A simulations approach for metaanalysis of genetic association studies based on additive genetic model
Abstract
Genetic association studies are becoming an important component of medical research. To cite one instance, pharmacogenomics which is gaining prominence as a useful tool for personalized medicine is heavily reliant on results from genetic association studies. Metaanalysis of genetic association studies is being increasingly used to assess phenotypic differences between genotype groups. When the underlying genetic model is assumed to be dominant or recessive, assessing the phenotype differences based on summary statistics, reported for individual studies in a metaanalysis, is a valid strategy. However, when the genetic model is additive, a similar strategy based on summary statistics will lead to biased results. This fact about the additive model is one of the things that we establish in this paper, using simulations. The main goal of this paper is to present an alternate strategy for the additive model based on simulating data for the individual studies. We show that the alternate strategy is far superior to the strategy based on summary statistics.
keywords:
genetic association studies, meta analysis, additive model, summary statistics, simulations1 Introduction
Over the last decade, genetic association studies (both candidate based designs and genomewide designs) have become one of the cornerstone techniques in detecting specific genetic variants related to any phenotype of interest. In association studies, genetic variation is typically measured using genotyping based on single nucleotide polymorphisms (SNPs). In order to fix ideas, let us assume that a SNP of interest is named and the corresponding genotypes AA, AB and BB, with A as the major allele and B as the minor allele (the risk allele). The phenotype of interest in our motivating examplestudies was weight gain due to antipsychotic treatment. With the proliferation of association studies, it is often seen that the standardized differences in weight gain (“effect sizes”) between any pair of genotypes vary across the studies. For this reason, metaanalysis has become the method that is being increasingly employed to assess the phenotypic differences across the genotypic groups.
Combining the effect sizes between genotype groups in a metaanalysis depends on the underlying genetic model. The most commonly considered genetic models are dominant, recessive and additive models. In a dominant model, allele B increases risk and the number of copies of B doesn’t matter; that is, genotypes AB and BB carry the same risk. In the recessive model, two copies of allele B are required for increased risk; that is, AB is grouped together with AA, and both groups are assumed to have no risk. In an additive model, the increase in risk is proportional to the number of copies of allele B; that is, if the risk is increased fold for the genotype group AB, then it is increased fold for BB.
Typically, in the association studies considered for the metaanalysis, summary statistics (mean, median, standard deviation etc.) for weight gain will be reported for each of the three genotype groups. If we consider the underlying model to be dominant, one may take the average of the mean (or median) weight gains reported for the groups AB and BB, and calculate the standardized difference between this average and the corresponding mean (or median) weight gain reported for AA, to get the effect size for each study. It is ideal to weight the average for AB and BB by their respective sample sizes. This is a valid strategy in the sense that if the metaanalyst had access to each study’s data, and if he or she grouped together the weight gains for the individual patients in groups AB and BB, and took the standardized mean difference with the individual data from AA group, then the effect size obtained will be same as the one obtained using summary statistics mentioned above; at least conceptually same  practically there might be some differences, for example, if one did not weight by sample sizes. The same argument applies for the recessive model too. However, for the additive model, the results from crude approach based on combining summary statistics will not correspond with the results using the approach based on the individual data, if the analyst was to have access to the individual data. The crude approach for the additive model would be roughly as follows. Take the difference of the summary statistics (mean or median) for AB and BB, then do similarly for AA and AB, and then take the average of these two standardized pairwise differences to obtain the corresponding effect size. One of the things that we show in this paper, via simulations, is that this crude approach leads to biased results.
Since the approach based on summary statistics leads to biased results for the additive model, the analyst will have to fall back on subject level data from each study to calculate the appropriate effect size. But, it is well known that very rarely does a metaanalyst have access to such data. The solution to this dilemma, that we suggest in this paper, is to generate subject level data via simulations based on the summary level data, in order to calculate the effect size for each study under the additive genetic model. We show via extensive simulations and real data that the simulationsbasedapproach that we propose is far superior to the crude approach, and gives results very similar to those that might be obtained if individual level data was available. Before causing any confusion, we would like to clarify the word ‘simulations’ used twice in the previous sentence. The new method proposed in this paper is based on simulations, and we compare this new method with the old method (that is, the crude approach based on summary statistics) also using simulations.
In section 2, we explain the methods more clearly using notations. In section 3, we explain the simulations comparing the two methods, and in section 4, we present the results of the simulations. Section 5 illustrates how well the simulationsbasedmethod works for a real data example; that is, for a data set consisting of studies for which we had subject level data. In sections 2 to 5, the phenotype of interest (e.g. weight gain) was considered as a continuous variable. But, phenotypes could be dichotomous as well (e.g. overweight  yes/no). In section 6, we show how our approach can be extended for dichotomous phenotypes as well. Finally, we summarize our findings in the last section.
2 Methods
In this section we describe two methods for metaanalysis assuming additive genetic model in genetic association studies. We are interested in the additive effect of the 3 genotypic groups on a phenotype of interest. In our motivating example, the phenotype of interest was the change in body weight due to antipsychotic treatment in patients with schizophrenia. We assume that the means and standard deviations of the phenotype of interest are given for the three groups along with the sample sizes .
Crude Approach. One way to find the additive effect would be to stack the three means and into a column vector and regress it against a column vector of group indicators . The thus obtained is just the average of the pairwise mean differences and . The R codes corresponding to this simple approach would be
M.crude
c(m1, m2, m3) #means
G.crude
c(1, 2, 3) #group indicator variable
beta.crude
summary(lm(M.crude~G.crude))$coeff[2,1]
In order to calculate the standard deviation of (that is, the denominator of Cohen’s ), there is no straightforward approach. However, one could take the standard deviation for the mean difference between samples 1 and 2, , and similarly for samples 2 and 3, , and take the average of these two standard deviations, as the standard deviation for . and are within group standard deviations, pooled across the corresponding groups. As mentioned by Borenstein and coauthors, the reason to pool is that even if the underlying population standard deviations are the same, it is unlikely that the sample estimates and (used in the calculation of ) and or (for ) will be equal. The R codes for this part are
SD.crude
c(sd1, sd2, sd3)
SS.crude
c(n1, n2, n3)
sd12
sqrt((((SS.crude[1]1)*SD.crude[1]^2) + ((SS.crude[2]1)*SD.crude[2]^2))/(SS.crude[1] + SS.crude[2]  2))
sd23
sqrt((((SS.crude[2]1)*SD.crude[2]^2) + ((SS.crude[3]1)*SD.crude[3]^2))/(SS.crude[2] + SS.crude[3]  2))
sd.beta.crude
mean(c(sd12, sd23))
We use the following wellknown formula to calculate an approximate variance for Cohen’s :
Note that the above formula can be used only for a pair of samples. So, we use this formula to calculate and separately for the sample pairs 1 and 2, and 2 and 3. Similarly the correction factor
for the conversion of to Hedge’s is also used separately for the two pairs to get and . Then and variance of for each pair are calculated as
Finally, and its variance are obtained as a weighted average,
Note that this weighted average was suggested by Hedges and coauthors for combining effect size estimates.
Simulations Approach. Another approach to the same problem would be based on simulations. In this approach, we generate individual data from the normal distribution via simulations using, for example, the rnorm function in R, based on the mean, standard deviation and sample size for each genotypic group, reported for each study. A group indicator variable is created with ’s, ’s and ’s, and the simulated individual data is regressed against the indicator variable to obtain , the numerator of Cohen’s . A oneway ANOVA test comparing the individual data across the 3 genotypic groups is then conducted. The denominator of Cohen’s for this approach is obtained as the square root of the ratio of betweenmeansquares and the statistic. This process is repeated 10,000 times and the average of Cohen’s ’s across all the 10,000 iterations is taken as the estimated Cohen’s for this approach. The corresponding R codes are given below.
Sim.Num
10000 ## number of iterations
beta.sim
array(, Sim.Num)
sd.sim
array(, Sim.Num)
for(m in 1:Sim.Num) {
sample1
rnorm(n1, m1, sd1)
sample2
rnorm(n2, m2, sd2)
sample3
rnorm(n3, m3, sd3)
M
c(sample1, sample2, sample3)
G
c(rep(1, n1), rep(2, n2), rep(3, n3))
beta.sim[m]
summary(lm(M~G))$coeff[2,1]
sd.sim[m]
sqrt(anova(lm(M~G))[1,3]/anova(lm(M~G))[1,4]) }
cohen.d.sim
mean(beta.sim/sd.sim)
The calculations for variance for Cohen’s , the correction factor for conversion from to , and then for and its variance are all done in a pairwise manner as was done for the crude approach (See above).
3 Monte Carlo Simulations
In order to assess the performance of the two methods described above under various scenarios, we conducted Monte Carlo simulations. By “various scenarios” we mean the scenarios obtained by varying the following parameters: number of studies in the meta analysis, the underlying distribution from which the sample for each study was obtained, the sample sizes for each study, and the true underlying effect size for each study. The last parameter was varied by varying the means for the three genotypic groups in each study, as well the withinstudy standard deviation. Our goals were a) to see whether the newly proposed methods improved with larger number of studies in the metaanalysis, b) whether they improved with larger sample sizes for the genotypic groups in the studies, c) whether the performance depended on either the underlying distribution or the underlying true effect sizes. It was intuitive to hypothesize that the performance of the methods would improve with increasing the number of studies and the sample sizes. So, our questions for parts a) and b) were really how large was good enough.
The key step in the simulationsbased method presented above is the generation of (simulated) individual data from normal distributions using the “reported” means, standard deviations and sample sizes of the three genotypic groups from each study. (Note that, for the simulation analysis presented in this section, there was actually no reported means or standard deviations, but these values were generated in such a way that they matched with reported values from one the real data sets (EUFEST) discussed in section 5. See below the discussion related to mean.vec for more details.) Thus, the first question that we considered was whether the performance of the simulationsbased method was affected when the original data distribution was not normally distributed. For this purpose, we conducted Monte Carlo simulations in which the original data were sampled from 3 distributions other than normal: strongly right skewed, asymmetric bimodal and heavily kurtotic. For comparison purposes, we assessed the performance of the methods, by sampling the original data from the normal distribution also. The shapes of the probability density functions for the four distributions considered are shown in Figure 1. If denote the density function for a normal distribution with mean and variance , the formulas for the densities used for our simulation study are
Each of the above densities is a normal mixture. These densities correspond to the first, third, eighth and fourth densities considered in Marron and Wand (1992), and have been used in simulation studies previously (for example, in John and Priebe (2007)).
Once we chose a distribution from among , , and , we then selected the number of studies, , to be included in the simulation analysis. We considered scenarios with and . After the distribution and number of studies were fixed, the next task was to select a “mean vector”, mean.vec, from among the following 3 triplets: and , and then to select a withinstudies standard deviation to be either or . The approximate effect size corresponding to the various mean.vec and are given in table 1 below.
Table 1. Effect size table  
Approximate  
true value of  
1 
4  5.5  7  0.82 
4  5.5  9  1.28  
4  5.5  11  1.64  
5  4  5.5  7  0.30 
4  5.5  9  0.48  
4  5.5  11  0.65 
In the next step, we generated means for the genotypic group () from a normal distribution with mean as the component of the mean.vec and standard deviation equals 2, using, for example, the function in R. Thus the average mean triplet across all studies will be the mean.vec that we selected for this scenario, but we create variation across the studies by generating the mean triplet for each study from a normal distribution. Similarly, we generated (withinstudy) standard deviations for the genotypic group () from normal distributions using the component of the selected. The codes for these steps with , mean.vec and are given below.
L
10 ## number of studies
mean.vec
c(4, 5.5, 9)
m1
rnorm(L, mean.vec[1], 2); m1[m1 < 0]
mean.vec[1] ## L means for the 1st genotypic group
m2
rnorm(L, mean.vec[2], 2); m2[m2 < 0]
mean.vec[1] ## L means for the 2nd genotypic group
m3
rnorm(L, mean.vec[3], 2); m3[m3 < 0]
mean.vec[1] ## L means for the 3rd genotypic group
sd.ws
1
sd1
rnorm(L, sd, 2); sd1[sd1 < 0]
sd.ws ## L withinstudy SD’s for the 1st genotypic group
sd2
rnorm(L, sd, 2); sd2[sd2 < 0]
sd.ws ## L withinstudy SD’s for the 2nd genotypic group
sd3
rnorm(L, sd, 2); sd3[sd3 < 0]
sd.ws ## L withinstudy SD’s for the 3rd genotypic group
With the means and standard deviations selected for the studies, we generate the three samples corresponding to the three genotypic groups for each study. The sample size considered at this step was also varied. We considered 8 sample size triplets (each triplet giving sample sizes for each of the three allelic groups), ranging from small to medium to very large .
Each of the scenarios described above was repeatedly generated 500 times (that is, we used 500 Monte Carlo iterations). At each iteration, the absolute difference between the values from the original sample, and the values obtained from either of the new approaches proposed in this paper was first calculated, and the mean difference across all studies was then calculated. The mean difference thus obtained was considered as the bias for the values corresponding to either of the new methods at each iteration. The average of the above bias across all Monte Carlo iterations was presented as the mean absolute bias for in the appendix tables A1 to A4. In the above case, the bias was calculated for the for each study.
At each iteration in our Monte Carlo analysis, we also conducted a meta analysis based on random effects model to calculate a mean effect size, WM (that is, a weighted average of the ’s across all studies) from the original sample and a mean effect size based on the ’s obtained via either of the newly proposed methods. The absolute difference between the two mean effect sizes was averaged across all iterations and was labeled as the mean absolute bias for WM. Thus, across different scenarios, we compared the bias of the ’s for each of the individual studies as well as the bias of the weightedaveraged estimate of obtained by a metaanalysis using the random effects model.
4 Simulation Results
Overall, the performance of the simulations based method was better than the crude approach, in all cases that we considered. In general, the performance of the simulations based approach improved with larger number of studies and larger sample sizes for the studies, although the same cannot be said for the crude approach. The simulations based method performed fairly well even when the underlying density was strongly skewed or heavily kurtotic, but when the underlying density substantially deviated from normal (as in the case of asymmetric bimodal density), the performance was slightly worse. The simulations based approach performed consistently well for all underlying effect sizes shown in table 1; however, the performance of the crude approach substantially worsened for larger effect sizes. We provide more details below.
Performance based on the number of studies. As hypothesized, the performance of both the methods improved, in general, when larger number of studies were included in the simulation analyses. For example, when the original data distribution was asymmetric bimodal, with a withinstudy standard deviation of 1, and with the means, and the corresponding sample sizes of the three genotypic groups being and , respectively, the bias for WM (the weighted average of effect sizes obtained using a metaanalysis based on random effects model) was , when the simulations based method was used and the number of studies was . For the same setting, when the number of studies was increased to , the corresponding bias reduced to (about reduction), and when the number of studies was further increased to , the bias was further reduced to (about reduction). A similar trend was observed for the crude approach also: the bias values corresponding to studysizes of , and were respectively , and , but the percent reductions, which amounted to and were much lower. Although the above pattern was observed in most of the scenarios there were certain cases, especially when the sample sizes were low, where the crude approach did worse with increasing number of studies. For example, when the original data distribution was strongly right skewed, with a withinstudy standard deviation of , and with the means, and the corresponding sample sizes of the three genotypic groups being and , respectively, the bias for WM went up from to and then changed to as the number of studies went up from to , and then to , when the crude approach was used. On the other hand, for the same setting, the corresponding bias values for the simulations based approach decreased from to ( reduction), and further to ( reduction) as the number of studies increased from to , and then to .
Performance based on the sample sizes within studies. When all other parameters were kept the same, the bias values for both the crude approach and the simulations based approach went down, in general, as the sample size values within each study was increased. For example, when the original data was generated from the strongly right skewed density, with the three genotypic group means as , with withinstudy standard deviation as and number of studies equals , the mean absolute bias for WM based on the simulations approach decreased from to ( reduction), and then to ( reduction), as the withinstudy sample size triplets increased from (small) to (medium), and then to (large); the corresponding bias values based on the crude approach were , ( reduction) and ( reduction), respectively (See appendix table A2b). However there were a few scenarios for which the bias values based on the crude approach decreased initially as the sample sizes increased from small to medium, but then increased as the sample sizes increased from medium to large, even as the bias based on the simulations based approach reduced steadily as the sample sizes increased. The following example based on appendix table A1c illustrates the above point. With the underlying data normally distributed, with the three genotypic group means as , with withinstudy standard deviation as and number of studies equal to , the bias values based on the crude approach based on the sample size triplets , and were respectively, , and (that is, an initial reduction of , but then an increase of ). Within the same setting and the same sample sizes, the bias based on the simulations approach decreased steadily from to , and then to .
Performance based on the underlying densities. As expected, when the underlying data distribution is normal, both the methods work better than when the data distribution is not normal. When the underlying distribution was asymmetric bimodal, the bias values of both the methods were somewhat larger compared to other distributions. When the underlying distribution was either strongly right skewed or heavily kurtotic, the bias values were slightly larger in general, yet comparable to those from the normal data distribution. For example, with a withinstudy standard deviation of , with the means for the three genotypic groups as , and the sample sizes as , the mean absolute bias for WM, using the crude approach, for the four densities (normal), (strongly right skewed), (asymmetric bimodal) and (heavily kurtotic) are respectively and ; for the simulations based approach the corresponding four bias values were respectively and . As mentioned above, the values under and were similar to those for , but values for were much larger. The pattern remained the same even for larger sample sizes. With all the parameters exactly same as in the above example, but with sample sizes , the four bias values corresponding to the densities to for the crude approach were and , and for the simulations based approach were and .
Performance based on effect sizes. For small effect sizes, although the simulations based method edged out slightly over the crude approach, the performance of both the methods were more or less the same. For example, when the underlying density was strongly right skewed and number of studies was (appendix table A2b), with a withinstudy standard deviation of and means (that is, an effect size approximately ; see table 1), when the sample size triplet was , the bias value for the crude approach was and for the simulations based approach was . As the sample size increased for the same underlying effect size, the performance of both the methods improved substantially, even though the simulations based method still edged out a little bit  for the same parameters as above but with the sample size triplet as , the corresponding bias values were and . As the underlying effect size increased, the performance of the crude approach got markedly worse than that of the simulations based approach, as seen by the examples that follow. When the underlying density was strongly right skewed and number of studies was , with a withinstudy standard deviation of and means (that is, an effect size approximately ), when the sample size triplet was , the bias value for the crude approach was and for the simulations based approach was . With same underlying density, same number of studies and same sample size triplet as in the above example, but with the withinstudy standard deviation as and the mean triplet as (effect size approximately ), the corresponding bias values for the two methods were and .
5 Real Data Example
In order to further assess the performance of the methods proposed in this paper, we applied them to a real data example for which the subjectlevel data was also available for the studies included in the meta analysis. This example is part of the analyses reported by Zhang and coauthors. Here we include only pertinent details sufficient to illustrate the methods proposed in the present paper and assess their performances. More details can be found in Zhang et al’s paper. The goal of metaanalysis was to determine specific genetic variants associated with weight gain related to antipsychotic drugs. Primary outcome was change in body mass index (BMI). Literature search with inclusion/exclusion criteria narrowed down reports from independent samples. For among these independent cohorts, patientlevel data were available. The three cohorts were the SecondGeneration Antipsychotic Treatment Indications, Effectiveness and Tolerablity in Youth (SATIETY) study, European First Episode Schizophrenia Trial (EUFEST), and Zucker Hillside Hospital First Episode Schizophrenia Trial (ZHHFE). Since patientlevel data were available for these three cohorts, we could directly assess the additive effect of the alleles and compare it with the additive effects obtained from the crude and simulations based approaches (proposed in this paper) which utilizes reported summary means, standard deviations and sample sizes only. In the analysis reported in Zhang et al, SNP in the ADRA2A (adrenoceptor alpha 2A) gene was significantly associated with antipsychotic drug induced weight gain across 6 studies, including the 3 studies mentioned above, with the G allele increasing the risk. The SNP is located in the upstream of ADRA2A, and may be a binding site for transcription factors. The reported summary data for the three studies mentioned above, for the allelic groups for the SNP are presented in the table 2 below; the subscripts 1, 2 and 3 correspond to the genotypes , and .
Table 2. Reported Summary Statistics for the 3 cohorts in the real data example  
Study/Cohort  
SATIETY 
11.45  12.16  14.73  8.29  8.38  9.63  63  63  42 
EUFEST  4.04  5.35  4.67  5.11  5.88  6.44  74  40  9 
ZHHFE  3.24  2.44  3.64  2.11  1.23  2.42  25  24  21 

Table 3 below shows the ’s (numerator of Cohen’s ), standard deviation of the ’s (denominator of Cohen’s ) and the Cohen’s ’s based on the patient level data, the crude approach and the simulations based approach. The values obtained using the simulations approach match very closely with the values calculated using patient level data, while as the values based on crude approach differed substantially. All the ’s and all the Cohen’s ’s from the simulations approach differed from those from the patientlevel data only by or less (percent error ranging from to ). On the other hand, the percent error for crude approach ranged from to .
Table 3. Effect Sizes based on patientlevel data and the two methods proposed in the paper  

Patient Level Data  Crude Approach  Simulations Approach  
Study/Cohort  
SATIETY 
1.552  8.661  0.179  1.625  8.675  0.187  1.563  8.680  0.180 
EUFEST  0.746  5.460  0.137  0.313  5.470  0.057  0.742  5.474  0.136 
ZHHFE  0.168  2.009  0.084  0.199  1.965  0.101  0.171  2.009  0.085 

6 The case when the phenotype of interest is a binary variable.
The methods that we have discussed so far are applicable only when the phenotype of interest is a continuous variable, like for example, change in body weight. Sometimes, the phenotype of interest is a binary variable, and in such cases odds ratios or relative risks comparing the genotypic groups are reported. That is, if we denote the genotypic groups as, say, AA, AB and BB, then usually an odds ratio for the dichotomous phenotype of interest comparing AB and AA, and another comparing BB and AB are reported, and typically these two odds ratios differ from each other. Now the question is, if we do a metaanalysis with additive genetic model as the underlying model, how do we combine the pair of odds ratios reported within a study to get a single odds ratio for the additive model within that study. In this section, we present methods that addresses this question. Although we focus on odds ratios to explain our method, a very similar method can be worked out for relative risk as well. The method that we present here is based on a simple adaptation of the reconstruction of fourfold cell frequencies for metaanalysis by Di Pietrantonj. This reconstruction was further studied in Veroniki and coauthors8.
An overview of our adaptation is easy to describe:
Step 1): Within each study, reconstruct the table for each odds ratio using Di Pietrantonj’s method. Thus, after reconstruction, with our above notation, there will be a table comparing the dichotomous phenotype of interest between AB and AA, and another one comparing BB and AB. Note that Di Pietrantonj’s method can possibly give rise to two tables for each odds ratio. Di Pietrantonj, and Veroniki (and coauthors) select the correct one among these two, for each odds ratio, using a cutoff based on the event rate in the ”treatment group or the control group” ( in our case, this would be event rate in either of the genotype group). Since, in genetic association studies, this event rate is rarely reported, we use a different approach to select the correct table for each odds ratio. More details are provided further down.
Step 2): Once the tables, one for each odds ratio within a study, are selected, we merge them to get a table. That is, three rows, one for each genotypic group, and two columns, one for each category of the dichotomous variable.
Step 3): Generate (via simulations) two variables  1) the binary variable for the phenotype of interest and 2) a categorical indicator variable for the three genotype groups  that reflect exactly the cell frequencies in the table obtained in step 2 above.
Step 4): Run a logistic regression with (the logit of) the simulated binary outcome variable as the dependent variable and the simulated 3genotypegroups indicator as the independent variable. Exponentiate the coefficient for the independent variable to get the combined odds ratio for the genetic additive model. Standard errors for the can be utilized to calculated the confidence intervals for the combined odds ratio.
We explain and illustrate the details of the above approach using an example. Before we get to the details of our approach, we describe the example first. We generated sample data for each of the three genotype groups (AA, AB, BB) with sample size 30 in each group, that eventually yielded the following tables:
Table 4a.  phenotype present  phenotype absent 

AB  18  12 
AA  10  20 
Table. 4b.  phenotype present  phenotype absent 

BB  18  12 
AB  18  12 
Details of how we generated the data and how we obtained the above tables from the data are somewhat irrelevant to our discussion, but a reader interested in these can find the details in the appendix B.
Thus, in our example, the odds ratio comparing AB with AA is and the odds ratio comparing BB with AB is . If we use the formula
for the standard error of the logoddsratio (here, and denote the cell frequencies of a generic table), then we get the corresponding confidence intervals for the two odds ratios as and . Typically, the above odds ratios and confidence intervals are the only information reported when the results from the study’s analysis are published (and hence the only information available for the metaanalyst). The tables shown in Table 4 above are typically not reported.
However, someone who has the original data for this study (that is, the data that we simulated for this example and that we pretend as from an original study) could use a logistic regression with the binary phenotype (present/absent) variable as the dependent variable and the 3genotypegroups indicator as the independent variable and then exponentiate the corresponding to obtain the odds ratio with confidence interval for the additive genetic model as (or with more digits). If this (combined) odds ratio is available for the metaanalyst, then s/he doesn’t have to look further (that is, s/he won’t need the method described in this section). However, the combined odds ratio is seldom reported, but the two separate odds ratios (and their confidence intervals) for pairs of genotypic group comparisons are the ones that are typically reported. The method described in this section would help the metaanalyst to recover/estimate the unknown (unknown to the metaanalyst, but not perhaps to the original data analyst), combined odds ratio (, in our example) and the confidence interval, from the two reported odds ratios (3.00 for AB vs. AA and 1.00 for BB vs. AB) and their corresponding confidence intervals.
For a generic table given below,
Generic  phenotype  phenotype  genotype group 

Table  present  absent  totals 
Genotype group 2  a  b  
Genotype group 1  c  d 
Di Pietrantonj’s formula for recovering is
where
OR in these formulas denote the odds ratio obtained from the generic table and is the estimate of the standard error of obtained from the upper limit, UL, and lower limit, LL of the confidence interval, as
Based on the estimated, we can obtain and as
Note that there are two possible solutions for , leading to two possible values for and , and hence two possible estimates for the unknown table. Let us denote the cell entries in the two possible tables as and . As mentioned above, Di Pietrantonj and later Veroniki and coauthors devised methods based on event rates for one of the marginal groups that would help to decide on which among these possible tables to be picked. Such event rates are sometimes reported for epidemiological studies or clinical trials, but rarely for genetic association studies. We devised a simple scheme, more suitable to our context, that’d help us recover the correct table.
Recall that there are two odds ratios that we are considering  one for AB vs. AA and the other for BB vs. AB, denoted from now on as and , respectively. For each of these odds ratios, Di Pietrantonj’s formulas give two tables. Thus in total there are four tables. Our scheme for selecting the correct tables is best explained by working it out for the example that we are considering at the moment. For this example, the two possible tables for are
Table 5a.  phenotype present  phenotype absent 

AB  18  12 
AA  10  20 
Table. 5b.  phenotype present  phenotype absent 

AB  20  10 
AA  12  18 
and for are
Table 6a.  phenotype present  phenotype absent 

BB  12  18 
AB  12  18 
Table 6b.  phenotype present  phenotype absent 

BB  18  12 
AB  18  12 
There are four different ways to match the tables:
Table 5a Table 6a, Table 5a Table 6b, Table 5b Table 6a, Table 5b Table 6b.
A careful look at all the tables will immediately reveal that among the four combinations that match a table corresponding to with one of the tables corresponding to , the only one that makes sense (that is, plausible) is Table 5a Table 6b, because this is the only pair where the rows corresponding to AB match. In all the other three tablematchingcombinations, the estimates for the row corresponding to the AB genotype group doesn’t match.
In this particular example, there was one combination (Table 5a Table 6b) where the rows for AB matched exactly. But, there could be other examples, where the ABrows do not match exactly for any of the combinations. In such cases, we can calculate the “distance” for the corresponding ABrows for all four table match combinations listed above (e.g. the “distance” could be the Euclidean distance, if one may consider the pair of values for the ABrow as a 2dimensional vector), and select the combination where this distance is the minimum. Once we decide on the tablecombination based on this criteria, then we can take the columnaverage of the corresponding ABrows to get the ABrow for the final table. The distances calculated for our example is given below:
Distance table.  Distance between the corresponding AB rows 

Table 5a Table 6a  
Table 5a Table 6b  
Table 5b Table 6a  
Table 5b Table 6b 
Again, based on this minimum distance criterion, the combination that we will select is Table 5a Table 6b. In this particular example, the ABrows for Table 5a and Table 6b are the same, and . Hence the average is also , so that by merging Table 5a and Table 6b, we get the final table as
Table 7.  phenotype present  phenotype absent 

BB  18  12 
AB  18  12 
AA  10  20 
This completes step 2. In the next step we generate a phenotype (present/absent) variable and a 3genotypegroup indicator variable using the following R codes:
al.gp.ind
c(rep(1, 30), rep(2, 30), rep(3, 30))
# 30 is the sample size for each genotype group
ev.ind
c(sample(c(rep(1, 10), rep(0, 20))),
sample(c(rep(1, 18), rep(0, 12))),
sample(c(rep(1, 18), rep(0, 12))))
In the R codes above, ev.ind
is the phenotype (present/absent) variable with 1 = present and 0 = absent, and al.gp.ind
is the genotypegroupindicator variable with 1 = AA, 2 = AB and 3 = BB. This completes step 3. In the final step, we run a logistic regression with ev.ind
as the dependent binary variable and al.gp.ind
as the independent variable, and then exponentiate the for al.gp.ind
to get the combined odds ratio for the additive genetic model as . This is remarkably very close to the unknown combined odds ratio from the original study in our example, . The difference is less than . Using the standard error for the , we calculate/recover the confidence interval as , which is also remarkably close to the original interval . Thus our slight adaptation of Di Pietrantonj’s method works very well for this example. We did not conduct extensive simulations for the method presented in this section, since Di Pietrantonj and Veroniki and coauthors have studied elaborately the core method.
7 Conclusion
Meta analysis is being increasingly used to estimate the phenotype differences between genotype groups from genetic association studies. When the underlying genetic model is dominant or recessive, summary statistics from individual studies can be combined to get the pooled estimate in the metaanalysis. However, we show via simulations that when the underlying model is additive, the pooled estimate based on summary statistics leads to biased results. Since, data from individual studies is rarely available for the metaanalyst, we recommend using simulated data based on the summary statistics. We show in this paper that the method based on such simulations leads to much improved results.
8 Acknowledgements
Majnu John's work was supported in part by grants from the National Institute of Mental Health for an Advanced Center for Intervention and Services Research (P30 MH090590) and a Center for Intervention Development and Applied Research (P50 MH080173).
9 References
References
 (1) Lewis CM. Genetic association studies: design, analysis and interpretation. Brief Bioinform. 2002 Jun;3(2):14653. Review.
 (2) Zhang JP, Lencz T, Zhang RX, Nitta M, Maayan L, John M, Robinson DG, Fleischhacker WW, Kahn RS, Ophoff RA, Kane JM, Malhotra AK, Correll CU. Pharmacogenetic Associations of Antipsychotic DrugRelated Weight Gain: A Systematic Review and Metaanalysis. Schizophr Bull. 2016 Nov;42(6):14181437.
 (3) Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Metaanalysis. Chichester (UK): John Wiley Sons, 2009.
 (4) Hedges LV, Shymansky KA, Woodworth G. Practical guide to modern methods of Metaanalysis. National Science Teachers Association, 1989.
 (5) Marron S, and Wand M. Exact Mean Integrated Squared Error. Annals of Statistcs. 1992 20: 712736.
 (6) John M, Priebe CE. A dataadaptive methodology for finding an optimal weighted generalized MannWhitneyWilcoxon statistic. Computational Statistics Data Analysis. 2007 51: 43374353
 (7) Di Pietrantonj C. Fourfold table cell frequencies imputation in meta analysis. Statistics in Medicine. 2006 25: 22992322.
 (8) Veroniki AA, Pavlides M, Patsopoulos NA, Salanti G. Reconstructing contingency tables from odds ratios using the Di Pietrantonj method: difficulties, constraints and impact in metaanalysis results. Res Synth Methods. 2013 Mar;4(1):7894.
Appendix A Tables of simulation results
Table A1a. Original Data from standard normal density  
Number of Studies = 5  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.3829  0.2299  0.1995  0.0966 
15  20  10  0.3510  0.2203  0.1603  0.0794  
15  20  30  0.2972  0.1701  0.1272  0.0687  
15  45  30  0.3706  0.2579  0.1253  0.0623  
35  45  30  0.3290  0.2498  0.1055  0.0497  
75  100  60  0.3066  0.2401  0.0700  0.0298  
150  200  120  0.2764  0.2240  0.0472  0.0239  
300  400  240  0.2902  0.2508  0.0315  0.0164  
9  10  15  5  0.6053  0.4559  0.2209  0.1099  
15  20  10  0.4947  0.4000  0.1819  0.0881  
15  20  30  0.4005  0.2902  0.1394  0.0754  
15  45  30  0.4482  0.3438  0.1345  0.0670  
35  45  30  0.4907  0.4231  0.1156  0.0515  
75  100  60  0.4722  0.4286  0.0787  0.0315  
150  200  120  0.4660  0.4344  0.0529  0.0257  
300  400  240  0.4899  0.4528  0.0376  0.0196  
11  10  15  5  0.9037  0.7497  0.2483  0.1199  
15  20  10  0.7837  0.6901  0.1916  0.0997  
15  20  30  0.6235  0.5160  0.1568  0.0840  
15  45  30  0.6453  0.5342  0.1563  0.0845  
35  45  30  0.7449  0.6751  0.1282  0.0589  
75  100  60  0.7291  0.6827  0.0867  0.0318  
150  200  120  0.7380  0.7120  0.0564  0.0290  
300  400  240  0.7850  0.7387  0.0419  0.0212  
5  4  5.5  7  10  15  5  0.2085  0.0961  0.1988  0.0935 
15  20  10  0.1679  0.0864  0.1610  0.0773  
15  20  30  0.1417  0.0714  0.1303  0.0656  
15  45  30  0.1522  0.0729  0.1327  0.0590  
35  45  30  0.1026  0.0444  0.0950  0.0395  
75  100  60  0.0843  0.0434  0.0681  0.0292  
150  200  120  0.0576  0.0266  0.0464  0.0203  
300  400  240  0.0516  0.0268  0.0320  0.0154  
9  10  15  5  0.2224  0.1033  0.2013  0.0920  
15  20  10  0.1785  0.0922  0.1621  0.0772  
15  20  30  0.1490  0.0766  0.1336  0.0682  
15  45  30  0.1542  0.0680  0.1326  0.0604  
35  45  30  0.1142  0.0532  0.0976  0.0401  
75  100  60  0.0981  0.0590  0.0699  0.0294  
150  200  120  0.0667  0.0458  0.0328  0.0156  
300  400  240  0.0726  0.0428  0.0479  0.0209  
11  10  15  5  0.2444  0.1262  0.2050  0.0902  
15  20  10  0.1966  0.1068  0.1632  0.0768  
15  20  30  0.1571  0.0813  0.1383  0.0704  
15  45  30  0.1608  0.0703  0.1335  0.0625  
35  45  30  0.1326  0.0714  0.1014  0.0417  
75  100  60  0.1191  0.0838  0.0721  0.0296  
150  200  120  0.0967  0.0719  0.0493  0.0214  
300  400  240  0.0935  0.0731  0.0337  0.0158 
Table A1b. Original Data from standard normal density  
Number of Studies = 10  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4157  0.1933  0.2326  0.0764 
15  20  10  0.3377  0.1787  0.1777  0.0552  
15  20  30  0.3083  0.1707  0.1342  0.0474  
15  45  30  0.3551  0.2128  0.1297  0.0462  
35  45  30  0.3108  0.2279  0.0998  0.0338  
75  100  60  0.2816  0.2125  0.0705  0.0222  
150  200  120  0.2853  0.2324  0.0494  0.0145  
300  400  240  0.2712  0.2183  0.0371  0.0130  
9  10  15  5  0.6007  0.4092  0.2432  0.0787  
15  20  10  0.4995  0.3650  0.1938  0.0586  
15  20  30  0.3896  0.2476  0.1633  0.0583  
15  45  30  0.4670  0.3326  0.1475  0.0616  
35  45  30  0.4782  0.3970  0.1169  0.0420  
75  100  60  0.4540  0.4019  0.0786  0.0263  
150  200  120  0.4658  0.4253  0.0554  0.0148  
300  400  240  0.4688  0.4372  0.0399  0.0136  
11  10  15  5  0.9001  0.7244  0.2559  0.0768  
15  20  10  0.7728  0.6490  0.1991  0.0620  
15  20  30  0.5594  0.4099  0.1855  0.0729  
15  45  30  0.6814  0.5550  0.1595  0.0610  
35  45  30  0.7433  0.6610  0.1279  0.0456  
75  100  60  0.7045  0.6577  0.0850  0.0300  
150  200  120  0.7453  0.7031  0.0617  0.0176  
300  400  240  0.7683  0.7344  0.0433  0.0138  
5  4  5.5  7  10  15  5  0.2234  0.0685  0.2186  0.0695 
15  20  10  0.1732  0.0540  0.1672  0.0482  
15  20  30  0.1353  0.0487  0.1227  0.0467  
15  45  30  0.1370  0.0438  0.1216  0.0405  
35  45  30  0.1007  0.0330  0.0939  0.0283  
75  100  60  0.0812  0.0315  0.0699  0.0235  
150  200  120  0.0606  0.0194  0.0486  0.0145  
300  400  240  0.0520  0.0221  0.0343  0.0107  
9  10  15  5  0.2335  0.0722  0.2220  0.0695  
15  20  10  0.1818  0.0632  0.1705  0.0492  
15  20  30  0.1400  0.0507  0.1256  0.0479  
15  45  30  0.1396  0.0466  0.1249  0.0434  
35  45  30  0.1102  0.0410  0.0969  0.0277  
75  100  60  0.0944  0.0408  0.0706  0.0239  
150  200  120  0.0749  0.0381  0.0498  0.0143  
300  400  240  0.0665  0.0419  0.0352  0.0108  
11  10  15  5  0.2530  0.0935  0.2241  0.0692  
15  20  10  0.1985  0.0842  0.1727  0.0504  
15  20  30  0.1464  0.0555  0.1308  0.0501  
15  45  30  0.1492  0.0528  0.1289  0.0461  
35  45  30  0.1278  0.0591  0.0999  0.0280  
75  100  60  0.1164  0.0736  0.0714  0.0244  
150  200  120  0.0979  0.0667  0.0510  0.0141  
300  400  240  0.0927  0.0714  0.0361  0.0111 
Table A1c. Original Data from standard normal density  
Number of Studies = 15  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4404  0.1842  0.2143  0.0738 
15  20  10  0.3529  0.1937  0.1619  0.0369  
15  20  30  0.3204  0.1837  0.1329  0.0329  
15  45  30  0.3502  0.2064  0.1380  0.0337  
35  45  30  0.3003  0.2071  0.1000  0.0269  
75  100  60  0.2791  0.2014  0.0718  0.0213  
150  200  120  0.3066  0.2366  0.0494  0.0121  
300  400  240  0.2952  0.2396  0.0368  0.0110  
9  10  15  5  0.6261  0.3907  0.2375  0.0803  
15  20  10  0.5208  0.3784  0.1799  0.0401  
15  20  30  0.4280  0.2952  0.1510  0.0407  
15  45  30  0.4476  0.3060  0.1558  0.0403  
35  45  30  0.4666  0.3795  0.1124  0.0298  
75  100  60  0.4278  0.3717  0.0797  0.0261  
150  200  120  0.5009  0.4542  0.0560  0.0155  
300  400  240  0.4818  0.4419  0.0422  0.0124  
11  10  15  5  0.9440  0.7006  0.2601  0.0829  
15  20  10  0.7859  0.6528  0.2014  0.0450  
15  20  30  0.6324  0.4832  0.1736  0.0523  
15  45  30  0.6350  0.4979  0.1701  0.0446  
35  45  30  0.7311  0.6449  0.1214  0.0371  
75  100  60  0.6668  0.6208  0.0873  0.0283  
150  200  120  0.7919  0.7504  0.0614  0.0183  
300  400  240  0.7577  0.7195  0.0460  0.0135  
5  4  5.5  7  10  15  5  0.2224  0.0697  0.2110  0.0645 
15  20  10  0.1698  0.0394  0.1589  0.0317  
15  20  30  0.1350  0.0409  0.1244  0.0368  
15  45  30  0.1439  0.0381  0.1263  0.0324  
35  45  30  0.1032  0.0283  0.0960  0.0273  
75  100  60  0.0805  0.0262  0.0712  0.0213  
150  200  120  0.0607  0.0193  0.0485  0.0134  
300  400  240  0.0506  0.0196  0.0347  0.0095  
9  10  15  5  0.2355  0.0777  0.2137  0.0654  
15  20  10  0.1778  0.0482  0.1604  0.0322  
15  20  30  0.1399  0.0434  0.1286  0.0372  
15  45  30  0.1483  0.0409  0.1295  0.0333  
35  45  30  0.1124  0.0337  0.0984  0.0280  
75  100  60  0.0917  0.0402  0.0723  0.0213  
150  200  120  0.0748  0.0381  0.0495  0.0136  
300  400  240  0.0659  0.0378  0.0356  0.0099  
11  10  15  5  0.2580  0.0997  0.2151  0.0656  
15  20  10  0.1939  0.0678  0.1623  0.0327  
15  20  30  0.1464  0.0444  0.1336  0.0383  
15  45  30  0.1577  0.0480  0.1331  0.0341  
35  45  30  0.1277  0.0475  0.1010  0.0289  
75  100  60  0.1107  0.0651  0.0736  0.0215  
150  200  120  0.0991  0.0662  0.0505  0.0137  
300  400  240  0.0907  0.0664  0.0366  0.0103 
Table A2a. Original Data from strongly right skewed density  
Number of Studies = 5  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.3808  0.2275  0.2511  0.1179 
15  20  10  0.3704  0.2077  0.1987  0.0935  
15  20  30  0.2933  0.1860  0.1419  0.0744  
15  45  30  0.4627  0.3399  0.1297  0.0640  
35  45  30  0.3384  0.2615  0.1136  0.0598  
75  100  60  0.2580  0.1966  0.0835  0.0393  
150  200  120  0.2727  0.2134  0.0556  0.0268  
300  400  240  0.3476  0.2986  0.0416  0.0203  
9  10  15  5  0.5351  0.3683  0.2822  0.1350  
15  20  10  0.5413  0.3808  0.2308  0.1152  
15  20  30  0.4209  0.2793  0.1828  0.0914  
15  45  30  0.5838  0.4453  0.1520  0.0755  
35  45  30  0.4889  0.4182  0.1377  0.0746  
75  100  60  0.4375  0.3800  0.0954  0.0446  
150  200  120  0.4442  0.4113  0.0629  0.0279  
300  400  240  0.5385  0.4977  0.0515  0.0267  
11  10  15  5  0.7905  0.6018  0.3165  0.1513  
15  20  10  0.8277  0.6335  0.2590  0.1273  
15  20  30  0.6337  0.4587  0.2109  0.1026  
15  45  30  0.7701  0.6212  0.1811  0.1001  
35  45  30  0.7286  0.6331  0.1609  0.0867  
75  100  60  0.7081  0.6646  0.1038  0.0490  
150  200  120  0.7416  0.7093  0.0722  0.0328  
300  400  240  0.8530  0.8260  0.0550  0.0298  
5  4  5.5  7  10  15  5  0.2323  0.1054  0.2224  0.0915 
15  20  10  0.1687  0.0760  0.1632  0.0774  
15  20  30  0.1344  0.0666  0.1236  0.0622  
15  45  30  0.1443  0.0655  0.1279  0.0534  
35  45  30  0.1138  0.0527  0.1058  0.0486  
75  100  60  0.0888  0.0396  0.0778  0.0340  
150  200  120  0.0676  0.0391  0.0517  0.0240  
300  400  240  0.0532  0.0311  0.0347  0.0157  
9  10  15  5  0.2498  0.1139  0.2300  0.0957  
15  20  10  0.1822  0.0842  0.1724  0.0824  
15  20  30  0.1416  0.0746  0.1326  0.0684  
15  45  30  0.1528  0.0655  0.1324  0.0560  
35  45  30  0.1251  0.0616  0.1104  0.0537  
75  100  60  0.1021  0.0510  0.0803  0.0344  
150  200  120  0.0835  0.0591  0.0533  0.0242  
300  400  240  0.0721  0.0510  0.0372  0.0172  
11  10  15  5  0.2725  0.1320  0.2397  0.1021  
15  20  10  0.2000  0.0995  0.1802  0.0854  
15  20  30  0.1519  0.0813  0.1428  0.0732  
15  45  30  0.1613  0.0724  0.1392  0.0589  
35  45  30  0.1433  0.0741  0.1153  0.0587  
75  100  60  0.1232  0.0742  0.0834  0.0349  
150  200  120  0.1085  0.0892  0.0557  0.0248  
300  400  240  0.1006  0.0808  0.0396  0.0187 
Table A2b. Original Data from strongly right skewed density  
Number of Studies = 10  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4718  0.2340  0.2556  0.0832 
15  20  10  0.3490  0.2008  0.1856  0.0631  
15  20  30  0.3304  0.1959  0.1436  0.0450  
15  45  30  0.4147  0.2617  0.1481  0.0520  
35  45  30  0.3467  0.2429  0.1172  0.0432  
75  100  60  0.2840  0.2164  0.0859  0.0274  
150  200  120  0.2817  0.1962  0.0555  0.0183  
300  400  240  0.2703  0.2227  0.0388  0.0139  
9  10  15  5  0.6651  0.4418  0.2747  0.0926  
15  20  10  0.5253  0.3661  0.2243  0.0765  
15  20  30  0.4522  0.2946  0.1760  0.0545  
15  45  30  0.5396  0.3827  0.1763  0.0689  
35  45  30  0.5122  0.4274  0.1362  0.0495  
75  100  60  0.4682  0.4082  0.0970  0.0286  
150  200  120  0.4416  0.3939  0.0620  0.0210  
300  400  240  0.4437  0.4107  0.0449  0.0168  
11  10  15  5  0.9679  0.7447  0.2990  0.1016  
15  20  10  0.7988  0.6482  0.2401  0.0855  
15  20  30  0.6595  0.4927  0.1987  0.0601  
15  45  30  0.7407  0.5690  0.2088  0.0803  
35  45  30  0.7808  0.7001  0.1531  0.0512  
75  100  60  0.7543  0.6962  0.1040  0.0308  
150  200  120  0.7155  0.6840  0.0668  0.0261  
300  400  240  0.7349  0.7039  0.0495  0.0186  
5  4  5.5  7  10  15  5  0.2418  0.0723  0.2241  0.0684 
15  20  10  0.1705  0.0549  0.1639  0.0511  
15  20  30  0.1395  0.0458  0.1283  0.0407  
15  45  30  0.1456  0.0473  0.1214  0.0426  
35  45  30  0.1094  0.0431  0.1010  0.0358  
75  100  60  0.0836  0.0311  0.0720  0.0212  
150  200  120  0.0616  0.0253  0.0485  0.0163  
300  400  240  0.0482  0.0225  0.0337  0.0117  
9  10  15  5  0.2530  0.0812  0.2277  0.0693  
15  20  10  0.1818  0.0631  0.1701  0.0539  
15  20  30  0.1481  0.0448  0.1338  0.0423  
15  45  30  0.1513  0.0492  0.1273  0.0453  
35  45  30  0.1198  0.0535  0.1028  0.0362  
75  100  60  0.0969  0.0468  0.0744  0.0217  
150  200  120  0.0742  0.0419  0.0498  0.0177  
300  400  240  0.0628  0.0404  0.0352  0.0122  
11  10  15  5  0.2736  0.1043  0.2323  0.0700  
15  20  10  0.2003  0.0754  0.1760  0.0558  
15  20  30  0.1593  0.0452  0.1411  0.0439  
15  45  30  0.1610  0.0569  0.1348  0.0490  
35  45  30  0.1395  0.0727  0.1056  0.0357  
75  100  60  0.1194  0.0744  0.0771  0.0224  
150  200  120  0.0967  0.0700  0.0514  0.0190  
300  400  240  0.0861  0.0679  0.0368  0.0125 
Table A2c. Original Data from strongly right skewed density  
Number of Studies = 15  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4125  0.2003  0.2456  0.0814 
15  20  10  0.3673  0.1902  0.1871  0.0441  
15  20  30  0.3131  0.1634  0.1491  0.0477  
15  45  30  0.4248  0.2665  0.1403  0.0443  
35  45  30  0.2919  0.1947  0.1167  0.0340  
75  100  60  0.3022  0.2353  0.0713  0.0243  
150  200  120  0.2887  0.2166  0.0556  0.0132  
300  400  240  0.3033  0.2501  0.0362  0.0126  
9  10  15  5  0.6115  0.4281  0.2695  0.0799  
15  20  10  0.5609  0.3867  0.2168  0.0608  
15  20  30  0.4175  0.2576  0.1756  0.0563  
15  45  30  0.5572  0.4020  0.1652  0.0509  
35  45  30  0.4557  0.3743  0.1445  0.0393  
75  100  60  0.4956  0.4433  0.0816  0.0283  
150  200  120  0.4721  0.4225  0.0649  0.0178  
300  400  240  0.4903  0.4553  0.0431  0.0159  
11  10  15  5  0.9083  0.7306  0.2865  0.0821  
15  20  10  0.8513  0.6581  0.2434  0.0763  
15  20  30  0.6007  0.4358  0.2027  0.0642  
15  45  30  0.7771  0.6164  0.1854  0.0520  
35  45  30  0.7512  0.6585  0.1601  0.0440  
75  100  60  0.7873  0.7333  0.0896  0.0321  
150  200  120  0.7584  0.7153  0.0726  0.0239  
300  400  240  0.7852  0.7526  0.0491  0.0176  
5  4  5.5  7  10  15  5  0.2427  0.0703  0.2258  0.0631 
15  20  10  0.1779  0.0474  0.1732  0.0432  
15  20  30  0.1389  0.0390  0.1320  0.0383  
15  45  30  0.1490  0.0362  0.1260  0.0315  
35  45  30  0.1097  0.0348  0.1044  0.0289  
75  100  60  0.0758  0.0268  0.0668  0.0189  
150  200  120  0.0604  0.0182  0.0480  0.0110  
300  400  240  0.0521  0.0225  0.0332  0.0104  
9  10  15  5  0.2603  0.0918  0.2316  0.0636  
15  20  10  0.1905  0.0557  0.1798  0.0460  
15  20  30  0.1471  0.0424  0.1386  0.0396  
15  45  30  0.1533  0.0404  0.1320  0.0348  
35  45  30  0.1223  0.0476  0.1106  0.0309  
75  100  60  0.0885  0.0450  0.0689  0.0199  
150  200  120  0.0744  0.0382  0.0499  0.0121  
300  400  240  0.0697  0.0443  0.0348  0.0113  
11  10  15  5  0.2867  0.1237  0.2392  0.0651  
15  20  10  0.2109  0.0728  0.1878  0.0492  
15  20  30  0.1588  0.0460  0.1467  0.0411  
15  45  30  0.1612  0.0479  0.1393  0.0382  
35  45  30  0.1423  0.0673  0.1170  0.0322  
75  100  60  0.1116  0.0722  0.0713  0.0210  
150  200  120  0.0977  0.0677  0.0522  0.0137  
300  400  240  0.0967  0.0748  0.0365  0.0121 
Table A3a. Original Data from asymmetric bimodal density  
Number of Studies = 5  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4474  0.2842  0.2214  0.1132 
15  20  10  0.3959  0.2646  0.1746  0.0903  
15  20  30  0.3257  0.2465  0.1462  0.0800  
15  45  30  0.4514  0.3609  0.1366  0.0744  
35  45  30  0.3268  0.2500  0.1193  0.0661  
75  100  60  0.3091  0.2501  0.0866  0.0514  
150  200  120  0.2938  0.2506  0.0731  0.0511  
300  400  240  0.3109  0.2684  0.0694  0.0549  
9  10  15  5  0.6536  0.5111  0.2486  0.1185  
15  20  10  0.5830  0.4879  0.1939  0.0983  
15  20  30  0.4565  0.3746  0.1738  0.1017  
15  45  30  0.6238  0.5616  0.1634  0.0938  
35  45  30  0.5173  0.4797  0.1441  0.0951  
75  100  60  0.5008  0.4781  0.1010  0.0768  
150  200  120  0.4750  0.4525  0.0944  0.0796  
300  400  240  0.4774  0.4572  0.0953  0.0875  
11  10  15  5  0.9596  0.8128  0.2660  0.1255  
15  20  10  0.8741  0.7817  0.2143  0.1132  
15  20  30  0.7028  0.6035  0.1945  0.1149  
15  45  30  0.9031  0.8340  0.1903  0.1163  
35  45  30  0.8144  0.7876  0.1634  0.1228  
75  100  60  0.7995  0.7845  0.1186  0.0952  
150  200  120  0.7363  0.7202  0.1108  0.1001  
300  400  240  0.7428  0.7342  0.1136  0.1086  
5  4  5.5  7  10  15  5  0.2297  0.0972  0.2157  0.0952 
15  20  10  0.1742  0.0882  0.1671  0.0857  
15  20  30  0.1329  0.0660  0.1292  0.0640  
15  45  30  0.1582  0.0877  0.1302  0.0712  
35  45  30  0.1113  0.0571  0.1005  0.0525  
75  100  60  0.0861  0.0450  0.0747  0.0372  
150  200  120  0.0649  0.0411  0.0507  0.0287  
300  400  240  0.0598  0.0441  0.0442  0.0288  
9  10  15  5  0.2458  0.1125  0.2209  0.0975  
15  20  10  0.1848  0.1027  0.1676  0.0879  
15  20  30  0.1368  0.0664  0.1339  0.0662  
15  45  30  0.1602  0.0865  0.1356  0.0751  
35  45  30  0.1270  0.0800  0.1053  0.0610  
75  100  60  0.1050  0.0767  0.0792  0.0456  
150  200  120  0.0876  0.0711  0.0588  0.0402  
300  400  240  0.0862  0.0785  0.0532  0.0427  
11  10  15  5  0.2720  0.1443  0.2248  0.0993  
15  20  10  0.2094  0.1302  0.1686  0.0918  
15  20  30  0.1450  0.0711  0.1401  0.0712  
15  45  30  0.1647  0.0860  0.1422  0.0795  
35  45  30  0.1547  0.1193  0.1119  0.0708  
75  100  60  0.1347  0.1167  0.0841  0.0557  
150  200  120  0.1192  0.1089  0.0673  0.0524  
300  400  240  0.1237  0.1200  0.0631  0.0562 
Table A3b. Original Data from asymmetric bimodal density  
Number of Studies = 10  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4523  0.2457  0.2135  0.0889 
15  20  10  0.3894  0.2341  0.1819  0.0701  
15  20  30  0.3141  0.2013  0.1441  0.0752  
15  45  30  0.3725  0.2546  0.1363  0.0581  
35  45  30  0.3137  0.2492  0.1181  0.0602  
75  100  60  0.3209  0.2641  0.0887  0.0524  
150  200  120  0.3472  0.2926  0.0761  0.0480  
300  400  240  0.3588  0.3041  0.0625  0.0461  
9  10  15  5  0.6790  0.5392  0.2332  0.0945  
15  20  10  0.6293  0.5597  0.1983  0.1115  
15  20  30  0.4620  0.3713  0.1701  0.0968  
15  45  30  0.4931  0.4135  0.1536  0.0906  
35  45  30  0.4798  0.4423  0.1391  0.0873  
75  100  60  0.5533  0.5240  0.1074  0.0793  
150  200  120  0.5766  0.5568  0.1055  0.0946  
300  400  240  0.5523  0.5378  0.0906  0.0840  
11  10  15  5  0.9939  0.8866  0.2901  0.1455  
15  20  10  0.8933  0.8201  0.2084  0.1137  
15  20  30  0.6706  0.5891  0.2006  0.1131  
15  45  30  0.7514  0.6904  0.1825  0.1214  
35  45  30  0.7834  0.7431  0.1494  0.0990  
75  100  60  0.8285  0.8051  0.1326  0.1098  
150  200  120  0.8009  0.7893  0.1221  0.1127  
300  400  240  0.7994  0.7925  0.1072  0.1034  
5  4  5.5  7  10  15  5  0.2262  0.0786  0.2213  0.0727 
15  20  10  0.1741  0.0648  0.1725  0.0567  
15  20  30  0.1405  0.0463  0.1325  0.0427  
15  45  30  0.1364  0.0457  0.1197  0.0417  
35  45  30  0.1106  0.0456  0.1061  0.0375  
75  100  60  0.0866  0.0451  0.0746  0.0316  
150  200  120  0.0727  0.0435  0.0545  0.0261  
300  400  240  0.0570  0.0346  0.0398  0.0214  
9  10  15  5  0.2435  0.0941  0.2192  0.0693  
15  20  10  0.1846  0.0810  0.1724  0.0585  
15  20  30  0.1517  0.0502  0.1434  0.0529  
15  45  30  0.1505  0.0584  0.1330  0.0559  
35  45  30  0.1244  0.0645  0.1075  0.0377  
75  100  60  0.1083  0.0785  0.0764  0.0408  
150  200  120  0.0928  0.0733  0.0609  0.0369  
300  400  240  0.0904  0.0782  0.0534  0.0397  
11  10  15  5  0.2924  0.1567  0.2362  0.0953  
15  20  10  0.1970  0.1134  0.1654  0.0608  
15  20  30  0.1398  0.0558  0.1345  0.0660  
15  45  30  0.1454  0.0481  0.1298  0.0607  
35  45  30  0.1495  0.1042  0.1143  0.0540  
75  100  60  0.1403  0.1176  0.0873  0.0526  
150  200  120  0.1205  0.1085  0.0660  0.0501  
300  400  240  0.1305  0.1271  0.0619  0.0541 
Table A3c. Original Data from asymmetric bimodal density  
Number of Studies = 15  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4665  0.2749  0.2312  0.0802 
15  20  10  0.3695  0.2537  0.1786  0.0683  
15  20  30  0.3311  0.2384  0.1471  0.0652  
15  45  30  0.4150  0.3044  0.1397  0.0659  
35  45  30  0.3250  0.2479  0.1128  0.0479  
75  100  60  0.3188  0.2597  0.0931  0.0529  
150  200  120  0.3334  0.2703  0.0786  0.0519  
300  400  240  0.3371  0.2937  0.0655  0.0511  
9  10  15  5  0.6454  0.5243  0.2491  0.0996  
15  20  10  0.6392  0.5347  0.2050  0.0894  
15  20  30  0.4335  0.3251  0.1659  0.0753  
15  45  30  0.4990  0.4051  0.1632  0.0882  
35  45  30  0.5162  0.4633  0.1374  0.0770  
75  100  60  0.5730  0.5502  0.1028  0.0743  
150  200  120  0.5270  0.5108  0.0907  0.0776  
300  400  240  0.5399  0.5279  0.0882  0.0825  
11  10  15  5  0.9689  0.8433  0.2737  0.0884  
15  20  10  0.8685  0.7752  0.2066  0.0927  
15  20  30  0.6852  0.5711  0.1976  0.1001  
15  45  30  0.7605  0.6873  0.1766  0.1064  
35  45  30  0.8375  0.7917  0.1507  0.0964  
75  100  60  0.8397  0.8227  0.1215  0.0986  
150  200  120  0.9333  0.8284  0.1132  0.1017  
300  400  240  0.8358  0.8286  0.0993  0.0948  
5  4  5.5  7  10  15  5  0.2233  0.0789  0.2126  0.0679 
15  20  10  0.1793  0.0642  0.1698  0.0556  
15  20  30  0.1320  0.0369  0.1242  0.0374  
15  45  30  0.1399  0.0564  0.1248  0.0429  
35  45  30  0.1032  0.0358  0.0977  0.0299  
75  100  60  0.0841  0.0391  0.0747  0.0265  
150  200  120  0.0712  0.0432  0.0566  0.0277  
300  400  240  0.0620  0.0381  0.0432  0.0222  
9  10  15  5  0.2547  0.1045  0.2292  0.0663  
15  20  10  0.1826  0.0826  0.1655  0.0562  
15  20  30  0.1376  0.0466  0.1304  0.0484  
15  45  30  0.1502  0.0478  0.1320  0.0495  
35  45  30  0.1213  0.0678  0.1074  0.0432  
75  100  60  0.1103  0.0745  0.0795  0.0368  
150  200  120  0.0952  0.0761  0.0630  0.0372  
300  400  240  0.0829  0.0721  0.0479  0.0359  
11  10  15  5  0.2713  0.1487  0.2230  0.0762  
15  20  10  0.2018  0.1072  0.1702  0.0547  
15  20  30  0.1419  0.0502  0.1403  0.0650  
15  45  30  0.1453  0.0447  0.1300  0.0574  
35  45  30  0.1575  0.1141  0.1153  0.0562  
75  100  60  0.1440  0.1258  0.0828  0.0550  
150  200  120  0.1205  0.1083  0.0637  0.0428  
300  400  240  0.1207  0.1153  0.0602  0.0526 
Table A4a. Original Data from heavily kurtotic density  
Number of Studies = 5  Crude Approach  Simulations Approach  
Mean absolute  Mean absolute  Mean absolute  Mean absolute  
bias for g  bias for gWM  bias for g  bias for gWM  
1  4  5.5  7  10  15  5  0.4617  0.2848  0.2477  0.1173 
15  20  10  0.3794  0.2681  0.1833  0.0823  
15  20  30  0.3278  0.1952  0.1527  0.0698  
15  45  30  0.3809  0.2349  0.1434  0.0559  
35  45  30  0.3478  0.2534  0.1208  0.0598  
75  100  60  0.3805  0.3192  0.0825  0.0404  
150  200  120  0.3348  0.2887  0.0536  0.0259  
300  400  240  0.2578  0.1993  0.0388  0.0160  
9  10  15  5  0.7155  0.5305  0.2708  0.1407  
15  20  10  0.5752  0.4717  0.2082  0.0961  
15  20  30  0.4638  0.2877  0.1840  0.0862  
15  45  30  0.5120  0.3831  0.1587  0.0684  
35  45  30  0.5492  0.4414  0.1414  0.0712  
75  100  60  0.6003  0.5469  0.0996  0.0540  
150  200  120  0.5332  0.4889  0.0643  0.0322  
300  400  240  0.3970  0.3512  0.0465  0.0186  
11  10  15  5  1.0573  0.8815  0.2714  0.1471  
15  20  10  0.8831  0.7722  0.2264  0.0996  
15  20  30  0.6700  0.4892  0.2044  0.1016  
15  45  30  0.7480  0.6143  0.1681  0.0753  
35  45  30  0.8052  0.7034  0.1511  0.0784 