Once the shovel hits the ground : Evaluating the management of complex implementation processes of public-private partnership infrastructure projects with qualitative comparative analysis

Much attention is being paid to the planning of public-private partnership (PPP) infrastructure projects. The subsequent implementation phase – when the contract has been signed and the project ‘starts rolling’ – has received less attention. However, sound agreements and good intentions in project planning can easily fail in project implementation. Implementing PPP infrastructure projects is complex, but what does this complexity entail? How are projects managed, and how do public and private partners cooperate in implementation? What are effective management strategies to achieve satisfactory outcomes? This is the fi rst set of questions addressed in this thesis. Importantly, the complexity of PPP infrastructure development imposes requirements on the evaluation methods that can be applied for studying these questions. Evaluation methods that ignore complexity do not create a realistic understanding of PPP implementation processes, with the consequence that evaluations tell us little about what works and what does not, in which contexts, and why. This hampers learning from evaluations. What are the requirements for a complexity-informed evaluation method? And how does qualitative comparative analysis (QCA) meet these requirements? This is the second set of questions addressed in this thesis

De bescherming van den hypotheekhouder tegen beschadiging van den verbonden opstal door middel van verzekering /

Proefschrift Universiteit van Amsterdam.Met lit.opg

Measuring balance and model selection in propensity score methods

Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To describe and evaluate measures for balance in PS methods: the overlapping coefficient, the Kolmogorov- Smirnov distance, and the Lévy distance, and mean based measures for balance. Methods: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect). Results: For large sample sizes (n=2000) the average Pearson's correlation coefficients between bias and Kolmogorov- Smirnov distance (r=0.89), the Lévy distance (r=0.89) and the absolute standardized mean difference (r=0.90) were similar, whereas this was lower for the overlapping coefficient (r= -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables. Conclusions: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model

Measuring balance and model selection in propensity score methods

Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To describe and evaluate measures for balance in PS methods: the overlapping coefficient, the Kolmogorov- Smirnov distance, and the Lévy distance, and mean based measures for balance. Methods: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect). Results: For large sample sizes (n=2000) the average Pearson's correlation coefficients between bias and Kolmogorov- Smirnov distance (r=0.89), the Lévy distance (r=0.89) and the absolute standardized mean difference (r=0.90) were similar, whereas this was lower for the overlapping coefficient (r= -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables. Conclusions: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model

Comparing treatment effects after adjustment with multivariable Cox proportional hazards regression and propensity score methods

PURPOSE: To compare adjusted effects of drug treatment for hypertension on the risk of stroke from propensity score (PS) methods with a multivariable Cox proportional hazards (Cox PH) regression in an observational study with censored data. METHODS: From two prospective population-based cohort studies in The Netherlands a selection of subjects was used who either received drug treatment for hypertension (n = 1293) or were untreated 'candidates' for treatment (n = 954). A multivariable Cox PH was performed on the risk of stroke using eight covariates along with three PS methods. RESULTS: In multivariable Cox PH regression the adjusted hazard ratio (HR) for treatment was 0.64 (CI(95%): 0.42, 0.98). After stratification on the PS the HR was 0.58 (CI(95%): 0.38, 0.89). Matching on the PS yielded a HR of 0.49 (CI(95%): 0.27, 0.88), whereas adjustment with a continuous PS gave similar results as Cox regression. When more covariates were added (not possible in multivariable Cox model) a similar reduction in HR was reached by all PS methods. The inclusion of a simulated balanced covariate gave largest changes in HR using the multivariable Cox model and matching on the PS. CONCLUSIONS: In PS methods in general a larger number of confounders can be used. In this data set matching on the PS is sensitive to small changes in the model, probably because of the small number of events. Stratification, and covariate adjustment, were less sensitive to the inclusion of a non-confounder than multivariable Cox PH regression. Attention should be paid to PS model building and balance checking