When studies use different scales to measure continuous outcomes, standardised mean differences (SMD) are required to meta-analyse the data. However, outcomes are often reported as endpoint or change from baseline scores. Combining corresponding SMDs can be problematic and available guidance advises against this practice. We aimed to examine the impact of combining the two types of SMD in meta-analyses of depression severity. We used individual participant data on pharmacological interventions (89 studies, 27,409 participants) and internet-delivered cognitive behavioural therapy (iCBT; 61 studies, 13,687 participants) for depression to compare endpoint and change from baseline SMDs at the study level. Next, we performed pairwise (PWMA) and network meta-analyses (NMA) using endpoint SMDs, change from baseline SMDs, or a mixture of the two. Study-specific SMDs calculated from endpoint and change from baseline data were largely similar, although for iCBT interventions 25% of the studies at 3 months were associated with important differences between study-specific SMDs (median 0.01, IQR -0.10, 0.13) especially in smaller trials with baseline imbalances. However, when pooled, the differences between endpoint and change SMDs were negligible. Pooling only the more favourable of the two SMDs did not materially affect meta-analyses, resulting in differences of pooled SMDs up to 0.05 and 0.13 in the pharmacological and iCBT datasets, respectively. Our findings have implications for meta-analyses in depression, where we showed that the choice between endpoint and change scores for estimating SMDs had immaterial impact on summary meta-analytic estimates. Future studies should replicate and extend our analyses to fields other than depression.

UNASSIGNED: Most meta-analyses use the \'standardised mean difference\' (effect size) to summarise the outcomes of studies. However, the effect size has important limitations that need to be considered.
UNASSIGNED: After a brief explanation of the standardized mean difference, limitations are discussed and possible solutions in the context of meta-analyses are suggested.
UNASSIGNED: When using the effect size, three major limitations have to be considered. First, the effect size is still a statistical concept and small effect sizes may have considerable clinical meaning while large effect sizes may not. Second, specific assumptions of the effect size may not be correct. Third, and most importantly, it is very difficult to explain what the meaning of the effect size is to non-researchers. As possible solutions, the use of the \'binomial effect size display\' and the number-needed-to-treat are discussed. Furthermore, I suggest the use of binary outcomes, which are often easier to understand. However, it is not clear what the best binary outcome is for continuous outcomes.
UNASSIGNED: The effect size is still useful, as long as the limitations are understood and also binary outcomes are given.

Meta-analysis is a powerful tool used to generate quantitatively informed answers to pressing global challenges. By distilling data from broad sets of research designs and study systems into standardised effect sizes, meta-analyses provide physiologists with opportunities to estimate overall effect sizes and understand the drivers of effect variability. Despite this ambition, research designs in the field of comparative physiology can appear, at the outset, as being vastly different to each other because of \'nuisance heterogeneity\' (e.g. different temperatures or treatment dosages used across studies). Methodological differences across studies have led many to believe that meta-analysis is an exercise in comparing \'apples with oranges\'. Here, we dispel this myth by showing how standardised effect sizes can be used in conjunction with multilevel meta-regression models to both account for the factors driving differences across studies and make them more comparable. We assess the prevalence of nuisance heterogeneity in the comparative physiology literature - showing it is common and often not accounted for in analyses. We then formalise effect size measures (e.g. the temperature coefficient, Q10) that provide comparative physiologists with a means to remove nuisance heterogeneity without the need to resort to more complex statistical models that may be harder to interpret. We also describe more general approaches that can be applied to a variety of different contexts to derive new effect sizes and sampling variances, opening up new possibilities for quantitative synthesis. By using effect sizes that account for components of effect heterogeneity, in combination with existing meta-analytic models, comparative physiologists can explore exciting new questions while making results from large-scale data sets more accessible, comparable and widely interpretable.

暂无摘要。

BACKGROUND: The magnitude of the superiority of antipsychotics over placebo is debated. One reason is that the effect-size index which is usually used in meta-analyses is in standard deviation units. Many other indices, some of which are more intuitive, exist.
METHODS: We explain the formulae, advantages, and limitations of 13 effect-size indices: Mean Difference (MD), Standardized-Mean-Difference (SMD), Correlation Coefficient, Ratio-of-Means (RoM, endpoint and change data), Improvement Fraction (IF), Drug-Response Fraction (DRF), Minimally-Clinically-Important-Difference-Units (MCIDU), Number-Needed-to-Treat-derived from SMD (NNT), Odds Ratio (OR), Relative Risk (RR), and Risk Difference (RD) derived from SMD, Drug-response and Placebo-response in percent. We applied these indices to meta-analyses comparing antipsychotic drugs with placebo for acute schizophrenia.
RESULTS: The difference of all antipsychotics pooled vs placebo (105 trials with 22741 participants) was: MD 9.4 (95% CI 8.4,10.2) PANSS points, SMD 0.47 (0.42,0.51), Correlation coefficient 0.23 (0.21,0.25), RoM endpoint 0.83 (0.81,0.85), RoM change 1.94 (1.84,2.02), IF (%) 49 (46,51), DRF (%) 94 (84,102), MCIDU 0.63 (0.56,0.68), NNT 5 (5,6), OR 2.34 (2.14, 2.52), RR 1.67 (1.59,1.73), RD 20% (18-22), and 50% (48, 52) improved on drug compared to 30% on placebo. Results of individual drugs compared to placebo are presented, as well.
CONCLUSIONS: Taken together these indices show a substantial, but not a large superiority of antipsychotics compared to placebo. The general chronicity of the patients in the trials must be considered. Future meta-analyses should report other effect size indices in addition to the Standardized-Mean-Difference, in particular percentage responders in the drug and placebo groups. They can be easily derived and would enhance the interpretation of research findings.

Randomisation is often believed to lead to baseline comparability of treatment groups in controlled trials. This study aims to challenge this popular belief, which is relevant in expectation- but not necessarily in realisation.
After presenting an overview of methods for assessing baseline comparability of treatment groups in randomised controlled trials (RCTs), we reviewed RCTs published over 1 year in three high-impact medical journals. We extracted data regarding the methods used to evaluate baseline comparability. To quantify baseline balance, we calculated post hoc standardised mean differences (SMDs) in baseline characteristics reported in these trials.
Amongst 142 RCTs, 120 (84.5%) claimed that baseline comparability was achieved. However, 81 RCTs (57%) did not report how they assessed this balance. The rest (61 RCTs, 43%) used traditional statistical tests, which are deemed inappropriate for balance checking. Our post hoc calculation of SMDs showed that 49 (34.5%) RCTs had at least one baseline variable, which might have been strongly unbalanced (i.e., SMD ≥25%) across treatment groups.
Baseline incomparability of treatment groups in RCTs is often blindly ignored. We suggest it be thoroughly evaluated and transparently reported, using the standardised mean difference or other proper balance metrics.

Aims The standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).
In this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.
One important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an \'individual patient data\' meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.
We conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalisation, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative. When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardised mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers.
CONCLUSIONS: Summary of Findings tables provide succinct presentations of evidence quality and magnitude of effects. Summarising the findings of continuous outcomes presents special challenges to interpretation that become daunting when individual trials use different measures for the same construct. The most commonly used approach to providing pooled estimates for different measures, presenting results in standard deviation units, has limitations related to both statistical properties and interpretability. Potentially preferable alternatives include presenting results in the natural units of the most popular measure, transforming into a binary outcome and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting results in preestablished minimally important difference units.

We describe a standardised mean difference statistic (d) for single-case designs that is equivalent to the usual d in between-groups experiments. We show how it can be used to summarise treatment effects over cases within a study, to do power analyses in planning new studies and grant proposals, and to meta-analyse effects across studies of the same question. We discuss limitations of this d-statistic, and possible remedies to them. Even so, this d-statistic is better founded statistically than other effect size measures for single-case design, and unlike many general linear model approaches such as multilevel modelling or generalised additive models, it produces a standardised effect size that can be integrated over studies with different outcome measures. SPSS macros for both effect size computation and power analysis are available.

Preeclampsia is the major cause of maternofetal and neonatal morbi-mortality including intrauterine growth retardation, miscarriages and stillbirths. Inadequate vascular dilation and angiogenesis represent the crucial underlying defect of gravidic hypertension, denoting a failed response to the vasodilatory and pro-angiogenic challenge imposed by pregnancy, especially if multifetal. A similar pathogenesis appears involved in gestational diabetes. In this review we aimed to provide a hint on understanding the deeply involved angiogenic disorders which eventually culminate in utero-placental failure. The key players in these complex processes may be found in an intricate network of growth factors (GFs) and GF inhibitors, controlled by several vascular risk factors modulated by environment and genes, which eventually impact on early and late cardiovascular outcomes of mother and fetus.