Calculate Relative Standard Error Sas
Contents |
Index MENU CDC A-Z SEARCH A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # proc logistic cluster standard error Start of Search Controls Search Form Controls Search NCHS Cancel Submit Search The CDC National
Proc Surveylogistic Example
Center for Health Statistics Note: Javascript is disabled or is not supported by your browser. For this reason, some items on this
Proc Surveyreg
page will be unavailable. For more information about this message, please visit this page: About CDC.gov. National Home and Hospice Care Survey About NHHCS What's New Survey Methodology, Documentation, and Data Files Scope of the Survey
Proc Surveylogistic Ucla
Sample Design Data Collection and Processing Estimation Procedures Reliability of Estimates Presentation of Estimations 2007 NHHCS Medication Data National Home Health Aide Survey Survey Publications and Products Current Home Health Care Patient and Annual Hospice Care Discharge Trends Home Care - Data Highlights Hospice Care - Data Highlights NHHCS Survey Participants Related Sites Long-Term Care Listserv Surveys and Data Collection Systems National Nursing Home Survey National Survey of Residential Care Facilities National sas survey procedures Study of Long-Term Care Providers National Hospice and Palliative Care Organization National Association for Home Care NCHS Reliability of Estimates Recommend on Facebook Tweet ShareCompartir On this Page 2000 Reliability of Estimates 1998 Reliability of Estimates 1996 Reliability of Estimates 2000 Reliability of Estimates Because the data presented on this tape are based on a sample, they will differ somewhat from data that would have been obtained if a complete census had been taken using the same schedules, instructions, and procedures. The standard error (SE) is primarily a measure of the variability that occurs by chance because a sample, rather than the entire universe, is surveyed. The SE also reflects part of the measurement error, but it does not measure any systematic biases in the data. The chances are about 95 in 100 that an estimate from the sample differs from the value that would be obtained from a complete census by less than twice the SE. However, SEs typically underestimate the true errors of the statistics because they reflect only errors due to sampling. To derive error estimates that would be applicable to a wide variety of statistics, variances for a wide variety of estimates were approximated using SUDAAN software. SUDAAN computes standard errors by using a first order Taylor approximation of the deviation
is used and the name of the dataset is BP_analysis_Data. Proc descript is being used as a generic example, but these statements apply to all SUDAAN procedures. Step 1: Sorting in SAS To carry out the proc surveymeans appropriate SUDAAN design option for NHANES data, the data from BP_analysis_Data must first be sorted proc surveymeans t test by strata and then by PSU (unless the data have already been sorted by PSU within strata). The proc sort procedure in SAS proc surveyreg output must precede any SUDAAN statements. WARNING Data must always be sorted in SAS before doing analyses in SUDAAN. Step 2: Use proc statement in SUDAAN Generally, a proc statement in SUDAAN immediately follows the sort http://www.cdc.gov/nchs/nhhcs/nhhcs_estimation_reliability.htm statement. In this example, the proc descript statement is used. In addition, the data option specifies BP_analysis_Data as the SAS dataset being used, the design option specifies with replacement (WR) as the design, and the noprint option suppresses printing of results as the results will output to a SAS data file. Use the DEFT2 option statement to request the calculation of the design effect using SUDAAN Method 2 (see SUDAAN manual for details), which http://www.cdc.gov/nchs/tutorials/nhanes/surveydesign/varianceestimation/Task3.htm is the method recommended by NCHS for NHANES data. Step 3: Specify design parameters in SUDAAN The nest statement lists the variables that identify the strata and the PSU. The nest statement is required to indicate the appropriate design effect used in NHANES. As in the sort statement, the nest statement lists the stratum variable (i.e., sdmstra) first, followed by the PSU variable (i.e., sdmvpsu). The weight statement accounts for the unequal probability of sampling and nonresponse. For more information on selecting the correct weight, please see Selecting the Correct Weight in the Weighting module. The subpopn statement sets the subgroup. It is recommended that you use the subpopn statement instead ofsubsetting the data in the data step in SAS. Please see Creating Appropriate Subsets of Data for NHANES Analyses in the Weighting module for more information. The var option sets the variable of interest. The subgroup and levels statements set the categorical variables of interest and the number of levels corresponding to each categorical variable. The tables statement requests a stratified output of the categorical variables. Step 4: Specify output In this step, you will specify how the results are saved to a file because the output in the proc descript procedure was suppressed using the noprint option. The filetype option determines the type of data fil
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us http://stats.stackexchange.com/questions/179187/how-does-sas-calculate-standard-errors-of-coefficients-in-logistic-regression Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody standard error can answer The best answers are voted up and rise to the top How does SAS calculate standard errors of coefficients in logistic regression? up vote 1 down vote favorite I am doing a logistic regression of a binary dependent variable on a four-value multinomial (categorical) independent variable. Somebody suggested to me that it was better to put the independent variable in as multinomial rather than as three calculate relative standard binary variables, even though SAS seems to treat the multinomial as if it is three binaries. THeir reason was that, if given a multinomial, SAS would report std errors and confidence intervals for the three binary variables 'relative to the omitted variable', whereas if given three binaries it would report them 'relative to all cases where the variable was zero'. When I do the regression both ways and compare, I see that nearly all results are the same, including fit statistics, Odds Ratio estimates and confidence intervals for odds ratios. But the coefficient estimates and conf intervals for those differ between the two. From my reading of the underlying theory,as presented in Hosmer and Lemeshow's 'Applied Logistic Regression', the estimates and conf intervals reported by SAS for the coefficients are consistent with the theory for the regression using three binary independent variables, but not for the one using a 4-value multinomial. I think the difference may have something to do with SAS's choice of 'design variables', as for the binary regression the values are 0 and 1, whereas for the multinomial they are -1 and 1. But I don't really understand what SAS is doing there. To compare, I also did both regressions i
be down. Please try the request again. Your cache administrator is webmaster. Generated Thu, 06 Oct 2016 00:51:39 GMT by s_hv987 (squid/3.5.20)