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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
RE: st: incidence rate and confidence intervals |

Date |
Mon, 18 Jan 2010 20:55:42 -0000 |

My comment was to underline a contradiction, not to suggest the right answer. Even if I were an epidemiologist, which many people know I certainly am not, I won't dare to guess on what would be an appropriate analysis just from knowing that you have data on a "disease". For example, is it infectious? Are there pre-existing stochastic models for its dynamics on one or more time scales? Is there literature showing that some off-the-shelf regression-type method works reasonably with data on it? A mention of -xt- suggests a panel structure to your data, not hinted at hitherto in this thread. Some of my colleagues at work specialise in "remote sensing", which can be spectacularly successful, but I don't think that "remote statistics" is either necessary or admirable. Nick [email protected] moleps I appreciate the inputs, but wouldnt modeling year either by xtpoisson or by dummy-coding, make it "hang tgether" ?? Or would I have to go for a autoregressive Poisson model? On 18. jan. 2010, at 16.00, Nick Cox wrote: > This doesn't seem to hang together. > > The confidence intervals from -ci- treat years separately, i.e. independently. > > That's a quite different model from a Poisson regression with -year- as a predictor -- not to mention a custom-built stochastic model that matches epidemiological knowledge for the condition you're studying. Martin Weiss > You can replace your entire loop by > > statsby mean=r(mean) ub=r(ub) lb=r(lb), /* > */ by(year) clear : ci cases,e(pop2) pois > > moleps > > After scrutinizing the manuals- this is as close as I get: > gen pop2=population/100000 > capt drop mean up lb > gen mean=. > gen up=. > gen lb=. > > forval k=1953/2008{ > > ci cases if year==`k',e(pop2) pois > replace mean=`r(mean)' if year==`k' > replace up=`r(ub)' if year==`k' > replace lb=`r(lb)' if year==`k' > } > sort year > tw (line mean year) (lowess mean year, lw(thick) bw(0.3)) (lowess up > year,bw(0.3)) (lowess lb year,bw(0.3)),xlab(1950(10)2010) xlab(,ang(45)) > xtit("Year") ytit("Incidence (per 100,000)") legend(lab(1 Iincidence") > lab(2 "Lowess") lab(3 "95% CI Upper bound") lab(4 "95% CI Lower bound")) > leg(col(1)) > > However looking at the graph and CI´s the upper-limit in the ´50s seem to be > lower than the lower-limit in the 2000´s, hence contradicting the poisson > regression model (both by continuous year and as "dummy-years,ie i.year" > where year is insignificant after correcting for population. I thought > overlapping CI-lines signified a significant effect or is there a problem > with the ci-calculations?? > > On 17. jan. 2010, at 20.11, moleps wrote: > >> I´ve got the incidence of a disease in a population in the following data > format -year,population,cases- >> I´ve 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´d like to add 95% CI lines to > corroborate this. Is this at all possible given my dataset?? > > >> | 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 | >> +----------------------------+ >> * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: incidence rate and confidence intervals***From:*moleps <[email protected]>

**Re: st: incidence rate and confidence intervals***From:*moleps <[email protected]>

**RE: st: incidence rate and confidence intervals***From:*"Martin Weiss" <[email protected]>

**RE: st: incidence rate and confidence intervals***From:*"Nick Cox" <[email protected]>

**Re: st: incidence rate and confidence intervals***From:*moleps <[email protected]>

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