By substitution of these parameters in the regression model, we obtain the elementary transition probabilities:
This graph plots the conditional transition probabilities from an initial state (1=healthy in red at the bottom, or 2=disabled in green on the top) at age @@ -791,7 +791,7 @@ href="http://euroreves.ined.fr/imach/doc 81 5.9775 (0.0873) 3.3484 (0.0933) 2.0222 (0.1230) 4.4520 (0.1320)
For example 70 11.0180 (0.1277) 3.1950 (0.3635) 4.6500 (0.0871) 4.4807 (0.2187) means -e11=11.0180 e12=3.1950 e21=4.6500 e22=4.4807
+e11=11.0180 e12=3.1950 e21=4.6500 e22=4.4807
For example, life expectancy of a healthy individual at age 70 is 11.0 in the healthy state and 3.2 in the disability state (total of 14.2 years). If he was disabled at age 70, his life expectancy will be shorter, 4.65 years in the @@ -829,9 +829,9 @@ weighted mean of e11 and e21). e.2=3.30 be spent in the disability state.
This figure represents the health expectancies and the total life expectancy -with a confidence interval (dashed line).
+with a confidence interval (dashed line).
Standard deviations (obtained from the information matrix of the model) of these quantities are very useful. Cross-longitudinal surveys are costly and do not involve huge samples, generally a few thousands; therefore it is very
