Dynamic regression model State-dependent regression parameters State-dependent variance parameters Tables of Transition probabilities Expected state durations Predictions Expected values of dependent variable P
Dynamic regression model State-dependent regression parameters State-dependent variance parameters Tables of Transition probabilities Expected state durations Predictions Expected values of dependent variable Probabilities of being in a state Static (one-step) ...
Table 6. Mobile telephony switching intentions models; ordered logit regression; dependent variable is “How likely are you to consider switching your service provider within the next 12 months?” From 1: Not at all likely to 5: Very likely. VariablesFull modelParsimonious modela ORSEORSE Stand...
model fit or parsimony is not a concern when estimating a propensity score model as the goal is to find a model that results in the best covariate balance. Second, logit regression was used to estimate the propensity score, with the PSM being implemented using four matching algorithms, ...
Implemented to evaluate the cross-sectional independence of the residuals in a fixed-effects regression model, the Breusch and Pagan (1980) test, thereby providing additional support for the aforementioned findings. The Wald statistic was used to evaluate groupwise heteroscedasticity in the residues of ...
Alluvial plots were generated using the ‘ggalluvial’ package in R [16], Kaplan-Meier and cox regression analyses were performed with the ‘sts graph’ and ‘stcox’ commands in Stata, respectively [17]. Results To answer research question 1, switches in vaccination preference were visualized ...
In our case, the underlying regression estimates suggest that the averages are mainly composed of many small effects in the same direction, rather than a mix of large and opposing effects (see Online Appendix L).32 5.3. Health care and drug consumption We next examine whether the interventions...
Change in eGFR from baseline was assessed by random-effect linear regression model. A p-value < 0.05 was deemed significant for all calculations. Analyses were performed using StataCorp 2019. Stata Statistical Software: Release 16, StataCorp LLC, College Station, Texas, USA. Figures were ...
Factors with p-value ≤ 0.10 in the univari- able models and ≤ 5% missing values were included in a multivariable modified Poisson regression model for each outcome and removed individually via backward selection until all remaining variables were significant (p < 0.05). Stata version ...
To examine plan choice in 2015, we used a multinomial logit regression that allowed us to analyze our categorical outcome variable without natural ordering. We stratified by dual eligibility and high-need status to see how patterns differ between groups. We excluded enrollees who (1) moved between...