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Network meta-analysis in SAS Danish Society of Biopharmaceutical Statistics, Elsinore, May 27, 2014. David A. Scott MA MSc Senior Director, ICON Health Economics Visiting Fellow, SHTAC, University of Southampton. Network Meta-Analysis: Software. winBUGS / OpenBUGS /JAGS (DSU series)

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network meta analysis in sas danish society of biopharmaceutical statistics elsinore may 27 2014

Network meta-analysis in SASDanish Society of Biopharmaceutical Statistics, Elsinore, May 27, 2014

David A. Scott MA MSc

Senior Director, ICON Health Economics

Visiting Fellow, SHTAC, University of Southampton

network meta analysis software
Network Meta-Analysis: Software
  • winBUGS/OpenBUGS/JAGS (DSU series)
  • R e.g. rmeta, netmeta, mvmeta packages
  • Statamvmeta
  • SAS e.g. procglimmix, procmcmc
a brief history of nma in sas
A brief history of NMA in SAS
  • Lots of different procedures to implement NMA in SAS
    • proc mixed, proc mixed, procnlmixed,procgenmod, proc glimmix1-3
    • Frequentist techniques
    • Difficult to fit complex hierarchical models2
  • MCMC techniques
    • procgenmod (using Easy Bayes) -> procmcmc
    • SAS 9.2 (level 2M3), SAS 9.3 (sas stat 12)

1 Glenny AM et al, Health Technology Assessment 2005; 9(26)

2 Jones B et al, Pharmaceutical Statistics 2011; 10:523-31

3 Piepho HP et al. Biometrics 2012; 68:1269-77

potential barriers
Potential barriers
  • DSU series winBUGS-focused
  • SAS not yet used in UK reimbursement submissions
  • ERG limited experience of SAS
  • Limited published code/articles
  • Validation exercise
syntax load data
Syntax: load data

data smoking;

input Study Trt R N narm;

datalines;

1 2 11 78 3 #Mothersill 1988

1 3 12 85 3 #Mothersill 1988

1 4 29 170 3 #Mothersill 1988

  • 1 75 731 2 #Reid 1974

run;

syntax fixed effects
Syntax: fixed effects

procmcmc data=smoking nmc=20000 seed=246810;

random Studyeffect ~general(0) subject=Study init=(0);

random Treat ~general(0) subject=Treatment init=(0) zero="No contact" monitor=(Treat);

mu= Studyeffect + Treat;

P=1-(1/(1+exp(mu)));

model R ~ binomial(n=N, p=P);

run;

syntax random effects
Syntax: random effects

procmcmc data=smoking nbi=20000 nmc=200000 thin=10 seed=246810 monitor=(mysd) dic;

random Studyeffect ~normal(0, var=10000) subject=Study init=(0) ;

random Treat ~normal(0, var=10000) subject=Treatment init=(0) zero="No contact" monitor=(Treat);

parmsmysd 0.2;

prior mysd ~ uniform(0,1);

random RE ~ normal(0,sd=mysd/sqrt(2)) subject=_OBS_ init=(0);

mu= Studyeffect + Treat +RE;

P=1-(1/(1+exp(mu)));

model R ~ binomial(n=N, p=P);

run;

diagnostics
Diagnostics
  • Trace
  • Density
  • Autocorrelation
    • thin= option
  • DIC (relative model fit)
    • dic option
practical exercise 1
Practical exercise 1
  • Run the code as is
  • Compare results for each model
  • Amend the code to generate fewer MCMC samples, how many are sufficient? How much burn-in is needed? Is thinning necessary in the RE model?
  • Which model is the better fit, fixed or random effects?
  • Change the baseline from “no contact” to “self help”. Are the results consistent?
  • Try changing the priors to other vague priors1, does this affect results?

1 Lambert PC et al, Statistics in Medicine, 2005; 24:2401-28

syntax load data1
Syntax: load data

data scott;

input study trt baseline y SE;

datalines;

1 2 8.5 -1.08 0.12

1 3 8.5 -1.13 0.12

1 1 8.5 0.23 0.2

2 2 8.4 -1 0.1

;

run;

syntax fixed effects1
Syntax: fixed effects

procmcmc data=scott nmc=200000 nthin=20 seed=246810;

random Studyeffect ~general(0) subject=Study init=(0) ;

random Treat ~general(0) subject=Treatment init=(0) zero="Placebo" monitor=(Treat);

Mu= Studyeffect + Treat ;

model Y ~ normal(mean=Mu, var=SE*SE);

run;

syntax random effects1
Syntax: random effects

procmcmc data=scott nmc=200000 nthin=20 seed=246810 monitor=(mysd) outpost=outp7 dic;

random Studyeffect ~normal(0,var=10000) subject=Study init=(0) ;

random Treat ~normal(0,var=10000) subject=Treatment init=(0) zero="Placebo" monitor=(Treat);

parmsmysd 0.2;

prior mysd ~ uniform(0,1);

random RE ~normal(0,sd=mysd/sqrt(2)) subject=_OBS_ init=(0);

Mu= Studyeffect + Treat +RE;

model Y ~ normal(mean=Mu, sd=SE);

run;

syntax fixed effects meta regression
Syntax: fixed effects meta-regression

procmcmc data=scott nmc=200000 nthin=20 seed=246810;

random Studyeffect ~general(0) subject=Study init=(0) ;

random Treat ~general(0) subject=Treatment init=(0) zero="Placebo" monitor=(Treat);

parms hba1c 0;

prior hba1c ~normal(0,var=10000);

Mu= Studyeffect + Treat + baseline*hba1c;

model Y ~ normal(mean=Mu, var=SE*SE);

run;

practical exercise 2
Practical exercise 2
  • Run the code as is
  • Compare results for each model
  • Which model is the better fit, fixed or random effects, or meta-regression?
  • Amend the code to generate fewer MCMC samples, how many are sufficient? How much burn-in is needed? Is thinning necessary in the RE model?
  • Change the baseline from “Placebo” to “Insulin Glargine”. Are the results consistent?
  • Compare results to WinBUGS output
  • Try changing the priors to other vague priors1, does this affect results?

1 Lambert PC et al, Statistics in Medicine, 2005; 24:2401-28