan D, Civeira F, Rosenson RS, Watts GF, et al. Anti-PCSK9 antibody effectively lowers cholesterol in patients with statin intolerance: the GAUSS-2 randomized, placebo-controlled phase 3 clinical trial of evolocumab. J Am Coll Cardiol. 2014;63:2541–8.

    Article  CAS  PubMed  Google Scholar 

  50. Teramoto T, Kondo A, Kiyosue A, Harada-Shiba M, Ishigaki Y, Tobita K et al. Efficacy and safety of alirocumab in patients with hypercholesterolemia not adequately controlled with non-statin lipid-lowering therapy or the lowest strength of statin: ODYSSEY NIPPON study design and rationale. Lipids Health Dis. 2017;16.

  51. Roth EM, McKenney JM, Hanotin C, Asset G, Stein EA. Atorvastatin with or without an antibody to PCSK9 in primary hypercholesterolemia. N Engl J Med. 2012;367:1891–900.

    Article  CAS  PubMed  Google Scholar 

  52. Robinson JG, Farnier M, Krempf M, Bergeron J, Luc G, Averna M, et al. Efficacy and safety of Alirocumab in reducing lipids and Cardiovascular events. N Engl J Med. 2015;372:1489–99.

    Article  CAS  PubMed  Google Scholar 

  53. Teramoto T, Kobayashi M, Uno K, Takagi Y, Matsuoka O, Sugimoto M et al. Efficacy and safety of alirocumab in Japanese subjects (phase 1 and 2 studies). In: Am J Cardiol. 2016. p. 56–63.

  54. Leiter LA, Cariou B, Müller-Wieland D, Colhoun HM, Del Prato S, Tinahones FJ, et al. Efficacy and safety of alirocumab in insulin-treated individuals with type 1 or type 2 diabetes and high cardiovascular risk: the ODYSSEY DM-INSULIN randomized trial. Diabetes Obes Metab. 2017;19:1781–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Koh KK, Nam CW, Chao TH, Liu ME, Wu CJ, Kim DS, et al. A randomized trial evaluating the efficacy and safety of alirocumab in South Korea and Taiwan (ODYSSEY KT). J Clin Lipidol. 2018;12:162–e1726.

    Article  PubMed  Google Scholar 

  56. Blom DJ, Harada-Shiba M, Rubba P, Gaudet D, Kastelein JJP, Charng MJ, et al. Efficacy and safety of Alirocumab in adults with homozygous familial hypercholesterolemia: the ODYSSEY HoFH Trial. J Am Coll Cardiol. 2020;76:131–42.

    Article  CAS  PubMed  Google Scholar 

  57. Vergouwen MDI, De Haan RJ, Vermeulen M, Roos YBWEM. Statin treatment and the occurrence of hemorrhagic stroke in patients with a history of cerebrovascular disease. Stroke. 2008;39:497–502.

    Article  CAS  PubMed  Google Scholar 

  58. Han Y, Chen J, Chopra VK, Zhang S, Su G, Ma C, et al. ODYSSEY EAST: Alirocumab efficacy and safety vs ezetimibe in high cardiovascular risk patients with hypercholesterolemia and on maximally tolerated statin in China, India, and Thailand. J Clin Lipidol. 2020;14:98–e1088.

    Article  PubMed  Google Scholar 

  59. McKenney JM, Koren MJ, Kereiakes DJ, Hanotin C, Ferrand AC, Stein EA. Safety and efficacy of a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 serine protease, SAR236553/REGN727, in patients with primary hypercholesterolemia receiving ongoing stable atorvastatin therapy. J Am Coll Cardiol. 2012;59:2344–53.

    Article  CAS  PubMed  Google Scholar 

  60. Nissen SE, Stroes E, Dent-Acosta RE, Rosenson RS, Lehman SJ, Sattar N, et al. Efficacy and tolerability of evolocumab vs ezetimibe in patients with muscle-related statin intolerance: the GAUSS-3 randomized clinical trial. JAMA - J Am Med Assoc. 2016;315:1580–90.

    Article  CAS  Google Scholar 

  61. Koba S, Inoue I, Cyrille M, Lu C, Inomata H, Shimauchi J, et al. Evolocumab vs. Ezetimibe in statin-intolerant hyperlipidemic Japanese patients: phase 3 gauss-4 trial. J Atheroscler Thromb. 2020;27:471–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Stein EA, Gipe D, Bergeron J, Gaudet D, Weiss R, Dufour R, et al. Effect of a monoclonal antibody to PCSK9, REGN727/SAR236553, to reduce low-density lipoprotein cholesterol in patients with heterozygous familial hypercholesterolaemia on stable statin dose with or without ezetimibe therapy: a phase 2 randomised controlle. Lancet. 2012;380:29–36.

    Article  CAS  PubMed  Google Scholar 

  63. Kastelein JJP, Ginsberg HN, Langslet G, Kees Hovingh G, Ceska R, Dufour R, et al. ODYSSEY FH i and FH II: 78 week results with alirocumab treatment in 735 patients with heterozygous familial hypercholesterolaemia. Eur Heart J. 2015;36:2996–3003.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Kereiakes DJ, Robinson JG, Cannon CP, Lorenzato C, Pordy R, Chaudhari U, et al. Efficacy and safety of the proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab among high cardiovascular risk patients on maximally tolerated statin therapy: the ODYSSEY COMBO i study. Am Heart J. 2015;169:906–e91513.

    Article  CAS  PubMed  Google Scholar 

  65. Ginsberg HN, Rader DJ, Raal FJ, Guyton JR, Baccara-Dinet MT, Lorenzato C, et al. Efficacy and safety of Alirocumab in patients with heterozygous familial hypercholesterolemia and LDL-C of 160 mg/dl or higher. Cardiovasc Drugs Ther. 2016;30:473–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Roth EM, Moriarty PM, Bergeron J, Langslet G, Manvelian G, Zhao J, et al. A phase III randomized trial evaluating alirocumab 300 mg every 4 weeks as monotherapy or add-on to statin: ODYSSEY CHOICE I. Atherosclerosis. 2016;254:254–62.

    Article  CAS  PubMed  Google Scholar 

  67. Wewege MA, Hansford HJ, Shah B, Gilanyi YL, Douglas SRG, Parmenter BJ, et al. Baseline imbalance and heterogeneity are present in meta-analyses of randomized clinical trials examining the effects of exercise and medicines for blood pressure management. Hypertens Res. 2022;45:1643–52.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Clark L, Fairhurst C, Hewitt CE, Birks Y, Brabyn S, Cockayne S, et al. A methodological review of recent meta-analyses has found significant heterogeneity in age between randomized groups. J Clin Epidemiol. 2014;67:1016–24.

    Article  PubMed  Google Scholar 

  69. Dicembrini I, Giannini S, Ragghianti B, Mannucci E, Monami M. Effects of PCSK9 inhibitors on LDL cholesterol, cardiovascular morbidity and all-cause mortality: a systematic review and meta-analysis of randomized controlled trials. J Endocrinol Invest. 2019;42:1029–39.

    Article  CAS  PubMed  Google Scholar 

  70. Karatasakis A, Danek BA, Karacsonyi J, Rangan BV, Roesle MK, Knickelbine T et al. Effect of PCSK9 inhibitors on clinical outcomes in patients with hypercholesterolemia: a meta-analysis of 35 randomized controlled trials. J Am Heart Association. 2017;6.

  71. Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Reviews. 2017;2017:1465–858.

    Google Scholar 

  72. Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. Baseline P value distributions in randomized trials were uniform for continuous but not categorical variables. J Clin Epidemiol. 2019;112:67–76.

    Article  PubMed  Google Scholar 

  73. Kebede MM, Peters M, Heise TL, Pischke CR. Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes. Diabetes, Metab Syndr Obes. 2019;12:59–73.

  74. Jordan VMB, Lensen SF, Farquhar CM. There were large discrepancies in risk of bias tool judgments when a randomized controlled trial appeared in more than one systematic review. J Clin Epidemiol. 2017;81:72–6.

    Article  PubMed  Google Scholar 

  75. Jørgensen L, Paludan-Müller AS, Laursen DRT, Savović J, Boutron I, Sterne JAC et al. Evaluation of the Cochrane tool for assessing risk of bias in randomized clinical trials: overview of published comments and analysis of user practice in Cochrane and non-cochrane reviews. Syst Rev. 2016;5.

  76. Jakobsen JC, Wetterslev J, Winkel P, Lange T, Gluud C. Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods. BMC Med Res Methodol. 2014;14.

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of General Practice and Elderly Care Medicine, University of Groningen, University Medical Centre Groningen (UMCG), PO Box 196, Groningen, AD, 9700, The Netherlands

    F. H. van Bruggen, S. U. Zuidema & H. J. Luijendijk

Authors
  1. F. H. van Bruggen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. U. Zuidema
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. J. Luijendijk
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F. van Bruggen performed the search, extracted and analyzed the data, and drafted the manuscript. S. Zuidema helped draft the manuscript. H. Luijendijk designed the study, performed the search, extracted and analyzed data, and helped draft the manuscript. H. Luijendijk is the guarantor of the paper and affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; All authors have approved the final manuscript and consented to its publication.

Corresponding author

Correspondence to H. J. Luijendijk.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Bruggen, F.H., Zuidema, S.U. & Luijendijk, H.J. Quantitative assessment of baseline imbalances in evolocumab and alirocumab trials: a meta-epidemiological study. BMC Med Res Methodol 24, 137 (2024). https://doi.org/10.1186/s12874-024-02260-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12874-024-02260-z

Keywords

BMC Medical Research Methodology

ISSN: 1471-2288

Contact us