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V.A.Morgan /tt/file_convert/5a7074227f8b9ab6538bf241/document.docx5 July 2012

USING WEIGHTS WITH SHIP DATAVersion: 05 July 2012

Contents

USING WEIGHTS WITH SHIP DATA.........................................................................1General introduction...........................................................................................2Weights in Stata.................................................................................................3Weights in SPSS..................................................................................................4Weights in SAS...................................................................................................5

APPENDICES...........................................................................................................6Stata example output.........................................................................................6

Stata: Mean.....................................................................................................6Stata: Tabulate................................................................................................6Stata: Logistic regression................................................................................7

SPSS example output.........................................................................................9SPSS: Complex Samples Analysis Plan............................................................9SPSS: Complex Samples Descriptives...........................................................10SPSS: Complex Samples Tables.....................................................................11SPSS: Complex Samples Logistic regression.................................................15

SAS example output.........................................................................................17SAS: The SURVEYMEANS Procedure..............................................................17SAS: The SURVEYFREQ Procedure.................................................................18SAS: The SURVEYLOGISTIC Procedure...........................................................19

1


General introduction

1. Weighted analyses should be only be done using appropriate statistical software:

Stata svy or pweight commands SPSS Complex Samples SAS Survey Procedures

2. The weights to use in Stata, SPSS Complex Samples and SAS Survey Procedures are in the variable:

weightsa

3. Weightsa may also be used for subgroup analyses using Stata commands, SPSS Complex Samples and SAS Survey Procedures.

4. If working with the full SHIP dataset based on 1825 survey participants, using weights results in a population N of 7955. For SHIP, ‘population’ refers to the individuals ascertained in the screening phase This N will be smaller for subsamples (e.g. those meeting full ICD-10 criteria for psychosis; schizophrenia only etc.).

5. It is very important that weightsa be used only as a sampling (or probably) weight. Using it as a weight in a Stata [fweight=…] option, the SPSS “Weight by…” command or as a WEIGHT or FREQ variable in a normal SAS proc will lead the program to believe that 7955 participants were actually interviewed leading to extremely serious errors in many statistics.

6. When relevant strata information is included in a model, the ‘need’ for weighting is dramatically reduced. Site strata information is not available, but our experience to date is that when age is included in models (e.g., regression), the difference in estimates and standard errors between weighted and unweighted data is relatively small. Nevertheless, if it possible to use weighting, it should be done for accuracy and for uniformity across analyses.

7. For information only: there is a variable that Andrew has created called weightsa_nrm which gives an approximation for those using basic SPSS and basic SAS. However we are not recommending that people work in basic SPSS or basic SAS with this variable and will not be providing it unless the person requesting to use this variable can justify their request.

2


Weights in Stata

In Stata, there are two approaches, depending on the procedure.

(a) For many procedures (e.g. mean; logistic), you can use the [pweight=weightsa] option

(b) Other procedures (e.g. tabulate) need to be run in conjunction with the survey (svy) feature. The survey feature can also be used with procedures that permit the [pweight=weightsa] option.

The survey structure must be first specified in svyset if you need to use the survey procedure:

* Specifying the survey structure before using the survey (svy) feature.

svyset [pweight=weightsa]

This will produce the following information outputpweight: weightsaVCE: linearizedSingle unit: missingStrata 1: <one>SU 1: <observations>FPC 1: <zero>

* Means using (a) pweight and (b) the survey feature. Note that (b) assumes you have specified the survey structure as above.

(a) mean age_calc [pweight = weightsa]

(b) svy: mean age_calc

* Tables using the survey feature. Note that this assumes you have specified the survey structure as above.

svy: tabulate sex DIAGicd10, count ci

* Logistic Regression a) pweight and (b) the survey feature. Note that (b) assumes you have specified the survey structure as above..

(a) logit anyIP_rev age_calc [pweight = weightsa]

(b) svy: logit anyIP_rev age_calc

3


Weights in SPSS

The appropriate SPSS Complex Samples analysis plan is in a file called SHIP_SPSSweighting.csaplan which is created by running the syntax below:

CSPLAN ANALYSIS /PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan' /PLANVARS ANALYSISWEIGHT=weightsa /SRSESTIMATOR TYPE=WR /PRINT PLAN /DESIGN /ESTIMATOR TYPE=WR.

You must use the SPSS Complex Samples battery of statistics with this plan. For example:

* Complex Samples Descriptives.

CSDESCRIPTIVES /PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan' /SUMMARY VARIABLES=age_calc /MEAN /STATISTICS SE COUNT POPSIZE CIN(95) /MISSING SCOPE=ANALYSIS CLASSMISSING=EXCLUDE.

* Complex Samples Tabulate.

CSTABULATE /PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan' /TABLES VARIABLES=sex BY DIAGicd10 /CELLS POPSIZE COLPCT /STATISTICS CIN(95) /MISSING SCOPE=TABLE CLASSMISSING=EXCLUDE.

* Complex Samples Logistic Regression.

CSLOGISTIC anyIP WITH age_calc /PLAN FILE='D:\SHIPdata_files\SHIP_SPSSweighting.csaplan' /MODEL age_calc /INTERCEPT INCLUDE=YES SHOW=YES /STATISTICS PARAMETER EXP CINTERVAL /TEST TYPE=F PADJUST=LSD /ODDSRATIOS COVARIATE=[age_calc(1)] /CRITERIA MXITER=100 MXSTEP=5 PCONVERGE=[1E-006 RELATIVE] LCONVERGE=[0] CHKSEP=20 CILEVEL=95 /PRINT SUMMARY CLASSTABLE VARIABLEINFO SAMPLEINFO.

4


5


Weights in SAS

For SAS Complex Survey Design, SAS uses specific commands when using weight variable. For example: for Means, SAS uses Proc Surveymeans as the main command instead of 'Proc Means'. Similarly for frequency and logistic procedures, SAS uses Proc Surveyfreq and Proc Surveylogistic respectively

SAS SURVEY PROCEDURES COMMANDS. For example:

*Complex survey design: MEAN PROCEDURE.

Proc Surveymeans data=xxx.xxxxxx; weight weightsa;var age_calc;Title 'MEAN PROCEDURE FOR AGE';run;

*Complex survey design: CROSS TABULATION.

Proc Surveyfreq data=xxx.xxxxxx ; weight weightsa;tables sex*DIAGICD10;Title 'Cross tabulation of Sex by Psychotic illness';run;

*Complex survey design: LOGISTIC REGRESSION.

Proc Surveylogistic data= xxx.xxxxxx order=internal;weight weightsa; Model anyIP (ref=last order=internal)= age_calc /link=glogit; Title 'Effect of age on inpatient admission'; run;

w

6


APPENDICESStata example output

7

Stata: Mean

Stata: Tabulate


8

Stata: Logistic regression


9


SPSS example output

SPSS: Complex Samples Analysis Plan

* SHIP_SPSSweighting_egs.sps.

* Analysis Preparation Wizard.CSPLAN ANALYSIS /PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' /PLANVARS ANALYSISWEIGHT=weightsa /SRSESTIMATOR TYPE=WR /PRINT PLAN /DESIGN /ESTIMATOR TYPE=WR.

Complex Samples: Plan

SummaryStage 1

Analysis Information Estimator Assumption Sampling with replacement

Plan File: D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplanWeight Variable: weightsa Sampling weight by site and age stratumSRS Estimator: Sampling with replacement

CSPLAN VIEW /PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' .

Complex Samples: PlanAnalysis Plan

SummaryStage 1

Analysis Information Estimator Assumption Sampling with replacement

Plan File: D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplanWeight Variable: weightsa Sampling weight by site and age stratumSRS Estimator: Sampling with replacement

10


SPSS: Complex Samples Descriptives* Complex Samples Descriptives.CSDESCRIPTIVES /PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' /SUMMARY VARIABLES=age_calc /MEAN /STATISTICS SE COUNT POPSIZE CIN(95) /MISSING SCOPE=ANALYSIS CLASSMISSING=EXCLUDE.

Complex Samples: DescriptivesUnivariate Statistics

Estimate Standard Error 95% Confidence IntervalLower Upper

Mean age_calc Age (calculated) in years at time of interview 39.47 .273 38.93 40.00

Univariate StatisticsPopulation Size Unweighted Count

Mean age_calc Age (calculated) in years at time of interview 7955.000 1825

11


SPSS: Complex Samples Tables* Complex Samples Tables.CSTABULATE /PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' /TABLES VARIABLES=sex BY DIAGicd10 /CELLS POPSIZE COLPCT /STATISTICS CIN(95) /MISSING SCOPE=TABLE CLASSMISSING=EXCLUDE.

Complex Samples: Tablessex sex * DIAGicd10 DIP ICD-10

sex sex DIAGicd10 DIP ICD-10

1 schizophrenia

0 male

Population SizeEstimate 2650.360

95% Confidence IntervalLower 2464.343Upper 2836.378

% within DIAGicd10 DIP ICD-10Estimate 70.4%

95% Confidence IntervalLower 67.1%Upper 73.6%

1 female


95% Confidence Interval Lower 974.388Upper 1251.344


95% Confidence Interval Lower 26.4%Upper 32.9%

Total





sex sex * DIAGicd10 DIP ICD-10sex sex DIAGicd10 DIP

ICD-102 schizoaffective

0 male





1 female





Total





12



ICD-103 bipolar, mania

0 male





1 female





Total






ICD-104 depressive

psychosis

0 male





1 female





Total





13



ICD-105 delusional

disorders and other non-organic psychosis

0 male





1 female





Total






ICD-106 severe

depression without psychosis

0 male





1 female





Total





14



ICD-107 screen-positive for psychosis but did not meet full

criteria for ICD-10 psychosis

0 male





1 female





Total





sex sex * DIAGicd10 DIP ICD-10sex sex DIAGicd10

DIP ICD-10Total

0 male





1 female





Total





15


SPSS: Complex Samples Logistic regression

* Complex Samples Logistic Regression.CSLOGISTIC anyIP WITH age_calc /PLAN FILE='D:\SHIPdata_files\DataMain\weighting\SPSS\SHIP_SPSSweighting.csaplan' /MODEL age_calc /INTERCEPT INCLUDE=YES SHOW=YES /STATISTICS PARAMETER EXP CINTERVAL /TEST TYPE=F PADJUST=LSD /ODDSRATIOS COVARIATE=[age_calc(1)] /CRITERIA MXITER=100 MXSTEP=5 PCONVERGE=[1E-006 RELATIVE] LCONVERGE=[0] CHKSEP=20 CILEVEL=95 /PRINT SUMMARY CLASSTABLE VARIABLEINFO SAMPLEINFO.

Complex Samples: Logistic Regression

Sample Design InformationN

Unweighted CasesValid 1825Invalid 0Total 1825

Population Size 7955.000

Stage 1 Strata 1Units 1825

Sampling Design Degrees of Freedom 1824

Categorical Variable InformationWeighted Count Weighted Percent

anyIP Any inpatient admissions - past yeara

0 no/na/dk 4552.607 57.2%1 yesb 3402.393 42.8%

Population Size 7955.000 100.0%

a. Dependent Variableb. Reference Category

Covariate InformationMean

age_calc Age (calculated) in years at time of interview 39.47

Pseudo R SquaresCox and Snell .002Nagelkerke .003McFadden .002

Dependent Variable: anyIP Any inpatient admissions - past year (reference category = 1 yes) Model: (Intercept), age_calc

Tests of Model EffectsSource df1 df2 Wald F Sig.(Corrected Model) 1.000 1824.000 3.883 .049(Intercept) 1.000 1824.000 .101 .751age_calc 1.000 1824.000 3.883 .049


16


Parameter EstimatesanyIP Any inpatient admissions - past year

Parameter B 95% Confidence Interval Exp(B)Lower Upper

0 no/na/dk(Intercept) -.057 -.407 .293 .945age_calc .009 4.109E-005 .018 1.009

Parameter EstimatesanyIP Any inpatient admissions - past year Parameter 95% Confidence Interval for Exp(B)

Lower Upper

0 no/na/dk(Intercept) .666 1.341age_calc 1.000 1.018


ClassificationObserved Predicted

0 no/na/dk 1 yes Percent Correct0 no/na/dk 4552.607 .000 100.0%1 yes 3402.393 .000 0.0%Overall Percent 100.0% 0.0% 57.2%


Odds Ratiosa

Units of Change anyIP Any inpatient admissions - past year

Odds Ratio

age_calc Age (calculated) in years at time of interview 1.000 0 no/na/dk 1.009

Odds Ratiosa

Units of Change anyIP Any inpatient admissions - past year 95% Confidence IntervalLower


Odds Ratiosa

Units of Change anyIP Any inpatient admissions - past year 95% Confidence IntervalUpper


Dependent Variable: anyIP Any inpatient admissions - past year (reference category = 1 yes) Model: (Intercept), age_calca

a. Factors and covariates used in the computation are fixed at the following values: age_calc Age (calculated) in years at time of interview=39.47

17


SAS example output

SAS: The SURVEYMEANS Procedure

MEAN PROCEDURE FOR AGE

Data SummaryNumber of Observations

1825

Sum of Weights 7955

Statistics

Variable Label N Mean

Std Error of

Mean 95% CL for MeanAGE_CALC Age (calculated) in years at time of

interview182

539.4656

170.27287

738.93043

2240.00080

12

18


SAS: The SURVEYFREQ Procedure

Cross tabulation of Sex by Psychotic illness

Data SummaryNumber of Observations

1825

Sum of Weights 7955

Table of SEX by DIAGICD10

SEX DIAGICD10Frequenc

y

Weighted

Frequency

Std Dev of

Wgt FreqPerce

nt

Std Err of

Percentmale schizophrenia 612 2650 94.84571 33.316

91.1668

schizoaffective 157 623.91958

50.64980 7.8431 0.6389

bipolar, mania 141 621.15590

53.24893 7.8084 0.6662

depressive psychosis 33 146.11248

27.03131 1.8367 0.3392

delusional disorders and other non-organic psychosis

65 303.38049

39.57960 3.8137 0.4950

severe depression without psychosis

62 288.22831

38.35848 3.6232 0.4800

screen-positive for psychosis but did not meet full criteria for ICD-10 psychosis

17 79.34982 19.93295 0.9975 0.2504

Total 1087 4713 103.79991

59.2396

1.2216

female

schizophrenia 245 1113 70.60636 13.9895

0.8748

schizoaffective 136 540.86291

47.49690 6.7990 0.5987

bipolar, mania 178 774.09087

58.69530 9.7309 0.7339


33.29560 2.7892 0.4174


27 117.84948

24.27508 1.4815 0.3048


96 427.94215

45.43072 5.3795 0.5684


8 46.99893 17.08476 0.5908 0.2144

19


Table of SEX by DIAGICD10

SEX DIAGICD10Frequenc

y

Weighted

Frequency

Std Dev of

Wgt FreqPerce

nt

Std Err of

Percent

Total 738 3242 101.39056

40.7604

1.2216

Total schizophrenia 857 3763 103.66711

47.3064

1.2396

schizoaffective 293 1165 66.71838 14.6421

0.8442

bipolar, mania 319 1395 75.85090 17.5392

0.9430


42.47047 4.6260 0.5315


92 421.22997

46.00675 5.2952 0.5745


158 716.17047

58.31017 9.0028 0.7259


25 126.34875

26.17484 1.5883 0.3282

Total 1825 7955 65.57273 100.000

20


SAS: The SURVEYLOGISTIC Procedure

Logistic regressionEffect of age on inpatient admission

Model InformationData Set SAHA.SHIPDATAResponse Variable ANYIP Any inpatient admissions -

past yearNumber of Response Levels

2

Weight Variable weightsa weightsaModel Generalized LogitOptimization Technique

Newton-Raphson

Variance Adjustment Degrees of Freedom (DF)

Variance EstimationMethod Taylor SeriesVariance Adjustment

Degrees of Freedom (DF)

Number of Observations Read

1825

Number of Observations Used

1825

Sum of Weights Read 7955

Sum of Weights Used 7955

Response ProfileOrdere

dValue ANYIP

TotalFrequen

cyTotal

Weight1 no/

na/dk1028 4552.60

712 yes 797 3402.39

29

Logits modeled use ANYIP='yes' as the reference category.

21


Model Convergence StatusConvergence criterion (GCONV=1E-8)

satisfied.

Model Fit Statistics

Criterion

Intercept

Only

Interceptand

Covariates

AIC 10863.078

10846.426

SC 10868.587

10857.445

-2 Log L 10861.078

10842.426

Testing Global Null Hypothesis: BETA=0

TestChi-

Square DFPr > ChiS

qLikelihood Ratio

18.6517 1 <.0001

Score 18.6230 1 <.0001Wald 3.8805 1 0.0489

Type 3 Analysis of Effects

Effect DF

WaldChi-

SquarePr > ChiS

qAGE_CALC 1 3.8805 0.0489

Analysis of Maximum Likelihood Estimates

Parameter ANYIP DFEstima

te

Standard

Error

WaldChi-

SquarePr > ChiS

qIntercept no/na/

dk1 -0.0568 0.1786 0.1011 0.7506

AGE_CALC no/na/dk

1 0.00884 0.00449 3.8805 0.0489

Odds Ratio Estimates

Effect ANYIP

Point Estima

te

95% WaldConfidence Limit

sAGE_CALC no/na/

dk1.009 1.000 1.018

22


Association of Predicted Probabilities and Observed Responses

Percent Concordant

51.8 Somers' D

0.062

Percent Discordant

45.7 Gamma 0.063

Percent Tied 2.5 Tau-a 0.030

Pairs 819316

c 0.531

23

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