Maybe something like this too?
xi: poisson cases i.year, exposure(population)
predict logn, xb
predict se, stdp
gen ratehat=exp(logn-log(population))*100000
gen rateup=exp(logn+(1.96*se)-log(population))*100000
gen ratelo=exp(logn-(1.96*se)-log(population))*100000
twoway line ratehat rateup ratelo year, sort
slightly different CIs than those obtained from ci, maybe because ci
produces exact intervals.
I think you can then test for differences in years of interest using
-test-, e.g.
test _Iyear_1955== _Iyear_1956
overlapping confidence intervals do not always indicate no difference
at p=0.05, see:
http://www.stata.com/statalist/archive/2008-01/msg00900.html , and
other discussions.
you can also compare to a linear trend using model without year as a
categorical variable, e.g.
xi: poisson cases year, exposure(population)
Hope this helps, Tim
On Sun, Jan 17, 2010 at 2:11 PM, moleps <[email protected]> wrote:
> Dear listers,
> I扉e got the incidence of a disease in a population in the following data format -year,population,cases-
> I扉e created the incidence rate by dividing cases by population and multiplying by 100000.
>
> gen ins=(cases/population)*100000
> poisson cases year population
>
> **no tendency for increasing incidense after correcting for population.
> **however
>
> line ins ye
>
> **shows an increasing trend. But I悲 like to add 95% CI lines to corroborate this. Is this at all possible given my dataset??
>
> Regards,
> //M
>
>
>
>
>
>
> | year population cases|
> |----------------------------|
> 1. | 1953 2285542 49 |
> 2. | 1954 3075055 44 |
> 3. | 1955 3015476 50 |
> 4. | 1956 3073404 52 |
> 5. | 1957 3407827 94 |
> |----------------------------|
> 6. | 1958 3404373 78 |
> 7. | 1959 3343568 79 |
> 8. | 1960 3196884 59 |
> 9. | 1961 3372724 80 |
> 10. | 1962 3508295 67 |
> |----------------------------|
> 11. | 1963 3348748 84 |
> 12. | 1964 3680068 72 |
> 13. | 1965 3594444 85 |
> 14. | 1966 3270933 67 |
> 15. | 1967 3668785 66 |
> |----------------------------|
> 16. | 1968 3802479 140 |
> 17. | 1969 3758948 115 |
> 18. | 1970 3765404 75 |
> 19. | 1971 3811994 103 |
> 20. | 1972 3595579 86 |
> |----------------------------|
> 21. | 1973 3846154 98 |
> 22. | 1974 3972990 122 |
> 23. | 1975 3736829 111 |
> 24. | 1976 4017101 109 |
> 25. | 1977 4035202 96 |
> |----------------------------|
> 26. | 1978 3972186 100 |
> 27. | 1979 4066134 110 |
> 28. | 1980 3813510 111 |
> 29. | 1981 3908085 121 |
> 30. | 1982 4029101 126 |
> |----------------------------|
> 31. | 1983 4045128 112 |
> 32. | 1984 4134353 126 |
> 33. | 1985 3998700 136 |
> 34. | 1986 3936817 99 |
> 35. | 1987 4100854 126 |
> |----------------------------|
> 36. | 1988 4198289 111 |
> 37. | 1989 4124119 114 |
> 38. | 1990 4233116 114 |
> 39. | 1991 4175240 97 |
> 40. | 1992 4198459 112 |
> |----------------------------|
> 41. | 1993 4124798 113 |
> 42. | 1994 3986070 95 |
> 43. | 1995 4348410 109 |
> 44. | 1996 4369957 135 |
> 45. | 1997 4392714 135 |
> |----------------------------|
> 46. | 1998 4342720 142 |
> 47. | 1999 4445329 157 |
> 48. | 2000 4478497 135 |
> 49. | 2001 4503436 128 |
> 50. | 2002 4450334 144 |
> |----------------------------|
> 51. | 2003 4552252 144 |
> 52. | 2004 4577457 150 |
> 53. | 2005 4606363 164 |
> 54. | 2006 4567282 152 |
> 55. | 2007 4681134 153 |
> |----------------------------|
> 56. | 2008 4571227 150 |
> +----------------------------+
>
>
>
> *
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>
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