Model-based meta-analysis allows integration of aggregated-level data (AD) from different clinical trials in one model to assess population efficacy/safety. However, AD is limited in individual-level information, while individual-patient-level data (IPD) are hard to obtain. Combined modeling may take advantage of both sources. Chronic obstructive pulmonary disease (COPD) is a leading cause of poor health and death. This study established a combined ADIPD model of COPD clinical trials with forced expiratory volume in 1 s (FEV1) as an endpoint and explored methods for estimating interstudy variability (ISV), interindividual variability (IIV), and aggregation bias. Stochastic simulation and estimations (SSE) showed the best method in NONMEM to estimate ISV/IIV: using $LEVEL with equal weight of studies; for the AD part, ISVs from the AD model were fixed, estimating IIV with separate ETAs for each arm; the IPD part shared the fixed ISV and estimated IIV. An approximated normal distribution was derived for lognormal IIV to avoid aggregation bias. Covariate correlations were different at aggregated and individual levels, but did not introduce aggregation bias according to SSE. A separate AD model (published) and IPD model were built, then combined to form the ADIPD model. The ADIPD model included FEV1 baseline, disease progression, placebo effect, and Emax/constant dose-responses for 23 compounds. Identified covariate relationships: higher age, female, higher disease severity, non-current smoker related to lower baseline; higher baseline related to faster disease progression and higher drug effects. Covariate coefficients were estimated more precisely in the ADIPD model than the AD model. ADIPD modeling allows more informed clinical trial simulations for study design.Trial Registration: identifier: NCT01053988 and NCT01054885