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More complex event history analysis

More complex event history analysis. UNEMPLOYMENT AND RETURNING TO WORK STUDY. 0 = Unemployed; 1 = Working. Spell or Episode. 0. t1. End of Study. Start of Study. UNEMPLOYMENT AND RETURNING TO WORK STUDY. 0 = Unemployed; 1 = Working. 1. 0. 1. 0. t1 t2 t3.

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More complex event history analysis

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  1. More complex event history analysis

  2. UNEMPLOYMENT AND RETURNING TO WORK STUDY 0 = Unemployed; 1 = Working Spell or Episode 0 t1 End of Study Start of Study

  3. UNEMPLOYMENT AND RETURNING TO WORK STUDY 0 = Unemployed; 1 = Working 1 0 1 0 t1 t2 t3 End of Study Start of Study

  4. UNEMPLOYMENT AND RETURNING TO WORK STUDY 0 = Unemployed; 1 = Working Transition = movement from one state to another 1 0 t1 End of Study Start of Study

  5. Recurrent events are merely outcomes that can take place on a number of occasions. A simple example is unemployment measured month by month. In any given month an individual can either be employed or unemployed. If we had data for a calendar year we would have twelve discrete outcome measures (i.e. one for each month).

  6. Social scientists now routinely employ statistical models for the analysis of discrete data, most notably logistic and log-linear models, in a wide variety of substantive areas. I believe that the adoption of a recurrent events approach is appealing because it is a logical extension of these models.

  7. Consider a binary outcome or two-state event 0 = Event has not occurred 1 = Event has occurred In the cross-sectional situation we are used to modelling this with logistic regression.

  8. UNEMPLOYMENT AND RETURNING TO WORK STUDY – A study for six months 0 = Unemployed; 1 = Working

  9. Months1 2 3 4 5 6obs0 0 0 0 0 0Constantly unemployed

  10. Months1 2 3 4 5 6obs1 1 1 1 1 1Constantly employed

  11. Months1 2 3 4 5 6obs1 0 0 0 0 0Employed in month 1 then unemployed

  12. Months1 2 3 4 5 6obs0 0 0 0 0 1Unemployed but gets a job in month six

  13. Here we have a binary outcome – so could we simply use logistic regression to model it?Yes and No – We need to think about this issue.

  14. Appropriate SoftwareSTATISTICAL ANALYSIS FOR BINARY RECURRENT EVENTS (SABRE) • Fits appropriate models for recurrent events. • It is like GLIM. • It can be downloaded free.

  15. www.cas.lancs.ac.uk/software

  16. SABRE fits two models that are appropriate to this analysis. Model 1 = Pooled Cross-Sectional Logit Model Think of this as being the same as a logistic regression in any software package.

  17. POOLED CROSS-SECTIONAL LOGIT MODEL x it is a vector of explanatory variables and b is a vector of parameter estimates .

  18. We could fit a pooled cross-sectional model to our recurrent events data.This approach can be regarded as a naïve solution to our data analysis problem.

  19. We need to consider a number of issues….

  20. MonthsY1 Y2obs0 0 Pickle’s tip - In repeated measured analysis we would require something like a ‘paired’ t test rather than an ‘independent’ t test because we can assume that Y1 and Y2 are related.

  21. SABRE fits two models that are appropriate to this analysis. Model 2 = Random Effects Model (or logistic mixture model)

  22. Repeated measures data violate an important assumption of conventional regression models. The responses of an individual at different points in time will not be independent of each other. This problem has been overcome by the inclusion of an additional, individual-specific error term.

  23. The random effects model extends the pooled cross-sectional model to include a case-specific random error term to account for residual heterogeneity. For a sequence of outcomes for the ith case, the basic random effects model has the integrated (or marginal likelihood) given by the equation.

  24. Davies and Pickles (1985) have demonstrated that the failure to explicitly model the effects of residual heterogeneity may cause severe bias in parameter estimates. Using longitudinal data the effects of omitted explanatory variables can be overtly accounted for within the statistical model. This greatly improves the accuracy of the estimated effects of the explanatory variables

  25. An example – see Davies, Elias & Penn (1992). A study of wive’s employment status. Y (femp) 0 = wife unemployed 1 = wife employed X1 (fmune) 0 = husband employed 1 = husband unemployed X2 (fund1) 0 = no child under 1 year 1 = child under 1 year

  26. Results of various models

  27. Deviance = 1344.2363 on 1576 residual degrees of freedom dis e Parameter Estimate S. Error ___________________________________________________ int 1.5054 0.23772 fmune ( 1) 0.00000E+00 ALIASED [I] fmune ( 2) -2.2871 0.38153 fund1 ( 1) 0.00000E+00 ALIASED [I] fund1 ( 2) -2.5752 0.34447 scale 2.2524 0.16565 Random effect

  28. STATE DEPENDENCE Past Behaviour Current Behaviour

  29. STATE DEPENDENCE APRIL MAY Employed Unemployed Employed

  30. Lag Model MonthsY1 Y2obs0 0

  31. ACCOUNTS FOR PREVIOUS OUTCOME (yt-1)

  32. This is called a Lagged model A Lagged model helps to control for a previous outcome (or behaviour).

  33. Results of models – with state dependence

  34. Deviance = 823.21859 on 1420 residual degrees of freedom Deviance decrease = 336.96811 on 1 residual degree of freedom dis e Parameter Estimate S. Error ___________________________________________________ int -1.3695 0.17259 fmune ( 1) 0.00000E+00 ALIASED [I] fmune ( 2) -1.5287 0.39847 fund1 ( 1) 0.00000E+00 ALIASED [I] fund1 ( 2) -3.1227 0.35764 lag 4.3046 0.22885 scale 0.50379 0.28180

  35. State dependence can be explored further by the estimation of aa ‘two-state’ MARKOV model.

  36. The Model Provides TWO sets of estimates APRIL MAY Unemployed Explanatory Variables Employed Employed Explanatory Variables

  37. Results of models – with state dependence

  38. Parameter Estimate S. Error ___________________________________________________ Unemployed Women at t-1 _______ int -1.5549 0.23159 fmune ( 1) 0.00000E+00 ALIASED [I] fmune ( 2) -1.9071 0.74901 fund1 ( 1) 0.00000E+00 ALIASED [I] fund1 ( 2) -1.4606 0.71256 scale 1.2392 0.29000 Employed Women at t-1 _______ int 3.0647 0.17575 fmune ( 1) 0.00000E+00 ALIASED [I] fmune ( 2) -1.3717 0.50228 fund1 ( 1) 0.00000E+00 ALIASED [I] fund1 ( 2) -3.4226 0.35791 scale 0.10000E-02 0.28111

  39. SABRE – Good Points • Fits appropriate models for recurrent events. • It is like GLIM. • It can be downloaded free. • There is a users list. • Uses the deviance to compare models (correct likelihood). • Fits the Markov model. • Fits a range of other models (e.g. loglinear + ordinal). • Can do more advance analysis (e.g. Mover/Stayers).

  40. SABRE – Bad Points • It is like GLIM – you need to understand a prog. Syntax. • Data management and handling are poor. • There are few users.

  41. Alternatives to SABRE • STATA – Does not fit the full range of models. • Multilevel model software – Okay up to a point but check that the likelihood is correct (complicated). • No software other than SABRE fits a continuation ratio model (ordinal), Markov model or the mover/stayer.

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