t necessarily generalizable to other popular outcome types in the meta‐analytical field, such as binary and time‐to‐event outcomes. Binary outcomes, for example, are more complex to deal with than continuous outcomes, as a logistic mixed effects model is nonlinear, and hence, the corresponding maximum likelihood function has no closed form. Jackson et al[26](#sim7930-bib-0026){ref-type="ref"} recently investigated the use of ML estimation and found that a stratified intercept model leads to substantial downward bias in between‐trial variance estimates and under‐coverage of CIs for the summary result, which increases as the number of trials (and thus parameters) increases. Interestingly, the issue was resolved when using a + 0.5/−0.5 coding for the treatment variable, rather than a 1/0 coding, or when placing random effects on the trial intercept.[26](#sim7930-bib-0026){ref-type="ref"} McNeish[32](#sim7930-bib-0032){ref-type="ref"} investigated logistic mixed models by either retaining the nonlinearity of the model and making an approximation for the likelihood function or linearly approximating the model to give the likelihood function a closed form (pseudo‐likelihood approach). The latter option was shown to be favorable (under the specific conditions of the study), by use of a residual penalized quasi‐likelihood with a KR correction.

While our simulation study did consider an extensive range of scenarios---we varied the number of trials, number of participants per trial, and heterogeneity of parameters---we recognize that our conclusions were based on a final score model that did not adjust for baseline outcome value. Often, the ANCOVA model should be used, as in our applied example, because there will be baseline imbalances in practice. However, as baseline values did not vary across individuals in our simulation study, using a final score analysis model rather than ANCOVA was appropriate. When using one‐stage ANCOVA IPD models, an additional issue is using stratified adjustment terms or p