ORs and their accompanying RRs for the primary outcome in 16 RCTs (crude estimate) and 12 RCTs (adjusted estimate).

<p>Dotted line represents the points where the OR and RR are the same.</p

Overview of types (aims) of IPD-MAs of prediction modeling studies.

<p>Overview of types (aims) of IPD-MAs of prediction modeling studies.</p

ORs and their accompanying RRs for other outcomes or subgroup analyses in 41 RCTs.

<p>Dotted line represents the points where the OR and RR are the same; other lines represent 20%, 50% and 100% difference between OR and RR.</p

Suppose that 500 patients are treated with drug A and 500 with placebo.

<p>The outcome is survival for 30 days. In the treatment arm 85% of the patients survive 30 days, and in the placebo arm 70% of the patients survive 30 days. The two-by-two table looks as above.</p><p>The odds ratio is calculated as the ratio of the odds of treatment in the patients who survived (425/350 = 1.21) and the odds of treatment in the patient who did not survive (75/150 = 0.50), resulting in an odds ratio of 1.21/0.50 = 2.43. One could also calculate the cross-product of the table: (425*150)/(350*75) = 2.43.</p><p>The risk ratio is calculated as the ratio of the risk of survival in the treatment group (425/500 = 0.85) and the risk of survival in the placebo group (350/500 = 0.70), resulting in a risk ratio of 0.85/0.70 = 1.21.</p

Crude effect measures presented in 193 RCTs that had a dichotomous primary outcome, and adjusted effect measures presented in 53 RCTs that had a dichotomous primary outcome and adjusted for baseline variables (OR = odds ratio, RR = risk ratio, HR = hazard ratio, RD = risk difference).

<p>* Other includes presentation of only p-values or only percentages, or no crude measure presented.</p

Estimated unadjusted factor-outcome associations for the DVT case study.

<p>The number between brackets indicates the amount of available studies. Statistical significance (<i>p</i>-value), 95% confidence intervals (95% CI) and 95% prediction intervals (95% PI) are given for the odds ratio (OR). For some one-stage models, estimates could not be obtained because the adaptive Gauss-Hermite approximation did not converge.</p>†<p>Zero-cells occurred in two studies for factor <i>oachst</i>.</p

Estimated factor-outcome associations in the DVT case study for <i>ddimdich</i>, adjusted for <i>malign</i>, <i>surg</i> and <i>calfdif3</i>.

<p>Estimated factor-outcome associations in the DVT case study for <i>ddimdich</i>, adjusted for <i>malign</i>, <i>surg</i> and <i>calfdif3</i>.</p

Scenarios using a unique hospital case-mix distribution.

<p>In scenario 2 and 6 the HSMR is recalculated using the distribution of the case-mix variable under study of a single hospital. In these scenarios for each hospital 60 HSMRs are recalculated. In scenario 4 and 8 the HSMR is recalculated using the distribution of the case-mix variable under study of another year. In these scenarios for each hospital three HSMRs are recalculated. In the second column the numbers of hospitals are shown for which the recalculated HSMR crosses a ‘control limit’. In brackets: the number of hospitals for which the HSMR lies close to a control limit (within 2 HSMR points). Columns 3 to 5 show an overview of the number of hospitals whose case-mix distribution changes the HSMR of a hospital significantly. Columns 6 and 7 show an overview of the years where the differences in case-mix distribution change the HSMR of a hospital significantly.</p

Overview of the DVT data.

<p>Observed factor level counts (for which dvt = 1) for binary risk factors in each study of the DVT case study. Entries are left blank for studies that did not measure the corresponding factor.</p

Numerical Example of direct and indirect standardization.

<p>Although both hospitals have the same observed mortality hospital A performs worse than hospital B when the mortality rate is adjusted via the indirect standardization method.</p