# Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach
## 3 Results
### Statistical Analyses
#### 3.5.3. Binary Models: Assignment to a Clinical Pathway--Not-EMA Sample  
In the present section and the next, we restrict the sample to not-EMA. The reason for this choice is extremely simple; given the large number of mothers tested by maternal age, the estimates are biased. The analyses proposed are the same as in the previous subsection. The estimates now contain the variable age, which is no longer, logically, endogenous. Table 19 shows that, as for the general sample, paths B1, B5, and B6 are the most interesting ones. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **OLS** \\  & **PPML** & **PPML** & **PPML** & **OLS** & **OLS Reset** & **Bootstrap** \\ \hline \multirow{3}{*}{Weeks of pregnancy} & 0.0286 ** & 0.0125 & 0.0117 & 0.219 * & 0.0429 & 0.219 * \\  & (2.118) & (0.998) & (0.955) & (1.716) & (0.337) & (1.753) \\ \multirow{3}{*}{Foreign} & 0.782 *** & 0.446 ** & 0.433 ** & 6.529 *** & 0.853 & 6.529 *** \\  & (6.373) & (3.540) & (3.469) & (5.145) & (0.278) & (5.686) \\ \multirow{3}{*}{Live births} & - & \(-0.147\) ** & \(-0.139\) ** & \(-1.663\) ** & \(-0.739\) & \(-1.663\) *** \\  & & (\(-2.136\)) & (\(-1.992\)) & (\(-3.957\)) & (\(-1.254\)) & (\(-3.916\)) \\ \multirow{3}{*}{Miscarriages} & - & \(-0.0328\) & \(-0.0136\) & \(-0.668\) & \(-0.288\) & \(-0.668\) \\  & & (\(-0.419\)) & (\(-0.175\)) & (\(-1.482\)) & (\(-0.586\)) & (\(-1.492\)) \\ \multirow{3}{*}{VPI} & - & 0.440 ** & 0.418 ** & 4.647 * & 0.0417 & 4.647 * \\  & & (2.330) & (2.219) & (1.855) & (0.0118) & (1.870) \\ \multirow{3}{*}{General practitioner} & - & -0.465 ** & \(-2.416\) ** & \(-1.769\) & \(-2.416\) ** \\  & & & (\(-2.021\)) & (\(-2.895\)) & (\(-1.633\)) & (\(-2.752\)) \\ \multirow{3}{*}{Hospital specialist} & - & - & 0.381 *** & 3.082 ** & 1.187 & 3.082 ** \\  & & - & (2.664) & (3.062) & (0.736) & (2.937) \\ \multirow{3}{*}{Private specialist} & - & - & 0.615 *** & 6.190 *** & 1.532 & 6.190 *** \\  & & & (3.418) & (3.532) & (0.447) & (3.396) \\ \multirow{3}{*}{Consultant specialist} & - & - & 0.244 & 2.958 ** & 0.930 & 2.958 ** \\  & & & (1.550) & (2.473) & (0.575) & (2.428) \\ \multirow{3}{*}{NHS specialist} & - & - & 0.530 ** & 5.117 *** & 1.515 & 5.117 *** \\  & & - & (2.960) & (3.124) & (0.547) & (3.069) \\ \multirow{3}{*}{Squares of estimated values} & - & - & - & - & (0.673) & - \\  & & & & & 0.000438 & \\ \multirow{3}{*}{Cubes of estimated values} & - & - & - & - & (0.190) & - \\  & & & & & & \\ \multirow{3}{*}{Constant} & 1.389 *** & 2.411 *** & 2.181 ** & 2.958 * & 3.787 ** & 2.958 * \\  & (8.265) & (15.85) & (11.73) & (1.838) & (2.123) & (1.819) \\ \multirow{3}{*}{No. obs.} & 2119 & 2119 & 2119 & 2119 & 2119 & 2119 \\ \multirow{3}{*}{R squared} & 0.037 & 0.114 & 0.126 & 0.073 & 0.077 & 0.073 \\ \hline \multirow{3}{*}{Note: *** \(p<0.001\); ** \(p<0.01\); * \(p<0.05\).} & & & & \\ \cline{1-1}  & & & & & & \\ \end{tabular}
\end{table} 
Table 19. Probability of being assigned to a clinical pathway—LOGIT estimates—not-EMA sample.