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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: SV: st: Survey - raking - calibration - post stratification - calculating weights |

Date |
Sat, 6 Dec 2008 12:05:13 -0500 |

Thanks, Steve On Dec 6, 2008, at 11:40 AM, Kristian Wraae wrote:

Hi SteveThe 5000 men were randomly drawn using the Danish CPR registerwhich is adatabase containing all Danish citizens. Everybody is assigned aunique10-digit number making it possible to select people 100% at random.The number has the form DDMMYY-vxyz with DDMMYY being date ofbirth. Z anequal number for women and unequal for men.The 5000 men were selected in a way that they reflected the backgroundpopulation regarding age and zip code. They were aged 60 to 74 years at the date of data acquisition.So the information we have on the 5000 men is 100% accurate andthey are a100% match for the back ground population but the only informationwas ageand zip code.The questionnaire we mailed to the 5000 men contained a lot ofinformation.For the 3750 who filled out the questionnaire we know all chronicdiseaseson ICD10 codes, all medication on ATC codes, partner status, level of education, job situation, housing, smoking habits, physical activity, height, weight, number of children, sexual problems.The study is a cross sectional study examining androgens andrelations tobody composition, health status, life style, quality of life, sexual dysfunction, physical performance, genetics etc. You can see a PowerPoint file with the inclusion procedure here: www.euphonium.dk/Inclusion.pptI sought equal numbers in each group in order not to end up withtoo fewpeople amongst the eldest. I wanted a good representation in allage stratasince we wanted to be able to make reference intervals for thedifferentandrogens for healthy 60-74 year olds and because we mainlyinvestigateassociations and we are not primarily interested in thedistribution in theback ground population. But I'd like to be able to make someestimates. Asan example I'd like to know the prevalence of erectile dysfunctionin theback ground population or estimate the prevalence of hypogonadism or diabetes using the data from the 600 very thoroughly examined men. If you can help me I'll be very grateful Best regards Kristian Wraae -----Oprindelig meddelelse----- Fra: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af StevenSamuelsSendt: Saturday, December 06, 2008 5:04 PM Til: statalist@hsphsun2.harvard.edu Emne: Re: st: Survey - raking - calibration - post stratification - calculating weights -- Kristian, before I can answer your questions, I need details of how you selected the original 5,000 men. Describe the target population; the sampling "frame" (or list) from which you drew the sample of 5,000; what information you have about the population (age distribution, for example); what information about each man on the list was available; exactly how you selected the 5,000. Also, what is the purpose of the study? Why did you seek equal numbers of men in the 15 age groups at later stages? -Stevequestionaire.On Dec 6, 2008, at 5:32 AM, Kristian Wraae wrote:Hello all I have a question regarding how to weight a data set. The data is from a population based cross sectional study. 5000 randomly selected men reflecting the backgound population were mailed a questionaire. 75% responsrate. 3750 questionnaires filled out. We know the age and the zip code for non-responders. The questionnaire contained several sociodemographic parameters s1, s2, ..... Sn Then 1845 men from the group that had filled out a questionnaire were invited to take part in a scientific project. The men were randomly selected with an equal number in each age group (15 age groups of one year intervals). So 123 men in each group. 946 men accepted a telephone call. 768 men never responded and 131 refused to be interwied on telephone. 864 men of the 946 were then randomly contacted with equal numbers in eachage group and 697 men agreed to take part in the project. 97 menlatercancelled or never showed up. So 600 men were included for further studies. Now I would like to weight these 600 men so they reflect the background population in order to estimate the distribution of different measures in the background population (X1, X2, .... Xi) based on measures amongst the 600 men (Y1, Y2, ..... Yi). How do I do this? As far as I can tell I need to compensate for the differences between the 5000 and the 3750 and between the 3750 and the 600. Since the 1845 were randomly selected and an equal number in each age group were contacted I assume that all men had an equal probability of being included so design weights are not really needed. Right? But how do I compute a pweight that takes the two steps into account?

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**Follow-Ups**:**Re: SV: st: Survey - raking - calibration - post stratification - calculating weights***From:*"Stas Kolenikov" <skolenik@gmail.com>

**SV: SV: st: Survey - raking - calibration - post stratification - calculating weights***From:*"Kristian Wraae" <Kristian_Wraae@vip.cybercity.dk>

**References**:**SV: st: Survey - raking - calibration - post stratification - calculating weights***From:*"Kristian Wraae" <Kristian_Wraae@vip.cybercity.dk>

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