Process evaluation of the Data-driven Quality Improvement in Primary Care (DQIP) trial: quantitative examination of variation between practices in recruitment, implementation and effectiveness.

This has a number of implications including risk of biased overestimation of explained variation (which we accounted for by reporting an adjusted R 2 value) and risk of overfitting of models (as a result of which we dropped several marginally significant (0.05<P<0.1) variables from final models, and our fitted model has at least 10 practices per variable which is adequate in view of simulation studies showing that a minimum of only two SPV may suffice in linear regression for adequate estimation of regression coefficients). 24 More problematically, there is relatively limited power for identifying practice characteristics significantly associated with trial participation, and for examining variation in intervention implementation and outcomes.

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