tips:testing_factors_lincoms

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The examples below show how to test factors and linear combinations of parameters in (mixed-effects) meta-regression models.

Meta-regression models can easily handle categorical predictors (factors) via appropriate coding of factors in terms of dummy variables. We can use the dataset for the BCG vaccine meta-analysis (Colditz et al., 1994) as an illustration.

The (log) risk ratios and corresponding sampling variances for the 13 studies can be obtained with:

library(metafor) dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) dat

trial author year tpos tneg cpos cneg ablat alloc yi vi 1 1 Aronson 1948 4 119 11 128 44 random -0.8893 0.3256 2 2 Ferguson & Simes 1949 6 300 29 274 55 random -1.5854 0.1946 3 3 Rosenthal et al 1960 3 228 11 209 42 random -1.3481 0.4154 4 4 Hart & Sutherland 1977 62 13536 248 12619 52 random -1.4416 0.0200 5 5 Frimodt-Moller et al 1973 33 5036 47 5761 13 alternate -0.2175 0.0512 6 6 Stein & Aronson 1953 180 1361 372 1079 44 alternate -0.7861 0.0069 7 7 Vandiviere et al 1973 8 2537 10 619 19 random -1.6209 0.2230 8 8 TPT Madras 1980 505 87886 499 87892 13 random 0.0120 0.0040 9 9 Coetzee & Berjak 1968 29 7470 45 7232 27 random -0.4694 0.0564 10 10 Rosenthal et al 1961 17 1699 65 1600 42 systematic -1.3713 0.0730 11 11 Comstock et al 1974 186 50448 141 27197 18 systematic -0.3394 0.0124 12 12 Comstock & Webster 1969 5 2493 3 2338 33 systematic 0.4459 0.5325 13 13 Comstock et al 1976 27 16886 29 17825 33 systematic -0.0173 0.0714

For easier interpretation of the results, we will "center" the publication year and absolute latitude moderators at their minimum values (1948 and 13 degrees, respectively):

dat$year <- dat$year - 1948 dat$ablat <- dat$ablat - 13

Now let's fit a mixed-effects meta-regression model to these data, including publication year (`year`

), absolute latitude (`ablat`

), and the method of treatment allocation (`alloc`

) as predictors/moderators.^{1)} The standard way of handling a factor in regression models is to create dummy variables representing the various levels. One of the dummies is then left out of the model, with the corresponding level becoming the "reference" level. Instead of creating the dummy variables manually, we can let R handle the dummy coding of the allocation factor for us:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat) res

Mixed-Effects Model (k = 13; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.1796 (SE = 0.1425) tau (square root of estimated tau^2 value): 0.4238 I^2 (residual heterogeneity / unaccounted variability): 73.09% H^2 (unaccounted variability / sampling variability): 3.72 R^2 (amount of heterogeneity accounted for): 42.67% Test for Residual Heterogeneity: QE(df = 8) = 26.2030, p-val = 0.0010 Test of Moderators (coefficient(s) 2,3,4,5): QM(df = 4) = 9.5254, p-val = 0.0492 Model Results: estimate se zval pval ci.lb ci.ub intrcpt -0.2324 0.5550 -0.4187 0.6754 -1.3201 0.8554 factor(alloc)random -0.3421 0.4180 -0.8183 0.4132 -1.1613 0.4772 factor(alloc)systematic 0.0101 0.4467 0.0226 0.9820 -0.8654 0.8856 year 0.0075 0.0194 0.3849 0.7003 -0.0306 0.0456 ablat -0.0236 0.0132 -1.7816 0.0748 -0.0495 0.0024 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

By default, `alternate`

was made the reference level (since the first letter "a" comes before "r" and "s"), so the corresponding dummy variable for this level has been left out.^{2)} Therefore, the model intercept (coefficients $b_0$) is the estimated (average) log risk ratio for alternate allocation in 1948 at 13 degrees absolute latitude. Coefficients $b_1$ and $b_2$ indicate how much lower/higher the estimated (average) log risk ratio is for random and systematic allocation, respectively, compared to alternate allocation. Finally, the coefficients for year and absolute latitude (i.e., $b_3$ and $b_4$) estimate how the (average) log risk ratio changes for a one-unit increase in the respective moderator.

The results under "Test of Moderators" indicate that we can (just) reject the null hypothesis $H_0: \beta_1 = \beta_2 = \beta_3 = \beta_4 = 0$ (i.e., this is a so-called omnibus test of coefficients 2 through 5 – the first being the intercept, which is typically denoted as $\beta_0$ in model equations and is not included in the test by default). In other words, it appears as if at least part of the heterogeneity in the true effects is related to some of the predictors/moderators included in the model.

Now consider the two coefficients for the allocation factor. Neither coefficient is significantly different from 0 (as indicated by the corresponding p-values), but this isn't a proper test of the factor as a whole. Instead, we want to simultaneously test whether both coefficients are simultaneously equal to 0. We can do this with:

anova(res, btt=2:3)

Test of Moderators (coefficient(s) 2,3): QM(df = 2) = 1.3663, p-val = 0.5050

This is a (Wald-type) chi-square test of the null hypothesis $H_0: \beta_1 = \beta_2 = 0$ with two degrees of freedom. It indicates that the factor as a whole is not significant.

The `car`

package includes the `linearHypothesis()`

function that can be used for the same purpose. One can either specify a matrix (or vector) of one or more linear combinations of coefficients to test or a character vector giving the hypothesis in symbolic form. Therefore, both of the following commands produce the same output:

library(car) linearHypothesis(res, rbind(c(0,1,0,0,0),c(0,0,1,0,0))) linearHypothesis(res, c("factor(alloc)random = 0", "factor(alloc)systematic = 0"))

Linear hypothesis test Hypothesis: factor(alloc)random = 0 factor(alloc)systematic = 0 Model 1: restricted model Model 2: res Res.Df Df Chisq Pr(>Chisq) 1 10 2 8 2 1.3663 0.505

The `multcomp`

package provides similar functionality. Here, the commands would be:

library(multcomp) summary(glht(res, linfct=rbind(b1=c(0,1,0,0,0), b2=c(0,0,1,0,0))), test=Chisqtest())

General Linear Hypotheses Linear Hypotheses: Estimate b1 == 0 -0.3421 b2 == 0 0.0101 Global Test: Chisq DF Pr(>Chisq) 1 1.366 2 0.505

The same results could also have been obtained directly if we had fitted the model with the command:

rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat, btt=2:3)

With `btt=2:3`

, only coefficients 2 and 3 will be included in the omnibus test (output not shown).

Note that the reference level chosen for a factor does not alter the results of the omnibus test. For example, we could make `random`

the reference level with:

res <- rma(yi, vi, mods = ~ relevel(factor(alloc), ref="random") + year + ablat, data=dat, btt=2:3)

The results of `anova(res, btt=2:3)`

are unchanged (output not shown).

We can also use likelihood ratio tests (LRT) to test sets of model coefficients. For this, we fit both the model with and the model without the treatment allocation factor (i.e., the "full" and the "reduced" model) and then conduct a model comparison. When the two models to be compared differ in terms of their fixed effects (i.e., model coefficients) – as in the present example – then one must fit both models using maximum likelihood (ML) estimation, and not restricted maximum likelihood (REML) estimation (which is the default). So, the LRT can be conducted with:

res0 <- rma(yi, vi, mods = ~ year + ablat, data=dat, method="ML") res1 <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat, method="ML") anova(res0, res1)

df AIC BIC AICc logLik LRT pval QE tau^2 R^2 Full 6 25.8412 29.2309 39.8412 -6.9206 26.2030 0.0329 Reduced 4 23.2922 25.5520 28.2922 -7.6461 1.4510 0.4841 28.3251 0.0269 0%

In the present example, the test statistic for the LRT is equal to $1.4510$, which (asymptotically) follows a chi-square distribution with two degrees of freedom (i.e., the difference in the number of parameters in the full and the reduced model) under the null hypothesis (i.e., that the reduced model fits the data as well as the full model). This leads to a p-value of $.48$, which does not lead us to reject the null hypothesis and hence to the conclusion that the inclusion of the allocation factor in the model does not provide a significantly better fit.

Knapp and Hartung (2003) described an adjustment to the standard Wald-type tests typically used in mixed-effects meta-regression models that leads to tests with better statistical properties (i.e., better control of the Type I error rate). For individual model coefficients, the method leads to t-test for individual model coefficients. The adjustment can also be applied when testing sets of model coefficients, which leads to F-tests. In the present example, the allocation factor can be tested with:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat, btt=2:3, test="knha") res

Mixed-Effects Model (k = 13; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.1796 (SE = 0.1425) tau (square root of estimated tau^2 value): 0.4238 I^2 (residual heterogeneity / unaccounted variability): 73.09% H^2 (unaccounted variability / sampling variability): 3.72 R^2 (amount of heterogeneity accounted for): 42.67% Test for Residual Heterogeneity: QE(df = 8) = 26.2030, p-val = 0.0010 Test of Moderators (coefficient(s) 2,3): F(df1 = 2, df2 = 8) = 0.6503, p-val = 0.5474 Model Results: estimate se tval pval ci.lb ci.ub intrcpt -0.2324 0.5688 -0.4085 0.6936 -1.5441 1.0794 factor(alloc)random -0.3421 0.4284 -0.7984 0.4477 -1.3300 0.6459 factor(alloc)systematic 0.0101 0.4579 0.0221 0.9829 -1.0457 1.0659 year 0.0075 0.0199 0.3755 0.7170 -0.0385 0.0534 ablat -0.0236 0.0136 -1.7382 0.1204 -0.0548 0.0077 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

We obtain $F(2,8) = .65, p = .55$, which yields the same conclusion as obtained earlier.

Here, the `btt`

argument was used directly to only include the two coefficients corresponding to the allocation factor in the omnibus test (with the results again given under "Test of Moderators"). Equivalently, the same results could have been obtained with (output not shown):

anova(res, btt=2:3)

If we want to use the `linearHypothesis()`

function from the `car`

package, then we must explicitly request the F-test with:

linearHypothesis(res, c("factor(alloc)random = 0", "factor(alloc)systematic = 0"), test="F")

Linear hypothesis test Hypothesis: factor(alloc)random = 0 factor(alloc)systematic = 0 Model 1: restricted model Model 2: res Res.Df Df F Pr(>F) 1 10 2 8 2 0.6503 0.5474

Note that the model must have been fitted with `test="knha"`

for this to yield the correct F-test.

Finally, using the `multcomp`

package:

summary(glht(res, linfct=rbind(b1=c(0,1,0,0,0), b2=c(0,0,1,0,0))), test=Ftest())

General Linear Hypotheses Linear Hypotheses: Estimate b1 == 0 -0.3421 b2 == 0 0.0101 Global Test: F DF1 DF2 Pr(>F) 1 0.6503 2 8 0.5474

Again, it only makes sense to request an F-test here when the original model was fitted with `test="knha"`

.

Yet another possibility is to conduct a permutation test (Higgins & Thompson, 2004), which is implemented in the `permutest()`

function. For this, the rows of the model matrix are permuted, so that any association between the predictors and the observed outcomes is purely due to chance. This process is repeated many times (ideally for all possible unique permutations), yielding the distribution of the test statistic under the null hypothesis. For the omnibus test, the p-value is the proportion of times that the test statistic under the permuted data is as extreme or more extreme than the actually observed one.

We can try to carry out an exact permutation test (for all possible unique permutations) with:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat, btt=2:3) permutest(res, exact=TRUE)

Running 6227020800 iterations for exact permutation test. Error in permutest.rma.uni(res, exact = T) : Number of iterations requested too large.

So, the exact test would require more than $6 \times 10^9$ model fits, which is just not feasible (even on a fast computer, this could easily take years to complete). So, instead, we can approximate the exact permutation-based p-value by going through a smaller number (as specified by the `iter`

argument) of random permutations (the default is 1000). Let's go with 10000 iterations (this may still take a minutes or so to complete):

permutest(res, iter=10000)

Test of Moderators (coefficient(s) 2,3): QM(df = 2) = 1.3663, p-val* = 0.5344 Model Results: estimate se zval pval* ci.lb ci.ub intrcpt -0.2324 0.5550 -0.4187 1.0000 -1.3201 0.8554 factor(alloc)random -0.3421 0.4180 -0.8183 0.4540 -1.1613 0.4772 factor(alloc)systematic 0.0101 0.4467 0.0226 0.9740 -0.8654 0.8856 year 0.0075 0.0194 0.3849 0.7486 -0.0306 0.0456 ablat -0.0236 0.0132 -1.7816 0.1062 -0.0495 0.0024 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

The p-value for the omnibus test is equal to $.53$, which is very close to the one obtained with the Knapp and Hartung adjustment.^{3)}

In a similar way, one can also test linear combinations of model coefficients. For example, one could test whether random and systematic allocation lead to significantly different effects with:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat) anova(res, L=c(0,1,-1,0,0))

Hypothesis: 1: factor(alloc)random - factor(alloc)systematic = 0 Results: estimate se zval pval 1: -0.3522 0.3357 -1.0489 0.2942 Test of Hypothesis: QM(df = 1) = 1.1002, p-val = 0.2942

We can also use the `linearHypothesis()`

function from the `car`

package for this.

linearHypothesis(res, c(0,1,-1,0,0))

Linear hypothesis test Hypothesis: factor(alloc)random - factor(alloc)systematic = 0 Model 1: restricted model Model 2: res Res.Df Df Chisq Pr(>Chisq) 1 9 2 8 1 1.1002 0.2942

Apparently there is no significant difference between random and systematic allocation.

If the model was fitted with the Knapp and Hartung adjustment, then we again get an F-test:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat, test="knha") anova(res, L=c(0,1,-1,0,0))

Hypothesis: 1: factor(alloc)random - factor(alloc)systematic = 0 Results: estimate se tval pval 1: -0.3522 0.3441 -1.0234 0.3361 Test of Hypothesis: F(df1 = 1, df2 = 8) = 1.0473, p-val = 0.3361

linearHypothesis(res, c(0,1,-1,0,0), test="F")

Linear hypothesis test Hypothesis: factor(alloc)random - factor(alloc)systematic = 0 Model 1: restricted model Model 2: res Res.Df Df F Pr(>F) 1 9 2 8 1 1.0473 0.3361

As another example, we could test whether the estimated average log risk ratio based on random allocation in 1970 at an absolute latitude of 30 degrees is significantly different from zero. Recall that these two moderators were "centered" at 1948 and 13, respectively. So, this linear hypothesis can be tested with:

anova(res, L=c(1,1,0,1970-1948,30-13))

Hypothesis: 1: intrcpt + factor(alloc)random + 22*year + 17*ablat = 0 Results: estimate se tval pval 1: -0.8103 0.2155 -3.7607 0.0055 Test of Hypothesis: F(df1 = 1, df2 = 8) = 14.1432, p-val = 0.0055

Or analogously with the `linearHypothesis()`

function:

linearHypothesis(res, c(1,1,0,1970-1948,30-13), test="F")

Linear hypothesis test Hypothesis: intrcpt + factor(alloc)random + 22 year + 17 ablat = 0 Model 1: restricted model Model 2: res Res.Df Df F Pr(>F) 1 9 2 8 1 14.143 0.005538 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

So, the estimated average log risk ratio for this combination of moderator values is significantly different from zero.

We can obtain the corresponding estimated average log risk ratio with:

tmp <- predict(res, newmods=c(1,0,1970-1948,30-13)) tmp

pred se ci.lb ci.ub cr.lb cr.ub -0.8103 0.2155 -1.3072 -0.3135 -1.9067 0.2860

In addition, the corresponding standard error, confidence interval, and credibility/prediction interval are given. Note that the intercept is automatically added (by default) and hence should not be included in the vector specified for the `newmods`

argument for the `predict()`

function.

Since the F-value given for the test above is based on one degree of freedom in the numerator, the value must in fact be identical with the squared ratio of the predicted value to its standard error:

(tmp$pred/tmp$se)^2

[1] 14.14316

As we see, that works out as expected.

Finally, when including a factor in a model, one may be interested in obtaining all pairwise comparisons (contrasts) between the factor levels. Let's start with the model without the Knapp and Hartung adjustment.

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat, data=dat) res

The table of the model coefficients then look as follows:

estimate se zval pval ci.lb ci.ub intrcpt -0.2324 0.5550 -0.4187 0.6754 -1.3201 0.8554 factor(alloc)random -0.3421 0.4180 -0.8183 0.4132 -1.1613 0.4772 factor(alloc)systematic 0.0101 0.4467 0.0226 0.9820 -0.8654 0.8856 year 0.0075 0.0194 0.3849 0.7003 -0.0306 0.0456 ablat -0.0236 0.0132 -1.7816 0.0748 -0.0495 0.0024

Note that coefficients $b_1$ and $b_2$ are already contrasts (between random and alternate allocation and between systematic and alternate allocation). The contrast between systematic and random allocation is just $b_2-b_1$ (the direction here is arbitrary, but we will use this to make things match up further below).

All these comparisons can be obtained in a single table with:

anova(res, L=rbind(c(0,1,0,0,0), c(0,0,1,0,0), c(0,-1,1,0,0)))

Hypotheses: 1: factor(alloc)random = 0 2: factor(alloc)systematic = 0 3: -factor(alloc)random + factor(alloc)systematic = 0 Results: estimate se zval pval 1: -0.3421 0.4180 -0.8183 0.4132 2: 0.0101 0.4467 0.0226 0.9820 3: 0.3522 0.3357 1.0489 0.2942

The `multcomp`

package can also be used for this:

summary(glht(res, linfct=rbind(c(0,1,0,0,0), c(0,0,1,0,0), c(0,-1,1,0,0))), test=adjusted("none"))

Simultaneous Tests for General Linear Hypotheses Fit: rma(yi = yi, vi = vi, mods = ~factor(alloc) + year + ablat, data = dat) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) 1 == 0 -0.3421 0.4180 -0.818 0.413 2 == 0 0.0101 0.4467 0.023 0.982 3 == 0 0.3522 0.3357 1.049 0.294 (Adjusted p values reported -- none method)

The p-values are unadjusted, so the chances of making at least one Type I error can accumulate quickly when there are many factor levels.

Corresponding confidence intervals (CIs) can be obtained with:

confint(glht(res, linfct=rbind(c(0,1,0,0,0), c(0,0,1,0,0), c(0,-1,1,0,0))), calpha=univariate_calpha())

Simultaneous Confidence Intervals Fit: rma(yi = yi, vi = vi, mods = ~factor(alloc) + year + ablat, data = dat) Quantile = 1.96 95% confidence level Linear Hypotheses: Estimate lwr upr 1 == 0 -0.3421 -1.1613 0.4772 2 == 0 0.0101 -0.8654 0.8856 3 == 0 0.3522 -0.3059 1.0102

These are univariate confidence intervals (that have 95% coverage probability for each contrast individually). Simultaneous (family-wise) confidence intervals can also be obtained by leaving out the `calpha=univariate_calpha()`

argument (these will have 95% coverage probability for all three contrasts simultaneously).

We can also use the `predict()`

function to obtain the CIs (and also the credibility/prediction intervals). Recall that, by default, the intercept is automatically added to the vector(s) specified via the `newmods`

argument (when the model actually includes an intercept), which we do not want to happen here. So, we use:

predict(res, newmods=rbind(c(1,0,0,0), c(0,1,0,0), c(-1,1,0,0)), intercept=FALSE)

pred se ci.lb ci.ub cr.lb cr.ub 1 -0.3421 0.4180 -1.1613 0.4772 -1.5087 0.8246 2 0.0101 0.4467 -0.8654 0.8856 -1.1967 1.2169 3 0.3522 0.3357 -0.3059 1.0102 -0.7075 1.4118

We could also use an alternative parameterization of the same model, leaving out the intercept altogether, and including all three dummy variables:

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat - 1, data=dat) res

The table of coefficients now looks like this:

estimate se zval pval ci.lb ci.ub factor(alloc)alternate -0.2324 0.5550 -0.4187 0.6754 -1.3201 0.8554 factor(alloc)random -0.5744 0.6547 -0.8774 0.3803 -1.8576 0.7087 factor(alloc)systematic -0.2223 0.6636 -0.3349 0.7377 -1.5230 1.0784 year 0.0075 0.0194 0.3849 0.7003 -0.0306 0.0456 ablat -0.0236 0.0132 -1.7816 0.0748 -0.0495 0.0024

So, $b_1$, $b_2$, and $b_3$ are now the estimated (average) log risk ratios for alternate, random, and systematic allocation in 1948 at 13 degrees absolute latitude.

To obtain all pairwise comparisons and interval bounds, we would now use:

anova(res, L=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0)))

Hypotheses: 1: -factor(alloc)alternate + factor(alloc)random = 0 2: -factor(alloc)alternate + factor(alloc)systematic = 0 3: -factor(alloc)random + factor(alloc)systematic = 0 Results: estimate se zval pval 1: -0.3421 0.4180 -0.8183 0.4132 2: 0.0101 0.4467 0.0226 0.9820 3: 0.3522 0.3357 1.0489 0.2942

Or with:

summary(glht(res, linfct=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0))), test=adjusted("none"))

Confidence intervals for the contrasts can be obtained with:

confint(glht(res, linfct=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0))), calpha=univariate_calpha()) predict(res, newmods=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0)))

The results are exactly the same as above.

The `contrMat()`

function from the `multcomp`

package can also be used to automatically set up the contrast matrix with:

summary(glht(res, linfct=cbind(contrMat(c("alternate"=1,"random"=1,"systematic"=1), type="Tukey"), 0, 0)), test=adjusted("none"))

This will again yield the same results as above. An advantage is that the contrasts are labeled more informatively in the output.

The p-values for the contrasts can be adjusted using various methods (see `help(summary.glht)`

and `help(p.adjust)`

for possible options). For example, Holm's method is often a good choice, as it provides strong control of the family-wise error rate, but is not as overly conservative as the Bonferroni correction. We can obtain the adjusted p-values with:

summary(glht(res, linfct=cbind(contrMat(c("alternate"=1,"random"=1,"systematic"=1), type="Tukey"), 0, 0)), test=adjusted("holm"))

Simultaneous Tests for General Linear Hypotheses Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) random - alternate == 0 -0.3421 0.4180 -0.818 0.883 systematic - alternate == 0 0.0101 0.4467 0.023 0.982 systematic - random == 0 0.3522 0.3357 1.049 0.883 (Adjusted p values reported -- holm method)

The Knapp and Hartung adjustment can be used in this context as well.

res <- rma(yi, vi, mods = ~ factor(alloc) + year + ablat - 1, data=dat, test="knha")

Then all of the following commands yield the same results:

anova(res, L=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0))) summary(glht(res, linfct=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0)), df=df.residual(res)), test=adjusted("none")) summary(glht(res, linfct=cbind(contrMat(c("alternate"=1,"random"=1,"systematic"=1), type="Tukey"), 0, 0), df=df.residual(res)), test=adjusted("none"))

The output based on the `anova()`

function looks like this:

Hypotheses: 1: -factor(alloc)alternate + factor(alloc)random = 0 2: -factor(alloc)alternate + factor(alloc)systematic = 0 3: -factor(alloc)random + factor(alloc)systematic = 0 Results: estimate se tval pval 1: -0.3421 0.4284 -0.7984 0.4477 2: 0.0101 0.4579 0.0221 0.9829 3: 0.3522 0.3441 1.0234 0.3361

Note that for the `glht()`

function, we must use the `df`

argument to specify the degrees of freedom for the t-tests. This value can be obtained via `df.residual(res)`

.

Similarly, we can obtain confidence interval bounds for the contrasts with:

confint(glht(res, linfct=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0)), df=df.residual(res)), calpha=univariate_calpha())

Simultaneous Confidence Intervals Fit: rma(yi = yi, vi = vi, mods = ~factor(alloc) + year + ablat - 1, data = dat, test="knha") Quantile = 2.306 95% confidence level Linear Hypotheses: Estimate lwr upr 1 == 0 -0.3421 -1.3300 0.6459 2 == 0 0.0101 -1.0457 1.0659 3 == 0 0.3522 -0.4414 1.1457

These intervals are now based on t-distributions. Simultaneous (family-wise) confidence interval bounds can again be obtained by leaving out the `calpha=univariate_calpha()`

argument.

If we also want the credibility/prediction intervals, we could use the `predict()`

command:

predict(res, newmods=rbind(c(-1,1,0,0,0), c(-1,0,1,0,0), c(0,-1,1,0,0)))

pred se ci.lb ci.ub cr.lb cr.ub 1 -0.3421 0.4284 -1.3300 0.6459 -1.7317 1.0476 2 0.0101 0.4579 -1.0457 1.0659 -1.4286 1.4488 3 0.3522 0.3441 -0.4414 1.1457 -0.9067 1.6110

Colditz, G. A., Brewer, T. F., Berkey, C. S., Wilson, M. E., Burdick, E., Fineberg, H. V., et al. (1994). Efficacy of BCG vaccine in the prevention of tuberculosis: Meta-analysis of the published literature. *Journal of the American Medical Association, 271*(9), 698–702.

Higgins, J. P. T., & Thompson, S. G. (2004). Controlling the risk of spurious findings from meta-regression. *Statistics in Medicine, 23*(11), 1663–1682.

Knapp, G., & Hartung, J. (2003). Improved tests for a random effects meta-regression with a single covariate. *Statistics in Medicine, 22*(17), 2693–2710.

With only $k=13$ studies, results from models including multiple moderators should be interpreted cautiously. The example is used here primarily for illustration purposes.

Since

`alloc`

is a character variable, the coercion to a factor via the `factor()`

function isn't strictly necessary (since this would have happened automatically anyway), but it's better to be explicit. In some cases, a numeric variable may in fact be used to represent levels of a categorical variable, in which case use of the `factor()`

function is crucial.When running the permutation test with REML estimation (or some other iterative estimation method, such as ML or EB), there may be the occasional warning that the "Fisher scoring algorithm did not converge". Unless this happens a lot, this can be safely ignored. Alternatively, one could also fit the model with one of the non-iterative methods (such as DL), which will avoid non-convergence issues altogether.

tips/testing_factors_lincoms.1495101888.txt.gz · Last modified: 2017/05/18 10:04 by 127.0.0.1

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