On a Data-Mining Concept to Analyse Epidemiologic Data Using SAS Software. Hans-Peter Altenburg Mannheim

Transcription

1 On a Data-Mining Concept to Analyse Epidemiologic Data Using SAS Software Hans-Peter Altenburg Mannheim hpa-ma@gmx.de

2 Outline Introduction Data Base: EPIC Cohort Study Problem to be solved / Task Concept for Program Organization Statistical epidemiological Background Realization within the SAS System Example

3 Introduction Task: On the base of an european-wide distributed cohort Find relationships and new results (findings) between life style and dietary intake and the development of (Cancer-) diseases.

4 Examples Dietary intake Intake of fat or meat Processed / salted meat or food Smoking (active / passive) stomach / colon cancer colon, breast, prostate carcinoma stomach carcinoma lung cancer underlying biological mechanisms are more complex than one might expect from chemoprevention studies ( one molecule, one effect ) EPIC-Study

5 Introduction SAS Objective Build a flexible tool to detectand to analyse relationships on the base of epidemiologic and statistical analysis criteria.

6 Introduction Examination criteria Risk measures of epidemioly Hazard ratio / relative risk Odds Ratio Attributable risk...

7 Data Base

8 I.A.R.C W.H.O Europe Against Cancer European Commission EPIC European Prospective Investigation into Cancer and Nutrition Study Design:Cohort Study

9 EPIC-Studie European Prospective Investigation into Cancer und Nutrition Cohort Study European wide: 9 countries, 29 centres ~ participants ~ variables (dependant on tumor type / analysis / objective)

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11 EPIC: Background Relation Dietary intake Cancer Problem: etiologic importance of single food intake components as well as their quantitative contribution for cancer development e.g. influence of the fat proportion in the food, intake of vitamins, etc.

12 Task Development concept to analyse data and search for new and /or novel relationships Base: EPIC cohort SAS system Start: Analysis of fruits and vegetables intake and Lung cancer

13 SAS Task Whole concept should be flexibly adaptable Cohort study implies: Changes in Data Data management Variable names (for instance main objective criterion) e.g.caselung caselun, casepanc, caselymp, etc. Formats Inclusion / Exclusion of variables / Objects...

14 SAS Task Examples for exclusion criteria Prevalent cancer cases Centre Malmö participants (no dietary data) Country Greece (no dietary data) Persons with extreme EI/ER Ratio... EI/ER: Energy Intake/Energy Requirement

15 Changes in: Objective variable SAS Task e.g. initially Lung cancer, followed by stomach Ca, Lymphoma, etc. e.g. reference group: first quantile (group) or lowest (consumption) group influence / effect variables Adjustment variables Stratification Grouping in the analysis step...

16 Basic: Statistical Procedures Tools of descriptive epidemiology: Such as disease incidence or similar measures, graphics, etc. Analytic analysis tools: Determination of Hazard Ratios Using the Cox Proportional Hazard Model

17 Statistical Analysis Estimation procedure for the Hazard Ratios: Cox Proportional Hazard Regression Dependant Variable: Age Analysis / Mining Process: Flexible handling of distinct factors for the statistical modelling Data preparation / management Stratification Adjustment

18 Statistical Analysis Flexible Handling of Stratification: centres / countries / Northern / South Europe / Gender Adjustments: Follow up time primäry / secundary influence variables weight, height, physical activity, Consumption of additional dietary components, etc. smoking...

19 Example time dependant Cox Model Time scale: age Objective variable: caselun Time dependant covariable: length of follow-up Stratification: country Adjustment for: energy intake and sex PROC PHREG DATA=puk.stan; Model (age,agexit)*caselun(0)= qg07 qener sex inf ins / RISKLIMITS ; inf = (agexit < age + 1); ins = (age + 1 <= agexit < age + 2); STRATA country ; RUN ;

20 Concept / Organization Environment / File structure: per objective variable: Lyon delivers a Data Mart from an Oracle DB with the variables / formats required for a specified analysis per objective variable: the data are stored in a sas data table (rectangular structure) in a specific directory for all data structures: unified format structure (Lyon formats and own additional formats) per objective variable / analysis objective:there exists a special autoexec.sas file, where all macro variables are preset

21 Example autoexecp.sas z.b. Pancreas Carcinoma: /* */ %LET calibr=panc ; * lung panc lymp ; LIBNAME &calibr "D:\EPIC_&calibr\&..." ; /* */ LIBNAME hpafmt 'D:\EPIC_&CALIBR\HPA_FORMATS' ; LIBNAME lyonfmt 'D:\EPIC_&CALIBR\FORMATS' ; /* */ OPTIONS pagesize=56 linesize=80 nonumber nodate nocenter probsig=1 FMTSEARCH=(lyonfmt hpafmt) MAUTOSOURCE SASAUTOS=('D:\sasdat\m', sasautos) ; %INCLUDE 'D:\sasdat\m\mgopt.mac' ; DM 'PGM ; ZOOM ON ; ' ; /* */ %INCLUDE "D:\EPIC_&calibr\&P\&ds_info..sas" ;

22 Data Mining Process: Problems Cox Model Analysis for many variants and / or combinations according Objective criterion influence / effect variables Stratification Adjustments...

23 SAS Program: Macros / variables /* Hazard Ratios Heavy Drinkers BMI, diabetes, Smoking (Y/N), groups for Men, Women and gender stratified stratified for cntr_c adjusted (details see below) */ %LET dset=&calibr..&dsname ; /* */ OPTIONS source2 MPRINT ; %INCLUDE 'D:\sasdat\mepi\PH_std.sas' ; %INCLUDE 'D:\sasdat\mepi\HR_table.sas' ; * Vars ; %LET v_wght=obesity ovweight bmi_over25 ; %LET v_khk =diabetes ; %LET v_consvar=hdrinker ;

24 SAS Program: Variables / Output data sets * Smoking Vars ; %LET v_sm = smoker exsmoker ; * nosmoker ; %LET v_avnbcig = avncig_c0 avncig_c1 avncig_c2 avncig_c3 avncig_c4 avncig_c5 ; * avncig_c0 ; * Age var ; %LET agevar=age ; * ; Name conventions ; %LET adjn =PadjV ; /* Name of data set */ %LET adjdn =BDSm ; /* Name of data set adj vars */ %LET adjvt =_ ; /* text adj var */ %LET adjt =&adjvt ; /* title text add adj var */ %LET repn =Report0406 ; /* Name of report directrory */ %LET e_name=av ; * dset name for estvalues, rtf doc etc. ; %LET dsn_out=&e_name.&adjn ; * output name (results dset) ;

25 SAS Program: Adjustment / Strata /* Adjustment Variables in Data Set: */ %LET adjadd =Hdrinker bmi_over25 diabetes ; /* Use upper case letters only for IARC Vars, e.g. HEIGHT_C WEIGHT_C ; %LET adjust1= &v_sm ; %LET adjust= &adjadd &adjust1 ; /* HEIGHT_C WEIGHT_C QENER ener_fat enernfat */ * Reference text: ; %LET reference=nonconsumers ; * Nonconsumers 1st Quintile ; %LET var_text=selected Food Groups ; * for Headline summary table ; %LET keepvars=sex agexit ; * country smoke_r e_south ; /* Keep Variables */ /* Strata */ %LET strata1=cntr_c ; %LET strata2=cntr_c ; %LET strata3=sex cntr_c ; /* Data Title */ %LET dtitle1=men only ; %LET dtitle2=women only ; %LET dtitle3=gender Strata ;

26 SAS Program: Title / Footnote /* Project */ %LET project1=&dtitle1 / adj. for: &adjvt - &adjt ; %LET project2=&dtitle2 / adj. for: &adjvt - &adjt ; %LET project3=&dtitle3 / adj. for: &adjvt - &adjt ; /* Data Set for summary table */ %LET estvalues1=&calibr..&dsn_out._&adjdn._m ; %LET estvalues2=&calibr..&dsn_out._&adjdn._f ; %LET estvalues3=&calibr..&dsn_out._&adjdn._g_strata ; /* Rtf File */ %LET rtffile1=d:\epic_&calibr\&repn\&dsn_out._&adjdn._m.rtf ; %LET rtffile2=d:\epic_&calibr\&repn\&dsn_out._&adjdn._f.rtf ; %LET rtffile3=d:\epic_&calibr\&repn\&dsn_out._&adjdn._g_strata.rtf ; /* */ /* */ /* */

27 SAS Program: Introductory text / Analysis ODS RTF FILE="&rtffile1" ; * < Men ; %LET est_cum=&estvalues1 ; %LET dtitle=&dtitle1 ; DATA _NULL_ ; FILE PRINT ; PUT / 'DATA ANALYSIS' /// "&Ca_text" // "&ds_recv" /// "&dtitle" /// /// "Hazard Ratios" // "Project: &project1" /// 'adjusted for:' /// "&adjust" / ; RUN ; %LET where_c=%str(sex=1) ; * < Men ; %LET strata =&strata1 ; %INCLUDE "D:\EPIC_&calibr\&P\&P_QG\&P.sas" ; * ; /* */ ODS RTF CLOSE ;

28 SAS Program: Introductory text / Analysis ODS RTF FILE="&rtffile3" ; * < Gender Strata --- ; %LET est_cum=&estvalues3 ; %LET dtitle=&dtitle3 ; DATA _NULL_ ; FILE PRINT ; PUT / 'DATA ANALYSIS' /// "&Ca_text" // "&ds_recv" /// "&dtitle" /// /// "Hazard Ratios" // "Project: &project3" /// 'adjusted for:' /// "&adjust" / ; RUN ; %LET where_c= ; * < Gender Strata --- ; %LET strata =&strata3 ; %INCLUDE "D:\EPIC_&calibr\&P\&P_QG\&P_dummy.sas" ; * ; /* */ ODS RTF CLOSE ;

29 Cox-Model SAS Macro Call /* Dummy call without any effect / influence variables */ %LET t_leer =no alc Vars ; * < ; %LET q_leer = ; * < ; %LET qqq =&q_leer ; %LET ttt =&t_leer ; %PH_std(&dset,&where_c,&keepvars,&agevar,,&cavar,0,,&strata,,, &qqq,&adjust, inf ins, %STR(inf = ( agexit < age + 1) ; ins = (age + 1 <= agexit < age + 2) ; ),outph,&est_cum,est,estph,0.05,&ttt )

30 Cox-Model SAS Macro Call /* with effect variables */ %LET t14 =Alcoholic Beverages ; * < ; %LET q14 =alcb_c1 alcb_c2 alcb_c3 alcb_c4 alcb_c5 ; * < ; %LET qqq =&q14 ; %LET ttt =&t14 ; %PH_std(&dset,&where_c,&keepvars,&agevar,,&cavar,0,,&strata,,, &qqq,&adjust, inf ins, %STR(inf = ( agexit < age + 1) ; ins = (age + 1 <= agexit < age + 2) ; ),outph,&est_cum,est,estph,0.05,&ttt )

31 SAS Program Final Summary Table OPTIONS LINESIZE=220 PAGESIZE=130 ; %LET ttext0=overview Hazard Ratios ; %LET ttext2=&var_text - Reference: &reference ; %LET ttext3=adjusting Variables: &adjust ; /* Overview Table 1 */ %HR_table(&estvalues1,&est_g_strat, %STR("WEIGHT_C" "HEIGHT_C" "smkd_1" "smkd_2" "smkd_3" "smkd_4" "smkd_5" "giveup1" "giveup2" "giveup3" ), &ttext0: &project1,&ttext2,&ttext3) * ;

32 Output Summary Table Heavy Drinkers only Hazard Ratios for drinkers in classes Reference: Non-drinkers (Class 0) Strata: centres (Def. B) Adjusted for: BMI>25, smokers, exsmokers Men only Variable/ Hazard Lower Upper Class No. Ratio 95%-CL 95%-CL Effect Statistically / Risk Meaningful drinker c Decreasing drinker c Decreasing bmi_over Decreasing smoker Increasing exsmoker Decreasing Women only drinker c Increasing drinker c Increasing bmi_over Increasing smoker Increasing Yes! exsmoker Increasing

33 Output Summary Table (2) Heavy Drinkers only Hazard Ratios for drinkers in classes Reference: Non-drinkers (Class 0) Strata: centres (Def. B) Adjusted for: BMI>25, smokers, exsmokers Variable/ Hazard Lower Upper Effect Statistically Class No. Ratio 95%-CL 95%-CL / Risk Meaningful Gender Strata drinker c Decreasing drinker c Increasing bmi_over Decreasing smoker Increasing Yes! exsmoker Increasing

34 Summary With corresponding preparation and planning SAS allows the realization of a Data Mining Analysis for epidemiologic questions. SAS components used (SAS Vs 8): Data Step / Macro Statements STAT Procedures ODS The Cox Model examples presented here can easily transferred to other STAT procedures such as logistic regression!

35 References 1. Breslow, N.E. / Day, N.E.: Statistical Methods in Cancer Research II: The analysis of cohort studies. Lyon, IARC Clayton, D. and Hills, M.: Statistical Models in Epidemiology. Oxford, Oxford University Press AB Miller, H-P Altenburg, et.al.: Fruits and Vegetables and Lung Cancer: Findings from the European Prospective Investigation into Cancer and Nutrition. International Journal of Cancer, 108, 2004, Newman, S.C.: Biostatistical Methods in Epidemiology. New York, Wiley 2001

36 Any questions or comments? Thank you very much for your attention!