**Overview**

Most meta-analyses are based on one of two statistical models, the fixed-effect model or the random-effects model. **Under the fixed-effect model we assume that there is one true effect size (hence the term fixed effect) which underlies all the studies in the analysis, and that all differences in observed effects are due to sampling error**. While we follow the practice of calling this a fixed-effect model, a more descriptive term would be a common-effect model.

By contrast, under the random-effects model we allow that the true effect could vary from study to study. For example, the effect size might be higher (or lower) in studies where the participants are older, or more educated, or healthier than in others, or when a more intensive variant of an intervention is used, and so on. Because studies will differ in the mixes of participants and in the implementations of interventions, among other reasons, there may be different effect sizes underlying different studies.

Since all studies share the same true effect, it follows that the observed effect size varies from one study to the next only because of the random error inherent in each study. If each study had an infinite sample size the sampling error would be zero and the observed effect for each study would be the same as the true effect. If we were to plot the observed effects rather than the true effects, the observed effects would exactly coincide with the true effects.

In practice, of course, the sample size in each study in not infinite, and so there is sampling error and the effect observed in the study is not the same as the true effect. In Figure 11.2 the true effect for each study is still 0.60 but the observed effect differs from one study to the next.

**While the error in any given study is random**, we can estimate the sampling distribution of the errors. In Figure 11.3 we have placed a normal curve about the true effect size for each study, with the width of the curve being based on the variance in that study. In Study 1 the sample size was small, the variance large, and the observed effect is likely to fall anywhere in the relatively wide range of 0.20 to 1.00. By contrast, in Study 2 the sample size was relative large, the variance is small, and the observed effect is likely to fall in the relatively narrow range of 0.40 to 0.80. Note that the width of the normal curve is based on the square root of the variance, or standard error.

**Meta-analysis Procedure**

In an actual meta-analysis, of course, rather than starting with the population effect and making projections about the observed effects, we work backwards, starting with the observed effects and trying to estimate the population effect. In order to obtain the most precise estimate of the population effect (to minimize the variance) we compute a weighted mean, where the weight assigned to each study is the inverse of that study’s variance. Concretely, the weight assigned to each study in a fixed-effect meta-analysis is

Where V*Yi* is the within-study variance for study (*i*). The weighted mean (*M*) is then computed as

That is, the sum of the products *WiYi* (effect size multiplied by weight) divided by the sum of the weights.

The variance of the summary effect is estimated as the reciprocal of the sum the weights, or

Once V*M* is estimated, the standard deviation of the weighted mean (or, standard error of the weighted mean) is computed as the square root of the variance of the summary effect. Now we know the distribution, the point estimation, and the standard deviation, of the weight mean. Thus, the confidence interval of the summary effect could be computed by the confidence interval Z-procedure.

**Effect Sizes Measurements**

**Raw Mean Difference**

When the studies report means and standard deviations (continuous variables), the preferred effect size is usually the raw mean difference, the standard mean difference (SMD), or the response ratio. When the outcome is reported on a meaningful scale and all studies in the analysis use the same scale, the meta-analysis can be performed directly on the raw difference in means, or the raw mean difference. The primary advantage of the raw mean difference is that it is intuitively meaningful, either inherently or because of widespread use. Examples of raw mean difference include systolic blood pressure (mm Hg), serum LDL-C level (mg/dL), body surface area (m2), and so on.

We can estimate the mean difference *D* from a study that used two independent groups revealed by the inference procedure for two population means (independent samples). Let’s recall a little for the inference procedure for two population means. The sampling distribution of the difference between two sample meets these characteristics:

PS: All is based on the central limit theorem – if the sample size is large, the mean is approximately normally distributed, regardless of the distribution of the variable under consideration.

Once we know the sample mean difference, *D*, the standard deviation of the mean difference (or the standard error), and in the light of the central limit theorem, we could compute the variance of *D*. In addition to know the group mean, the standard deviation of group mean, and the group size, we also could compute the pooled sample standard deviation (*S*p) or the nonpooled method. Therefore, we would have the value of variance of *D*, which will be used by meta-analysis procedures (fixed-effect, or random-effects model) to compute the weight (*W*i = 1 / *V*Yi). And once the standard error is known, the synthesized confidence interval could be computed.

**Standardized Mean Difference, d and g**

As noted, the raw mean difference is a useful index when the measure is meaningful, either inherently or because of widespread use. By contrast, when the measure is less well known, the use of a raw mean difference has less to recommend it. In any event, the raw mean difference is an option only if all the studies in the meta-analysis use the same scale. If different studies use different instruments to assess the outcome, then the scale of measurement will differ from study to study and it would not be meaningful to combine raw mean differences.

In such cases we can divide the mean difference in each study by that study’s standard deviation to create an index (the standard mean difference, SMD) that would be comparable across studies. This is the same approach suggested by Cohen in connection with describing the magnitude of effects in statistical power analysis. The standard mean difference can be considered as being comparable across studies based on either of two arguments (Hedges and Olkin, 1985). If the outcome measures in all studies are linear transformations of each other, the standardized mean difference can be seen as the mean difference that would have been obtained if all data were transformed to a scale where the standard deviation within-groups was equal to 1.0.

The other argument for comparability of standardized mean differences is the fact that the standardized mean difference is a measure of overlap between distributions. In this telling, the standardized mean difference reflects the difference between the distributions in the two groups (and how each represents a distinct cluster of scores) even if they do not measure exactly the same outcome.

**Computing d and g from studies that use independent groups**

We can estimate the standardized mean difference from studies that used two independent groups as

where *S*within is the pooled standard deviation across groups. And *n*1 and *n*2 are the sample sizes in the two groups, *S*1 and *S*2 are the standard deviations in the two groups. The reason that we pool the two sample estimates of the standard deviation is that even if we assume that the underlying population standard deviations are the same, it is unlikely that the sample estimates *S*1 and *S*2 will be identical. By pooling the two estimates of the standard deviation, we obtain a more accurate estimate of their common value.

The sample estimate of the standardized mean difference is often called Cohen’s *d* in research synthesis. Some confusion about the terminology has resulted from the fact that the index 𝛿, originally proposed by Cohen as a population parameter for describing the size of effects for statistical power analysis is also sometimes called *d*. The variance of *d* is given by,

Again, with the standard mean difference and variance of the standard mean difference known, we could compute the confidence interval of the standard mean difference. However,** it turns out that d has a slight bias, tending to overestimate the absolute value of 𝛿 in small samples**. This bias can be removed by a simple correction that yields an unbiased estimate of 𝛿, with the unbiased estimate sometimes called Hedges’

*g*(Hedges, 1981). To convert from

*d*to Hedges’

*g*we use a correction factor, which is called

*J*. Hedges (1981) gives the exact formula for

*J*, but in common practice researchers use an approximation,

**Summary**

- Under the fixed-effect model all studies in the analysis share a common true effect.
- The summary effect is our estimate of this common effect size, and the null hypothesis is that this common effect is zero (for a difference) or one (for a ratio).
- All observed dispersion reflects sampling error, and study weights are assigned with the goal of minimizing this within-study error.

**Converting Among Effect Sizes**

Despite that widespread used outcome measures would be across studies under investigation, it is not uncommon that the outcome measures among individual studies are different. When we convert between different measures we make certain assumptions about the nature of the underlying traits or effects. Even if these assumptions do not hold exactly, the decision to use these conversions is often better than the alternative, which is to simply omit the studies that happened to use an alternate metric. This would involve loss of information, and possibly the systematic loss of information, resulting in a biased sample of studies. A sensitivity analysis to compare the meta-analysis results with and without the converted studies would be important. Figure 7.1 outlines the mechanism for incorporating multiple kinds of data in the same meta-analysis. First, each study is used to compute an effect size and variance of native index, the log odds ratio for binary data, *d* for continuous data, and *r* for correlational data. Then, we convert all of these indices to a common index, which would be either the log odds ratio, *d*, or *r*. If the final index is *d*, we can move from there to Hedges’ *g*. This common index and its variance are then used in the analysis.

We can convert from a log odds ratio to the standardized mean difference *d* using

where 𝜋 is the mathematical constant. The variance of *d* would then be

where *V*logOddsRatio is the variance of the log odds ratio. This method was originally proposed by Hasselblad and Hedges (1995) but variations have been proposed. It assumes that an underlying continuous trait exists and has a logistic distribution (which is similar to a normal distribution) in each group. In practice, it will be difficult to test this assumption.

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