Using PROC GENMOD to Analyse Ratio to Placebo in Change of Dactylitis. Irmgard Hollweck / Meike Best 13.OCT.2013
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1 Using PROC GENMOD to Analyse Ratio to Placebo in Change of Dactylitis Irmgard Hollweck / Meike Best 13.OCT.2013
2 Agenda 2 Introduction to Dactylitis Background Definitions: Trial Definitions:Terms Statistics: Basics Proc Genmod: Basics Implementation: Requirements 2 Solutions Simplification Generalization
3 Introduction: Dactylitis (general) 3 Associated with many different diseases (e.g. Tuberculosis, Sickle-cell) Precursors for psoriatic arthritis, ankylosing spondylitis, sickle-cell anemia Referred to as sausage digit Swelling and inflammation Direct treat of underlaying disease might prevent long-term deformation of digit.
4 Introduction: Dactylitis (general) 4 Pictures are from com/dactylitis + The Journal of Rheumatology: Dactylitis of Sausage- Shaped Digit from Ignazio Olivieri, Angela Pdula, Enrico Scarano, Raffaele Scarpa
5 Introduction: Dactylitis (clinical studies) 5 Record circumferences and tenderness of the affected digit => 20 digits (10 fingers and 10 toes) might be affected Data stored in SDTM-domain FA Analysis on LDI (Leeds Dactylitis Instrument) = score Number of affected Digits. Presence of Dactylitis.
6 Definitions: Trial 6 Treatment Groups: Active 200mg(Q2W) Active 400mg(Q4W) Placebo 0mg. Trial Design Parallel Assessment of Dactylitis performed on: Visit: 20, 40, 50, 70, 90,.. from 90 on visit = x +20. Baseline = Visit 20
7 Definitions: Dactylitis Terms 7 Number of affected digits: 0 to 20 per time point, depending on how many digits are affected. Subject with dactylitis: LDI-score criterion met on Baseline = dactylitis exists according to LDI.
8 Statistics: Basics 8 5% significance level with a 2-sided p-value is used. Number of affected digits assumes binary values. => 20 x repetition => binomial (20,p) distribution. SAS: Uses Generalized Estimation Equations (GEE) Logistic Regression for repeated measurements. Based on initial values which are generated via generalized linear model.
9 PROC GENMOD: Basics 9 p-values + repeated measurements provision link function in model statement => different distributions can be used ESTIMATE-statements: Hypothesis is tested by specifying a fixed model matrix L and testing. option E: requests that the L matrix should be displayed. option EXP: confidence limits.
10 Requirements (SAP) 10 Model will contain treatment group, visit (Baseline vs. the respective post-baseline visit), and the interaction of treatment group and visit. Model will be fitted for each post-baseline visit separately. Number of affected digits will be analyzed for each post-baseline visit separately. assumed to follow Binomial (20, p) distribution, where p is the probability of an individual digit being affected
11 Requirements (SAP) 11 The model will be a repeated measures logistic regression model Within-subject correlation will be taken into account by allowing an unstructured covariance structure between Baseline and the respective post-baseline visit the difference between each active treatment groups (and both combined) and PBO will be estimated as the ratio of odds ratios between post-baseline and Baseline visits.
12 Requirements (Mock-Shell) 12
13 Solution: The model will be fitted for each post-baseline visit separately. 13 Input dataset: Possibilities: macro call with PROC GENMOD per visit loop through the visits with PROC GENMOD in it.
14 Solution: 'The model will contain treatment group, visit and the interaction.. 14 Input dataset : SOLUTION 1: proc genmod data = ds(where = (trt_no in(4 5 6) and avisitn in(20 &vis))); class usubji trt_no avisitn; model aval/n = trt_no avisitn trt_no*avisitn; repeated subject = usubjid / corr = unstr; estimate "ACT 200 Vs PBO BL &vis" trt_no*avisitn / e exp; estimate "ACT 400 Vs PBO BL &vis" trt_no*avisitn / e exp; estimate "ALL ACT Vs PBO BL &vis" trt_no*avisitn / e exp; ods output estimates = num_&vis; run; SOLUTION 2: proc genmod data=ds (where=( trt_no IN (4,5,6) AND avisitn IN (20,&&avisitn&visno))) descending; class usubjid trt_no(ref=first) avisitn (ref=first) / param=ref; model aval/n = trt_no avisitn trt_no*avisitn / dist=bin link=logit; repeated subject = usubjid /withinsubject=avisitn type=un; estimate "ACT 200 Vs PBO" trt_no*avisitn 1 0 / exp; estimate "ACT 400 Vs PBO" trt_no*avisitn 0 1 / exp; estimate "ALL ACT Vs PBO" trt_no*avisitn /exp; ods output estimates =_estimatesa; run;
15 Solution: The model will contain treatment group, visit and the interaction.. 15 Input dataset : SOLUTION 1: proc genmod data = ds(where = (trt_no in(4 5 6) and avisitn in(20 &vis))); class model repeated estimate estimate estimate ods output run; SOLUTION 2: proc genmod data=ds (where=( trt_no IN (4,5,6) AND avisitn IN (20,&&avisitn&visno))) descending; class model repeated estimate estimate estimate ods output run;
16 Solution: The model will contain treatment group, visit and the interaction.. 16 Input dataset + Loop: SOLUTION 1: proc genmod data class model aval/n = trt_no avisitn trt_no*avisitn; repeated subject = usubjid / corr = unstr; estimate estimate estimate ods output run; SOLUTION 2: proc genmod data= class model aval/n = trt_no avisitn trt_no*avisitn / dist=bin link=logit; repeated subject = usubjid /withinsubject=avisitn type=un; estimate estimate estimate ods output run;
17 Solution: ratio of odds ratios 17
18 Solution: The model will contain treatment group, visit and the interaction.. 18 Input dataset SOLUTION 1: proc genmod data = ds(where = (trt_no in(4 5 6) and avisitn in(20 &vis))); class usubji trt_no avisitn; model repeated estimate "ACT 200 Vs PBO BL &vis" trt_no*avisitn / e exp; estimate "ACT 400 Vs PBO BL &vis" trt_no*avisitn / e exp; estimate "ALL ACT Vs PBO BL &vis" trt_no*avisitn / e exp; ods output run; SOLUTION 2: proc genmod data=ds (where=( trt_no IN (4,5,6) AND avisitn IN (20,&&avisitn&visno))) descending; class usubjid trt_no(ref=first) avisitn (ref=first) / param=ref; model repeated subject estimate "ACT 200 Vs PBO" trt_no*avisitn 1 0 / exp; estimate "ACT 400 Vs PBO" trt_no*avisitn 0 1 / exp; estimate "ALL ACT Vs PBO" trt_no*avisitn /exp; ods output run;
19 Solution: The model will contain treatment group, visit and the interaction Output: For ACT 200 visit 40 SOLUTION 1: Standard 95% Confidence Parameter Estimate Error Limits Z Pr > Z Intercept TRT_NO TRT_NO TRT_NO AVISITN AVISITN TRT_NO*AVISITN TRT_NO*AVISITN TRT_NO*AVISITN TRT_NO*AVISITN TRT_NO*AVISITN TRT_NO*AVISITN Coefficients for Contrast ACT 200 Vs PBO 40 Prm1 - Prm12 ACT 200 Vs PBO vis SOLUTION 2: Standard 95% Confidence Parameter Estimate Error Limits Z Pr > Z Intercept TRT_NO TRT_NO AVISITN TRT_NO*AVISITN TRT_NO*AVISITN For ACT 200 vs PBO vis 40 : *( ) + 0 =
20 Solution: The model will contain treatment group, visit and the interaction... output: For ACT 200 visit 40 20
21 Solution: The model will contain treatment group, visit and the interaction... output: For ACT 200 visit Interpretation: Odds ratio =1 Odds ratio < 1 => ACTIVE = PLACEBO => ACTIVE has negative effect on response = treatment is positive (see obs 4+5: ACT ALL, significant) Odds ratio > 1 =>ACTIVE is not better than PLACEBO Note: ACT 200 is not significant and has confidence interval > 1 => doubtful treatment
22 Simplification of Code in SAS Solution 1: LSMESTIMATE: combining features of both the LSMEANS and the ESTIMATE statement. In this case the confidence interval has to be stated explicitly in the options (cl) lsmestimate trt_no*avisitn "ACT 200 Vs PBO vis &vis" , "ACT 400 Vs PBO vis &vis" , "ALL ACT Vs PBO vis &vis" / e exp cl; Output: shows 3 lines instead of 6 but with less information: Least Squares Means Estimates Standard Effect Label Estimate Error z Value Pr > z TRT_NO*AVISITN ACT 200 Vs PBO vis Exponentiated Exponentiated Alpha Lower Upper Exponentiated Lower Upper
23 Generalization of Code 23 Results could also be obtained by linearly combining results from LSMEANS differences using slice option, details in paper. ods output SliceDiffs=diffs ; proc genmod data = ds; class... ; model... ; repeated... ; slice trt_no*avisitn / sliceby=avisitn diff oddsratio cl; run ;
24 Questions? 24
25 Thanks!
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