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help for hshaz                               Stephen P. Jenkins (December 2005)
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Discrete time (grouped data) proportional hazards models

hshaz covariates [weight] [if exp] [in range] [, id(idvar) dead(deadvar) seq(seqvar) nmp(#) m2(#) p2(#) m3(#) p3(#) m4(#) p4(#) m5(#) p5(#) eform nocons nolog nobeta0 level(#) maximize_options]

by ... : may be used with hshaz; see help by.

fweights and iweights are allowed; see help weights.

Description

hshaz estimates, by ML, two discrete time (grouped data) proportional hazards regression models, one of which incorporates a discrete mixture distribution to summarize unobserved individual heterogeneity (or 'frailty'). Covariates may include regressor variables summarizing observed differences between persons (either fixed or time-varying), and variables summarizing the duration dependence of the hazard rate. With suitable definition of covariates, models with a fully non-parametric specification for duration dependence may be estimated; so too may parametric specifications. hshaz thus provides a useful complement to stcox (for continuous survival time data), and related programs. For further discussion of hazard regression models with unobserved heterogeneity, see e.g. http://www.iser.essex.ac.uk/teaching/stephenj/ec968/index.php, espcially `Lesson 7'. The Lesson also shows how to derive predicted hazard and survivor functions from the estimates of the model.

hshaz estimates two models by maximum likelihood: (1) the Prentice-Gloeckler (1978) model; and (2) the Prentice-Gloeckler (1978) model incorporating a discrete mixture distribution to summarize unobserved individual heterogeneity, as proposed by Heckman and Singer (1984). (To estimate a model incorporating Gamma distributed heterogeneity, use pgmhaz8.)

Suppose there are individuals i = 1,...,N, who each enter a state (e.g. unemployment) at time t = 0 and are observed for j time periods, at which point each person either remains in the state (censored duration data) or leaves the state (complete duration data).

In Model 1, the discrete hazard rate in period t is

h_t = 1-exp(-exp(b0 + X_it*b))

where b0 is an intercept and the linear index function, X_it*b, incorporates the impact of covariates X_it. The contribution to the sample likelihood for a subject with a spell length of j periods is

S(j) * (h_j/(1 - h_j))^c

where S(j) is the probability of remaining in the state j periods, i.e. the survivor function, and c is a censoring indicator, equal to one for a completed spell and zero otherwise.

Suppose now that each individual belongs to one of a number of different types, and membership of each class is unobserved. This is parameterized by allowing the intercept term in the hazard function to differ across types. Thus, for a model with types z = 1,...,Z, the hazard function for an individual belonging to type z is:

hz_t = 1-exp(-exp(m_z + b0 + X_it*b))

and the probability of belonging to type z is p_z. The m_z characterize the discrete points of support of a multinomial distribution (`mass points'), with m_1 normalized to equal zero and p_1 = 1 - SUM(z=2 to z=Z)[ p_z ]. The zth mass point equals m_z + b0.

In hshaz, the default number of mass points is 2, but optionally may be set equal to 3, 4, or 5.

The contribution to the sample likelihood of a subject with observed duration j is:

L = SUM(z=1 to z=Z) [ p_z * Sz(j) * (hz_j/(1 - hz_j))^c ]

For suitably re-organised data, the log-likelihood function for Model 1 is the same as the log-likelihood for a generalized linear model of the binomial family with complementary log-log link: see Allison (1982) or Jenkins (1995). Model 1 is estimated using the glm command (cloglog produces the same estimates).

Model 2 is estimated using ml d0. Heckman and Singer (1984) note potential problems with finding the global maxima rather multiple local maxima, and emphasize the benefits of estimating models with alternative sets of starting values to check convergence. Different starting values for the mass points and associated probabilities may be set optionally: see below. See also the discussion of maximize_options below.

Important note about data organization and variables

The data set must be organised beforehand so that there is a data row corresponding to each time period at risk of the event for each subject. (This corresponds to `episode splitting' at each and every period.) expand is useful for putting the data in this form. Also see the `data step' discussion in Jenkins (1995).

Options

The first three options are required:

id(idvar) specifies the variable uniquely identifying each subject, i.

seq(seqvar) is the variable uniquely identifying each time period at risk for each subject. For each i at risk j periods, the variable is the integer sequence 1, 2, ..., j_i.

dead(deadvar) summarizes censoring status during each time period at risk. If c_i = 0, deadvar = 0 for all t = 1, 2, ..., j_i; if c_i = 1, deadvar = 0 for all t = 1,2,...,(j_i)-1, and deadvar = 1 for t = j_i.

nmp(#) specifies the number of discrete points of support Z. The default is two.

m2(#), m3(#), m4(#), m5(#), specify starting values for m_2, m_3, m_4, m_5. The default values are 1, -1, 0.1, -0.1, respectively.

p2(#), p3(#), p4(#), p5(#), specify starting values for the probabilities of belonging to types 2, 3, 4, and 5. The default values are 0.3, 0.3, 0.1, 0.1, respectively.

eform reports the coefficients transformed to hazard ratio format, i.e. exp(b) rather than b. Standard errors and confidence intervals are similarly transformed. eform may be specified at estimation or when redisplaying results.

level(#) specifies the significance level, in percent, for confidence intervals of the parameters; see help level.

nocons specifies no intercept term, i.e. b0 = 0.

nolog suppresses the iteration logs.

nobeta0 suppresses reporting of the estimates from Model 1.

maximize_options control the maximization process. The options available are those available for ml d0 as shown by help maximize, with the exception of from(). For difficult maximization problems, using the difficult or technique options may help convergence. So too might different starting values for the frailty parameters.

Saved results

In addition to the usual results saved after ml, hshaz also saves the following:

e(m2) and e(se_m2), e(m3) and e(se_m3), e(m4) and e(se_m4), e(m5) and e(se_m5), are the estimated m_z and their corresponding standard errors. e(m1) = 0.

e(pr1) and e(se_pr1), e(pr2) and e(se_pr2), e(pr3) and e(se_pr3), e(pr4) and e(se_pr4), e(pr5) and e(se_pr5), are the estimated probabilities of each type and their corresponding standard errors. e(pr1) = 1 - SUM(z=2 to z=Z)[e(prz)].

e(ll_nofr) is the log-likelihood value from Model 1.

e(depvar), e(deadvar), and e(seqvar) contain the names of depvar, deadvar, and seqvar.

e(N_spell) is the number of spells in the data. (Cf. e(N), the total number of `person-period' observations in the estimation data set.)

Examples

. sysuse cancer, clear

. ge id = _n

. recode drug 1=0 2/3=1

. expand studytime

. bysort id: ge t = _n // 'survival time', t, is # periods at risk per subject

. bysort id: ge dead = died & _n==_N // event indicator

. // duration dependence: log(time)

. ge logt = ln(t)

. // two mass points

. hshaz drug age logt, id(id) seq(t) d(dead)

. hshaz, eform

. // equivalent model estimated using -gllamm-

. gllamm dead age drug logt , i(id) ip(fn) nip(2) f(bin) l(cll) allc nocons

. // three mass points

. hshaz drug age logt, id(id) seq(t) d(dead) nmp(3) difficult tech(dfp)

Author

Stephen P. Jenkins <stephenj@essex.ac.uk>, Institute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, U.K.

Acknowledgements

Adrienne tenCate found a bug in the handling of fweighted data, and Jeff Pitblado showed me how to fix it. Arne Uhlendorff and Peter Haan found a bug in the calculation of the SEs for the estimated probabilities of the various Types.

References

Allison, P.D. (1982). Discrete-time methods for the analysis of event histories. In Sociological Methodology 1982, ed. S. Leinhardt, San Francisco: Jossey-Bass Publishers, 61-97.

Heckman, J.J. and Singer, B. (1984). A Method for minimizing the impact of distributional assumptions in econometric models for duration data, Econometrica, 52 (2): 271-320.

Jenkins, S.P. (1995). Easy estimation methods for discrete-time duration models. Oxford Bulletin of Economics and Statistics 57 (1): 129-138.

Prentice, R. and Gloeckler L. (1978). Regression analysis of grouped survival data with application to breast cancer data. Biometrics 34 (1): 57-67.

Also see

Help for stcox, glm, cloglog, xtcloglog, expand, and, if installed, pgmhaz, pgmhaz8.