untitled
|
|
|
- さみら ながだき
- 5 years ago
- Views:
Transcription
1 SAS 2016 SAS Network Meta-Analysis
2 SAS Network Meta-Analysis homogeneity consistency Ranking Network Meta-Analysis(homogeneity) (consistency) Ranking 1
3 Network Meta-Analysis Network Meta-Analysis P Ranking SAS Bayesian Network Meta-Analysis 2
4 Network Meta-Analysis Meta-Analysis 2 Network Meta-Analysis Multiple Treatments Comparison Mixed Treatments Comparison 3 3
5 Network Meta-Analysis Network Meta-Analysis Meta-Analysis Network Meta-Analysis I. II. homogeneity consistency III. IV I II SAS 4
6 Network Meta-Analysis Network Meta-Analysis Network Meta-Analysis 3 multi-arm study Network A vs. B A vs. C B vs. C + A vs. D A B C D 5
7 Network Meta-Analysis Incomplete Block Design Sequence Incomplete Block Design IBD Period A B Patient 11,...,1n 1 2 B A Patient 21,...,2n 2 3 C B Patient 31,...,3n 3 4 B C Patient 41,...,4n 4 Network Meta-Analysis NMA 1: A vs. B Study 11,...,1n 1 IBD A B B C A C B A C GLM MIXED A 2: B vs. C Study 21,...,2n 2 B C 6
8 Network Meta-Analysis Incomplete Block Design Network Meta-Analysis NMA Incomplete Block Design IBD Senn et al IBD Sequence Sequence NMA IBD 1 NMA IBD NMA transitivity consistency 7
9 Network Meta-Analysis Network Meta-Analysis homogeneity consistency Ranking Bayesian Network Meta-Analysis B Treatment Mean Diff. 95% CI A D A B C D [-1.94; -1.57] [-0.61; -0.19] [-1.15; -0.74] C
10 Network Meta-Analysis homogeneity heterogeneity transitivity similarity Network Meta-Analysis Network Meta-Analysis effect modifier consistency Network Meta-Analysis consistency inconsistency 9 Caldwell (2014) Krahn (2013)
11 Network Meta-Analysis Network Meta-Analysis P Ranking SAS Bayesian Network Meta-Analysis 10
12 Senn2013 Senn et al. (2013) Senn et al. (2013) multi-arm study 1 10 acarbose benfluorex metformin miglitol pioglitazone rosiglitazone sitagliptin sulfonylurea alone vildagliptin placebo HbA1c % Senn2013 STUDLAB TREAT1 1 TREAT2 2 TE HbA1c % TREAT1 TREAT2 SETE TE 11
13 Senn2013 metf 1 benf migl 1 1 acar piog vild plac 2 1 sulf 6 rosi 1 sita multi-arm Willms
14 Senn2013 TE SETE TREAT1 TREAT2 STUDLAB metf sulf Alex rosi plac Baksi acar plac Costa rosi plac Davidson metf plac DeFronzo piog rosi Derosa vild plac Garber metf plac Gonzalez-Ortiz piog metf Hanefeld sita plac Hermansen migl plac Johnston migl plac Johnston1998a migl plac Johnston1998b rosi plac Kerenyi rosi metf Kim piog plac Kipnes metf plac Lewin benf plac Moulin acar sulf Oyama rosi plac Rosenstock benf plac Stucci rosi sulf Vongthavaravat acar metf Willms metf plac Willms acar plac Willms rosi plac Wolffenbuttel rosi metf Yang rosi plac Zhu
15 Senn2013 ID TE SETE TREAT1 TREAT2 STUDLAB s d metf sulf Alex acar plac Costa metf plac DeFronzo metf plac Gonzalez-Ortiz acar metf Willms metf plac Willms acar plac Willms acar metf sulf Senn metf vs. plac 3 multi-arm 1 3 ID=5 plac 14
16 Network Meta-Analysis, Cov, diag,,,,, plac 1,,5 1,,4 metf acar sulf V 1 V 4 V 5 Y 1 Y 4 Y 5 Willms metf acar plac M A P ID=6 SETE Var(M-P) = Var(M) + Var(P) - 2Cov(M,P) ID=5 ID=7 Cov(M-P, A-P) = Cov(M,A) Cov(M,P) Cov(P,A) +Var(P) 15
17 Network Meta-Analysis 1-step Network Estimate: 1.64, 0.88, step metf acar sulf, Cov Direct Estimate:,,,, Cov, diag,,, Network Estimate: 1.64, 0.88, 1.27 X 1 X 3 X 4 16
18 Network Meta-Analysis,Cov,,Cov 1,,5 1,,4 DerSimonian and Laird Cov 17 multi-arm τ /2 ID=5(acar vs. metf)
19 homogeneity consistency heterogeneity Within-designs Q statistic, inconsistency Between-designs Q statistic, / heterogeneity
20 P Ranking Cov( ) Ranking metf metformin P 1. metf p,,,, 2. P : 1, metf metformin P Ranking P P Bayesian Network Meta-Analysis SUCRA Surface Under the Cumulative RAnking 19 Rucker G and Schwarzer G (2015)
21 Network Meta-Analysis proc iml ; y = {-0.37, -0.8, -1.9, -0.4, -1.2, -1} ; X = {1 0-1, 0 1 0, 1 0 0, 1 0 0, 1 0 0, 0 1 0} ; V = { , , , , , } ; theta_net = inv(t(x) * inv(v) * X) * t(x) * inv(v) *y ; v_theta_net = inv(t(x) * inv(v) * X) ; result1 = theta_net (theta_net-probit(0.975)#sqrt(vecdiag(v_theta_net))) (theta_net+probit(0.975)#sqrt(vecdiag(v_theta_net))) ; theta_dir = j(5, 1, 0) ; Va = j(5, 5, 0) ; Xa = X[{ },] ; theta_dir[1,1] = sum(inv(v[1,1]) * y[1]) # V[1,1] ; Va[1,1] = V[1,1] ; theta_dir[2,1] = sum(inv(v[2,2]) * y[2]) # V[2,2] ; Va[2,2] = V[2,2] ; theta_dir[3,1] = sum(inv(v[{3 4},{3,4}]) * y[{3 4}]) / sum(vecdiag(inv(v[{3 4},{3 4}]))) ; Va[3,3] = 1/sum(vecdiag(inv(V[{3 4},{3 4}]))) ; theta_dir[{4 5},1] = y[{5 6}] ; Va[{4 5},{4 5}] = V[{5 6},{5 6}] ; theta_net_a = inv(t(xa) * inv(va) * Xa) * t(xa) * inv(va) * theta_dir ; v_theta_net_a = inv(t(xa) * inv(va) * Xa) ; result2 = theta_net_a (theta_net_a-probit(0.975)#sqrt(vecdiag(v_theta_net_a))) (theta_net_a+probit(0.975)#sqrt(vecdiag(v_theta_net_a))) ; 20
22 Network Meta-Analysis Q_net = t(y - X * theta_net) * inv(v) * (y-x * theta_net) ; Q_inc = t(theta_dir - Xa * theta_net_a) * inv(va) * (theta_dir - Xa * theta_net_a) ; Q_het = Q_net - Q_inc ; CC = -1#i(3); CC[,1] = 1; E = CC * theta_net; z = j(1,3,0) ; do i=1 to 3 ; c = CC[i,] ; z[i] = sqrt(t(c * theta_net) * inv(c * v_theta_net * t(c)) * (c * theta_net)) ; if (E[i] < 0) then z[i] = -1*z[i] ; end ; p_metf = 1-(probnorm(z)) ; pscore_metf = p_metf[:] ; title "Treatment estimate (Fixed effect model)" ; print result1 result2, Q_net Q_het Q_inc pscore_metf ; quit ; result1 1-step 1 95%2 3 result2 2-step result1 Q_net Q_het Q_inc pscore_metf metf metformin P 21
23 inconsistency Detaching a single design inconsistency Krahn 2013 Detaching a single design, 1, 0, Cov 1 0 detach, 22
24 inconsistency Detaching a single design,,,,,,,,,,,, 0,,,,, 0 inconsistency, 0 inconsistency 23
25 inconsistency network estimate plac:metf_ plac:acar:metf plac:acar plac:metf plac:acar_ plac:acar:metf plac:metf_ plac:acar:metf direct estimate plac:acar plac:metf plac:acar_ plac:acar:metf (inconsistency) direct estimate network estimate Network estimate 24
26 inconsistency plac:metf_ plac:acar:metf Detach plac:acar plac:metf plac:acar_ plac:acar:metf Detach plac:metf_ plac:acar:metf plac:acar plac:metf plac:acar_ plac:acar:metf = inconsistent evidence = supportive evidence 25
27 inconsistency Detach plac:metf_ plac:acar:metf plac:acar plac:metf plac:acar_ plac:acar:metf plac:metf_ plac:acar:metf Detach plac:acar plac:metf plac:acar_ plac:acar:metf multi-arm metf vs. plac inconsistency metf vs. plac network estimate direct estimate metf vs. plac 3 2-arm 2 metf vs. plac 3 inconsistency 26
28 Network Meta-Analysis Network Meta-Analysis P Ranking SAS Bayesian Network Meta-Analysis 27
29 iml iml Network Meta-Analysis ranking 500 Q 2 3 R netmeta SAS 28
30 Network Meta-Analysis SAS 1. R + C: temp 2. %MYNETMETA R Backup Slides R install.packages("netmeta", dep=t) %MYNETMETA Network Meta-Analysis SAS R output.pdf %MYNETMETA 29
31 Network Meta-Analysis SAS %macro MYNETMETA(dataset =, sm = MD, level = 0.95, reference =, seq =, small = good, path = C:/temp) ; dataset TE SETE TREAT1 TREAT2 STUDLAB sm TE RD Risk Difference RR Risk Ratio OR Odds Ratio MD Mean Difference SMD Standardized Mean Difference IRR Incidence Rate Ratio IRD Incidence Rate Difference level reference reference=plac seq small good bad path output.pdf 30
32 Network Meta-Analysis data Senn2013_char ; length STUDLAB $100. ; input TE SETE TREAT1 $ TREAT2 $ STUDLAB $ ; cards ; metf plac DeFronzo metf plac Lewin metf acar Willms rosi plac Davidson rosi plac Wolffenbuttel piog plac Kipnes rosi plac Kerenyi piog metf Hanefeld piog rosi Derosa rosi plac Baksi rosi plac Rosenstock rosi plac Zhu rosi metf Yang rosi sulf Vongthavaravat acar sulf Oyama acar plac Costa sita plac Hermansen vild plac Garber metf sulf Alex migl plac Johnston migl plac Johnston1998a rosi metf Kim migl plac Johnston1998b metf plac Gonzalez-Ortiz benf plac Stucci benf plac Moulin metf plac Willms acar plac Willms1999 ; run ;
33 Network Meta-Analysis %MYNETMETA(dataset = Senn2013_char, sm = MD, level = 0.95) ; dataset Senn2013_char sm MD Mean Difference level 95% output.pdf TE_FIXED TE_FIXED_LCL TE_FIXED_UCL TE_RANDOM TE_RANDOM_LCL TE_RANDOM_UCL Q1_STATISTICS Q homogeneity / consistency Q1_WITHINDESIGNS Design-specific decomposition of within-designs Q statistic Q1_BETWEENDESIGNS Between-designs Q statistic after detaching of single designs Q1_XXX 3 Q2_STATISTICS Q2_WITHINDESIGNS Q2_BETWEENDESIGNS RANK_FIXED RANK_RANDOM Network Ranking 32
34 Network Meta-Analysis %MYNETMETA(dataset = Senn2013_char, sm = MD, level = 0.95, reference = plac, seq = %str('plac','acar','benf','metf', 'migl','piog','rosi','sita', 'sulf', 'vild')) ; reference reference=plac seq 33
35 Network Meta-Analysis PDF metf:sulf rosi:sulf metf:piog plac:piog plac:rosi plac:metf metf:rosi piog:rosi plac:acar_plac:acar:metf plac:metf_plac:acar:metf acar:sulf plac:acar 1 Forest Plot 3 34 metf:sulf rosi:sulf metf:piog plac:piog plac:rosi plac:metf metf:rosi piog:rosi plac:acar_plac:acar:metf plac:metf_plac:acar:metf acar:sulf plac:acar Fixed Effect Model inconsistency multi-arm Willms1999
36 Network Meta-Analysis PDF acar:sulf plac:acar_plac:acar:metf plac:metf plac:acar metf:rosi piog:rosi plac:metf_plac:acar:metf plac:rosi metf:piog plac:piog metf:sulf rosi:sulf 2 Forest Plot 4 acar:sulf plac:acar_plac:acar:metf plac:metf plac:acar metf:rosi piog:rosi plac:metf_plac:acar:metf plac:rosi metf:piog plac:piog metf:sulf rosi:sulf inconsistency 35 Random Effect Model multi-arm Willms1999
37 Network Meta-Analysis TE_FIXED Q1_STATISTICS Q homogeneity / consistency Q1_WITHINDESIGNS Within-designs Q statistic RANK_FIXED Network P Q1_BETWEENDESIGNS Between-designs Q statistic after detaching of single designs 36
38 Network Meta-Analysis TE_RANDOM Q2_STATISTICS Q homogeneity / consistency Q2_WITHINDESIGNS Within-designs Q statistic RANK_RANDOM Network P Q2_BETWEENDESIGNS Between-designs Q statistic after detaching of single designs 37
39 Arm-based Contrast-based STUDY Group1 TE1 sete1 Group2 TE2 sete2 Group3 TE3 sete TE sete treat1 treat2 studlab contrast-based format arm-based format 38 Dias et. al. (2013) Parkinson
40 Arm-based Contrast-based %MYCONVERT(dataset =, studlab =, treat =, event =, n =, mean =, sd =, TE =, sete =, time =, sm =, path = C:/temp) ; dataset studlab treat event n mean sd TE sete TE time sm path 39
41 Arm-based Contrast-based %MYCONVERT(dataset = Parkinson, studlab = STUDY, treat = %str(group1, Group2, Group3), TE = %str(te1, TE2, TE3), sete = %str(sete1, sete2, sete3)) ; dataset studlab treat 1 2 TE sete %MYCONVERT Parkinson Parkinson_contrast 40 treat n mean sd
42 Arm-based Contrast-based 2 %MYCONVERT(dataset = MYDATA, treat = %str(treat1, treat2, treat3), event = %str(event1, event2, event3), n = %str(n1, n2, n3), sm=rr) ; %MYNETMETA(dataset = MYDATA_contrast, sm=rr) ; 2 OR SAS %str() %MYCONVERT(dataset = MYDATA, treat = %str(treat1, treat2, treat3), event = %str(event1, event2, event3), n = %str(n1, n2, n3), sm=%str(or)) ; %MYNETMETA(dataset = MYDATA_contrast, sm=%str(or)) ; 2 treat event n 41 sm=rd
43 Arm-based Contrast-based %MYCONVERT(dataset = MYDATA, studlab=id, treat = %str(treat1, treat2, treat3), time = %str(years1, years2, years3), event = %str(d1, d2, d3), sm=ird) ; %MYNETMETA(dataset = MYDATA_contrast, sm=ird) ; %MYCONVERT(dataset = MYDATA, studlab=id, treat = %str(treat1, treat2, treat3), time = %str(years1, years2, years3), event = %str(d1, d2, d3), sm=irr) ; %MYNETMETA(dataset = MYDATA_contrast, sm=irr) ; treat time event 42
44 Network Meta-Analysis Network Meta-Analysis P Ranking SAS Bayesian Network Meta-Analysis 43
45 Bayesian Network Meta-Analysis SAS Bayesian Network Meta-Analysis 2014 Bayesian Network Meta-Analysis Network Meta-Analysis ~ 0, ,,9 : acarbose placebo : vildagliptin placebo : 0 placebo 2-arm study: ~,, 1,, 10 ; : 3-arm study: ~, /2 /2 44
46 Bayesian Network Meta-Analysis Senn mcmc studlab TE1 TE2 TE3 sete1 sete2 sete3 treat1_1 treat1_2 treat1_3 treat2_1 treat2_2 treat2_3 v1 v2 v
47 Bayesian Network Meta-Analysis ods graphics on ; proc mcmc data=senn nbi=5000 nmc= thin=10 seed=777 missing=ac diagnostics=all plots=all monitor=(theta) ; 46 array te[2] te1 te2 ; array theta[9] ; array s[2,2] ; array mu[2] mu1 mu2 ; parms theta: 0 ; prior theta: ~ normal(0,var=10000) ; if studlab=3 then do ; do i=1 to 9 ; if treat1_1=i then mu1_1=theta[i] ; if treat2_1=i then mu2_1=theta[i] ; if treat1_2=i then mu1_2=theta[i] ; if treat2_2=i then mu2_2=theta[i] ; end ; if treat1_1=10 then mu1_1=0 ; if treat2_1=10 then mu2_1=0 ; if treat1_2=10 then mu1_2=0 ; if treat2_2=10 then mu2_2=0 ; mu[1]=mu1_1-mu2_1 ; mu[2]=mu1_2-mu2_2 ; s[1,1]=v1 ; s[2,2]=v2 ; s[1,2]=(v1+v2-v3)/2 ; s[2,1]=s[1,2] ; ll=lpdfmvn(te,mu,s) ; end ;
![Bayesian Network Meta-Analysis else do ; do i=1 to 9 ; if treat1_1=i then mu1_1=theta[i] ; if treat2_1=i then mu2_1=theta[i] ; end ; if treat1_1=10 then](/docs-images/94/119054729/images/48-5.jpg "mu1_1=0 ; if treat2_1=10 then mu2_1=0 ; mu[1]=mu1_1-mu2_1 ; ll=lpdfnorm(te[1],mu[1],sqrt(v1)) ; end ; model general(ll) ; run ; ods graphics off ; θ 1 θ 2 θ 3")
48 Bayesian Network Meta-Analysis else do ; do i=1 to 9 ; if treat1_1=i then mu1_1=theta[i] ; if treat2_1=i then mu2_1=theta[i] ; end ; if treat1_1=10 then mu1_1=0 ; if treat2_1=10 then mu2_1=0 ; mu[1]=mu1_1-mu2_1 ; ll=lpdfnorm(te[1],mu[1],sqrt(v1)) ; end ; model general(ll) ; run ; ods graphics off ; θ 1 θ 2 θ 3 θ 9 47
49 Bayesian Network Meta-Analysis Bayesian Network Meta-Analysis 48
50 Bayesian Network Meta-Analysis ~ 0, ,,9 : acarbose placebo : vildagliptin placebo : 0 placebo ~ igamma 1, arm study: ~,, 1,,10 ; : 3-arm study: ~, /2 1 1/2 /2 1/2 1 49
51 Bayesian Network Meta-Analysis ods graphics on ; proc mcmc data=senn nbi=10000 nmc= thin=50 seed=777 missing=ac diagnostics=all plots=all stats(percent=( ))=all monitor=(theta var_h sd) ; array te[2] te1 te2 ; array theta[9] ; array s[2,2] ; array g[2,2] ; array mu[2] mu1 mu2 ; array delta[2] delta1 delta2 ; parms theta: 0 ; parms var_h 1; prior theta: ~ normal(0,var=10000) ; prior var_h ~ igamma(1,scale= ) ; if studlab=3 then do ; do i=1 to 9 ; if treat1_1=i then mu1_1=theta[i] ; if treat2_1=i then mu2_1=theta[i] ; if treat1_2=i then mu1_2=theta[i] ; if treat2_2=i then mu2_2=theta[i] ; end ; if treat1_1=10 then mu1_1=0 ; if treat2_1=10 then mu2_1=0 ; if treat1_2=10 then mu1_2=0 ; if treat2_2=10 then mu2_2=0 ; 50
52 Bayesian Network Meta-Analysis mu[1]=mu1_1-mu2_1 ; mu[2]=mu1_2-mu2_2 ; s[1,1]=v1 ; s[2,2]=v2 ; s[1,2]=(v1+v2-v3)/2 ; s[2,1]=s[1,2] ; g[1,1]=var_h ; g[2,2]=g[1,1] ; g[1,2]=var_h/2 ; g[2,1]=g[1,2] ; random delta ~ mvn(mu,g) subject=_obs_ ; ll=lpdfmvn(te,delta,s) ; end ; else do ; do i=1 to 9 ; if treat1_1=i then mu1_1=theta[i] ; if treat2_1=i then mu2_1=theta[i] ; end ; if treat1_1=10 then mu1_1=0 ; if treat2_1=10 then mu2_1=0 ; mu[1]=mu1_1-mu2_1 ; vt=sqrt(v1) ; random delta3 ~ normal(mu[1],v=var_h) subject=_obs_ ; ll=lpdfnorm(te[1],delta3,vt) ; end ; model general(ll) ; sd=sqrt(var_h) ; run ; ods graphics off ; 51
53 Bayesian Network Meta-Analysis Bayesian Network Meta-Analysis 52
54 Network Meta-Analysis Network Meta-Analysis P Ranking SAS Bayesian Network Meta-Analysis 53
55 Network Meta-Analysis Network Meta-Analysis SAS homogeneity consistency Ranking Bayesian Network Meta-Analysis 54
56 Caldwell DM (2014) An overview of conducting systematic reviews with network meta-analysis. Systematic Reviews, 3:109. Dias S, Sutton AJ, Ades AE and Welton NJ (2013) Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Medical Decision Making, 33, Higgins JPT, Jackson D, Barrett JK, Lu G, et. al. (2012) Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods, 3(2), pages Hutton B, Salanti G, Caldwell DM, Chaimani A, et. al. (2015) The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Annals of Internal Medicine, 162(11): Krahn U, Binder H and Konig J (2013) A graphical tool for locating inconsistency in network meta analyses. BMC Medical Research Methodology, 13:35. Mills EJ, Thorlund K and Ioannidis JP (2013) Demystifying trial networks and network meta-analysis. BMJ. May14; 346:f2914. DerSimonian R and Laird N (1986) Meta-analysis in clinical trials. Controlled Clinical Trials 7:
57 Rucker G, Schwarzer G, Krahn U and Konig J (2014) Netmeta: network meta-analysis with R. R package (version 0.8-0). License GPL-2+ License GPL-2+ Rucker G and Schwarzer G (2015) Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Medical Research Methodology, 15:58. Senn S, Gavini F, Magrez D, and Scheen A (2013) Issues in performing a network meta-analysis. Statistical Methods in Medical Research, 22 (2), ,, (2014). SAS 56
58 2016/7/20
59 Backup Files 2016/7/20
60 Q homogeneity consistency
61 Windows R CRAN R R exe 60
62 Windows R OK (N) > 61
63 Windows R 1. Message translations 2. PC SAS 64-bit 64-bit Files 32-bit Files 32-bit Files (N) > 62
64 Windows R SDI 63
65 Windows R (N) > 64
66 Windows R R or 65
67 Windows R R R install.packages("netmeta", dep=t) Japan (Tokyo) OK R 66
68 Windows R C temp R R.exe C: Program Files R R bin i386 %MYNETMETA %macro MYNETMETA(dataset =, sm = MD, level = 0.95, reference =, seq =, small = good, path = C:/temp) ; options noxwait xsync ; %let Rexepath='C: Program Files R R bin i386 R.exe' ; 67
Network Meta-Analysis
SAS ユーザー総会 2016 SAS での Network Meta-Analysis の実施例 ~ 頻度論に基づくアプローチ ~ 武田薬品工業株式会社日本開発センター生物統計室舟尾暢男 黒田晋吾 要旨 SAS による Network Meta-Analysis の実施方法として 均質性 (homogeneity) や一致性 (consistency) の評価 各薬剤の Ranking の算出方法を含めて紹介する
untitled
R Network Meta-Analysis R Network Meta-Analysis homogeneity consistency Ranking Network Meta-Analysis(homogeneity) (consistency) Ranking 1 Network Meta-Analysis Network Meta-Analysis P Ranking R Bayesian
Microsoft Word - StatsDirectMA Web ver. 2.0.doc
Web version. 2.0 15 May 2006 StatsDirect ver. 2.0 15 May 2006 2 2 2 Meta-Analysis for Beginners by using the StatsDirect ver. 2.0 15 May 2006 Yukari KAMIJIMA 1), Ataru IGARASHI 2), Kiichiro TSUTANI 2)
5 Armitage x 1,, x n y i = 10x i + 3 y i = log x i {x i } {y i } 1.2 n i i x ij i j y ij, z ij i j 2 1 y = a x + b ( cm) x ij (i j )
5 Armitage. x,, x n y i = 0x i + 3 y i = log x i x i y i.2 n i i x ij i j y ij, z ij i j 2 y = a x + b 2 2. ( cm) x ij (i j ) (i) x, x 2 σ 2 x,, σ 2 x,2 σ x,, σ x,2 t t x * (ii) (i) m y ij = x ij /00 y
2 H23 BioS (i) data d1; input group patno t sex censor; cards;
H BioS (i) data d1; input group patno t sex censor; cards; 0 1 0 0 0 0 1 0 1 1 0 4 4 0 1 0 5 5 1 1 0 6 5 1 1 0 7 10 1 0 0 8 15 0 1 0 9 15 0 1 0 10 4 1 0 0 11 4 1 0 1 1 5 1 0 1 1 7 0 1 1 14 8 1 0 1 15 8
H22 BioS (i) I treat1 II treat2 data d1; input group patno treat1 treat2; cards; ; run; I
H BioS (i) I treat II treat data d; input group patno treat treat; cards; 8 7 4 8 8 5 5 6 ; run; I II sum data d; set d; sum treat + treat; run; sum proc gplot data d; plot sum * group ; symbol c black
DATA Sample1 /**/ INPUT Price /* */ DATALINES
3180, 3599, 3280, 2980, 3500, 3099, 3200, 2980, 3380, 3780, 3199, 2979, 3680, 2780, 2950, 3180, 3200, 3100, 3780, 3200 DATA Sample1 /**/ INPUT Price @@ /* @@1 */ DATALINES 3180 3599 3280 2980 3500 3099
y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f() + f() + f(3) + f(4) () *4
Simpson H4 BioS. Simpson 3 3 0 x. β α (β α)3 (x α)(x β)dx = () * * x * * ɛ δ y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f()
Autumn 2005 1 9 13 14 16 16 DATA _null_; SET sashelp.class END=eof; FILE 'C: MyFiles class.txt'; /* */ PUT name sex age; IF eof THEN DO; FILE LOG; /* */ PUT '*** ' _n_ ' ***'; END; DATA _null_;
H22 BioS t (i) treat1 treat2 data d1; input patno treat1 treat2; cards; ; run; 1 (i) treat = 1 treat =
H BioS t (i) treat treat data d; input patno treat treat; cards; 3 8 7 4 8 8 5 5 6 3 ; run; (i) treat treat data d; input group patno period treat y; label group patno period ; cards; 3 8 3 7 4 8 4 8 5
スライド 1
Model-based meta-analysis を用いた経時の試験結果の統合 乙黒俊也日本たばこ産業株式会社 荒野俊平株式会社データフォーシーズ Combination of longitudinal results from different studies by model-based meta-analysis Toshiya Otoguro Japan Tobacco Inc. Shumpei
こんにちは由美子です
Prevalence (proportion) 1. 1991 23.8 2. 1960 52 85 2477 310 cross sectional study prevalence Time referent: prevalence 1985 40 45 prevalence 0.5 80 85 43 Time referent 1985 time referent Risk and Cumulative
Kaplan-Meierプロットに付加情報を追加するマクロの作成
Kaplan-Meier 1, 2,3 1 2 3 A SAS macro for extended Kaplan-Meier plots Kengo Nagashima 1, Yasunori Sato 2,3 1 Department of Parmaceutical Technochemistry, Josai University 2 School of Medicine, Chiba University
こんにちは由美子です
Sample size power calculation Sample Size Estimation AZTPIAIDS AIDSAZT AIDSPI AIDSRNA AZTPr (S A ) = π A, PIPr (S B ) = π B AIDS (sampling)(inference) π A, π B π A - π B = 0.20 PI 20 20AZT, PI 10 6 8 HIV-RNA
kubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib
kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit
EBM Reviews Ovid Ovid EBM Reviews ACP Journal Club Cochrane Database of Systematic Revie
EBM Reviews 2006 4 Ovid http://www.usaco.co.jp/products/ovid/ovidplatform.html Ovid EBM Reviews ACP Journal Club Cochrane Database of Systematic Reviews CDSR Database of Abstracts of Reviews of Effectiveness
<30332D D8CB492982D FC95E490E690B62D8FAC8FBC96BE90E690B62E706466>
A questionnaire survey on the use of placebo in clinical practice by nurses in Japan 1 1 2 2 1 4 1964 5, 6 1998 GCP 1 School of Nursing, Faculty of Medicine, Toho University 2Faculty of Medical Technology,
一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM
.. ( ) (2) GLMM kubo@ees.hokudai.ac.jp I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!
スライド 1
61 SAS SAS LOHAS 18 18 12 01 LOHAS ( ) ( ) LOHAS 29% 35% LOHAS LOHAS 18 5 20 60 GMO 500 Yes No Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 LOHAS Q25 LOHAS / 2
2 The Bulletin of Meiji University of Integrative Medicine 3, Yamashita 10 11
1-122013 1 2 1 2 20 2,000 2009 12 1 2 1,362 68.1 2009 1 1 9.5 1 2.2 3.6 0.82.9 1.0 0.2 2 4 3 1 2 4 3 Key words acupuncture and moxibustion Treatment with acupuncture, moxibustion and Anma-Massage-Shiatsu
technews2012autumn
For Higher Customer Satisfaction, We Bridge the SAS System Between Customer s World. SPRING 2013 L Ext SAS 9.3 for Windows 02 SPRING 2013 1 SAS terms SPRING 2013 03 2 User account net localgroup Administrators
ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT;
Summer 2009 1 8 12 14 16 16 16 ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT; PROC SGPLOT DATA=SASHELP.PRDSALE; HBAR
Autumn 2007 1 5 8 12 14 14 15 %!SASROOT/sassetup SAS Installation Setup Welcome to SAS Setup, the program used to install and maintain your SAS software. SAS Setup guides you through a series of menus
GLM PROC GLM y = Xβ + ε y X β ε ε σ 2 E[ε] = 0 var[ε] = σ 2 I σ 2 0 σ 2 =... 0 σ 2 σ 2 I ε σ 2 y E[y] =Xβ var[y] =σ 2 I PROC GLM
![GLM PROC GLM y = Xβ + ε y X β ε ε σ 2 E[ε] = 0 var[ε] = σ 2 I σ 2 0 σ 2 =... 0 σ 2 σ 2 I ε σ 2 y E[y] =Xβ var[y] =σ 2 I PROC GLM](/thumbs/84/89087584.jpg "GLM PROC GLM y = Xβ + ε y X β ε ε σ 2 E[ε] = 0 var[ε] = σ 2 I σ 2 0 σ 2 =... 0 σ 2 σ 2 I ε σ 2 y E[y] =Xβ var[y] =σ 2 I PROC GLM")
PROC MIXED ( ) An Introdunction to PROC MIXED Junji Kishimoto SAS Institute Japan / Keio Univ. SFC / Univ. of Tokyo e-mail address: jpnjak@jpn.sas.com PROC MIXED PROC GLM PROC MIXED,,,, 1 1.1 PROC MIXED
: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
4703ALL01
473201010 *** (1999) 16 2017 20023 2 1 2 1220 (1999a),(1999b) (1998), (2002) (2003)(1999)(2003) Conjoint Analysis Conjoint Analysis Willingness to PayStandard gamble Time trade-off Rating Scale Willingness
II) SIR SMR)
II) ) 6 55 95 95Confidence Interval; 95CI) confidence intervals 95 95 1 5 95CI.66 Mortality experience of cockpit crewmembers from Japan Airlines. Kaji M, Tango T, Asukata I, Tajima N, Yamamoto K, Yamamoto
An Interactive Visualization System of Human Network for Multi-User Hiroki Akehata 11N F
An Interactive Visualization System of Human Network for Multi-User Hiroki Akehata 11N8100002F 2013 3 ,.,.,.,,., (, )..,,,.,,.,, SPYSEE. SPYSEE,,., 2,,.,,.,,,,.,,,.,, Microsoft Microsoft PixelSense Samsung
ＪＭＰ V4 による生存時間分析
V4 1 SAS 2000.11.18 4 ( ) (Survival Time) 1 (Event) Start of Study Start of Observation Died Died Died Lost End Time Censor Died Died Censor Died Time Start of Study End Start of Observation Censor
Presentation Title Goes Here
SAS 9: (reprise) SAS Institute Japan Copyright 2004, SAS Institute Inc. All rights reserved. Greetings, SAS 9 SAS 9.1.3 Copyright 2004, SAS Institute Inc. All rights reserved. 2 Informations of SAS 9 SAS
untitled
72 22 30 1985 Conductance Vessels flow Vessels< - Shear stress conductance vessel Exercise and Adenosine SPECT in the Patient with Angina SPECT,PET ACC/AHA/ASNC Guideline 2003 Dicarli JNM 2007;48:783
/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
4.9 Hausman Test Time Fixed Effects Model vs Time Random Effects Model Two-way Fixed Effects Model
1 EViews 5 2007 7 11 2010 5 17 1 ( ) 3 1.1........................................... 4 1.2................................... 9 2 11 3 14 3.1 Pooled OLS.............................................. 14
食道がん化学放射線療法後のsalvage手術
2006 2 17 52 Daly JM, et al. J Am Coll Surg 2000;190:562-573 Esophageal Cancer: Results of an American College of Surgeons Patient Care Evaluation Study Daly JM, et al. J Am Coll Surg 2000;190:562-573
1) Lysozyme and Viral Infections, 2nd Intern- Symposium on Fleming's Lysozyme, ational Milano, Apr. 1961. 2) Ermol'eva, Z.V. et al.: Experimental study and clinical application of lysozyme. Fed. Proc.
EAMS 2014
2014 2 10 RCT EAMS 2014 2015. 3. 31 Evidence Reports of Anma-Massage-Shiatsu: 2 Meta-Analisys and 10 Randomized Controlled Trials of Japan 31 Mar 2015 CONTENTS 1. (prologue).. 1 2. (steps for development
untitled
Summer 2008 1 7 12 14 16 16 16 SAS Academic News B-8 4 B-9 6 B-11 7 B-15 10 DATA _NULL_; dlm=","; char1="" char2="" char3="15" char4="a",,15,a results=catx(dlm, OF char1-char4); PUT results; DATA
1) University Group Diabetes Program: A study of hypoglycemic agents on vascular complica- in patients with adult-onset tions diabetes. I. Design, methods and baseline results. Diabetes 19 (suppl. 2):
kubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or
kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4
特 集 conditionally essential amino acid, グルタミンジペプチド (Glutamine dipeptide), 多臓器不全 Ⅰ はじめに sepsis RE- DOXS Ⅱ 重症患
143 特 集 conditionally essential amino acid, グルタミンジペプチド (Glutamine dipeptide), 多臓器不全 Ⅰ はじめに 1980 2013 sepsis RE- 173 8605 2 11 1 Ⅱ 重症患者における生体内でのグルタミンの役割 1950 Eagle 1 1970 2 1/3 40 50 144 30 conditionally
PROC OPTIONS; NOTE: XXXXXXXXSASV8.2 SASV9.1 SASV9.1 LIBNAME source ""; LIBNAME target V9 ""; PROC MIGRATE IN=source OUT=target ; RUN ; LIBNAME v8lib V8 "d: saslib v8lib"; LIBNAME v9lib V9 "d: saslib
untitled
18 1 2,000,000 2,000,000 2007 2 2 2008 3 31 (1) 6 JCOSSAR 2007pp.57-642007.6. LCC (1) (2) 2 10mm 1020 14 12 10 8 6 4 40,50,60 2 0 1998 27.5 1995 1960 40 1) 2) 3) LCC LCC LCC 1 1) Vol.42No.5pp.29-322004.5.
層化
(confounding) Confounding Exposure? Outcome Confounders adjustment (confounders) Not Confounding? 2 exposure 3rd factor 3rd factor NEJM 2000; 342: 1930-6. (adjustment) univariate or crude relative risk
44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2)
(1) I 44 II 45 III 47 IV 52 44 4 I (1) ( ) 1945 8 9 (10 15 ) ( 17 ) ( 3 1 ) (2) 45 II 1 (3) 511 ( 451 1 ) ( ) 365 1 2 512 1 2 365 1 2 363 2 ( ) 3 ( ) ( 451 2 ( 314 1 ) ( 339 1 4 ) 337 2 3 ) 363 (4) 46
i ii i iii iv 1 3 3 10 14 17 17 18 22 23 28 29 31 36 37 39 40 43 48 59 70 75 75 77 90 95 102 107 109 110 118 125 128 130 132 134 48 43 43 51 52 61 61 64 62 124 70 58 3 10 17 29 78 82 85 102 95 109 iii
1 I EViews View Proc Freeze
EViews 2017 9 6 1 I EViews 4 1 5 2 10 3 13 4 16 4.1 View.......................................... 17 4.2 Proc.......................................... 22 4.3 Freeze & Name....................................
生命倫理100_資料4-7
26 27 Single cell clones picked from swaps12p36 (18) Single cell clones picked from swaps12p18 (24) 1.3% (p0) 1.1% (p58) 28 29 Outline 30 31 32 33 34 Columbia, The New York Stem Cell Foundation Oocyte
Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52: ] "GET /url/url2/page2.htm HTTP/1.1" "http://www.domain.co.jp/url/url2/page1.htm" "(compatibl
![Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52: ] GET /url/url2/page2.htm HTTP/1.1 http://www.domain.co.jp/url/url2/page1.htm (compatibl](/thumbs/82/86651881.jpg "Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52: ] GET /url/url2/page2.htm HTTP/1.1 http://www.domain.co.jp/url/url2/page1.htm (compatibl")
Web Web-Site Analytics Fukuoka Financial Group, Inc. Mahiru Sunaga SAS Institute Japan Ltd. Kiyoshi Murakami (Combind log format) Apache Web 2 1 Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52:55 +0900] "GET
,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i
![,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i](/thumbs/87/95421301.jpg ",, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i")
Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,
DATA test; /** **/ INPUT score DATALINES; ; PROC MEANS DATA=test; /** DATA= **/ VAR sc
70 80 43 63 20 71 77 31 24 21 DATA test; /** **/ INPUT score @@; DATALINES; 70 80 43 63 20 71 77 31 24 21 ; PROC MEANS DATA=test; /** DATA= **/ VAR score; /** **/ RUN ; MEANS : score N ------------------------------------------------------------
橡災害.PDF
1 2 3 4 5 6 7 8 9 10 11 12 2.1 2.2 2.2.1 13 2.2.2 2.2.3 14 2.3 2.3.1 2.3.2 all or nothing 2.4. 2.4.1 15 i) ii) iii) iv) 2.5 2.4.2 2.2 2.5 2.5.1 16 2.5.2 2.6 2.6.1 2.4 2.6.2 2.6.3 2.6.4 17 18 3.2.1 Hazard
7... 1... 34... 55... 86... 108 15 2003 1 2 3 1 7 pdf 2 8 1996 2-1 9010 5 247 362 4 7 5 4 11 7 22 4 29 45 5 59 6 61 4 63 10 2 820 3 1646 19 1944 1 2 4 11 3 11 22 4 1 340 2 20 3 13 4 450 1 2 3 4 2-2 29
init: /**/ call notify(., _get_widget_, graph1, graphid); return; graph1: /**/ title1=getnitemc(graphid, title1 ); /**/ call display( title.frame, tit
init: /**/ call notify(., _get_widget_, graph1, graphid); return; graph1: /**/ title1=getnitemc(graphid, title1 ); /**/ call display( title.frame, title1); /**/ call notify( graph1, _set_title_, 1, title1);
Ⅰ. 緒言 Suzuki, et al., Ⅱ. 研究方法 1. 対象および方法 1 6 表 1 1, 調査票の内容 図
The Journal of the Japan Academy of Nursing Administration and Policies Vol 12, No 1, pp 515, 2008 原著 病棟勤務看護師の勤務状況とエラー ニアミスのリスク要因 Relationship between Risk Factors Related Medical Errors and Working Conditions
:... a
Title 発達障害と睡眠困難 に関する研究の動向と課題 ( fulltext ) Author(s) 柴田, 真緒 ; 髙橋, 智 Citation 東京学芸大学紀要. 総合教育科学系, 69(2): 107-121 Issue Date 2018-02-28 URL http://hdl.handle.net/2309/148914 Publisher 東京学芸大学学術情報委員会 Rights
日本統計学会誌, 第44巻, 第2号, 251頁-270頁
44, 2, 205 3 25 270 Multiple Comparison Procedures for Checking Differences among Sequence of Normal Means with Ordered Restriction Tsunehisa Imada Lee and Spurrier (995) Lee and Spurrier (995) (204) (2006)
第３回委員会（２月２０日）資料NO
ISO9001 ISO9001 E.Codman A.Donabedian Benefit, utility and risk A.Donabedian risk Output Output Output Adequacy Continuity / over time Efficacy (randomized controlled clinical trials) 7 Effectiveness
IPSJ SIG Technical Report Vol.2009-BIO-17 No /5/26 DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing
DNA 1 1 DNA DNA DNA DNA Correcting read errors on DNA sequences determined by Pyrosequencing Youhei Namiki 1 and Yutaka Akiyama 1 Pyrosequencing, one of the DNA sequencing technologies, allows us to determine
% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti One-sample test of pr
1 1. 2014 6 2014 6 10 10% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti 1029 0.35 0.40 One-sample test of proportion x: Number of obs = 1029 Variable Mean Std.
講義のーと : データ解析のための統計モデリング. 第５回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
untitled
The Japanese Association for Behavior Analysis Newsletter, No. 35 The Japanese Association for Behavior Analysis Newsletter, No. 35 The Japanese Association for Behavior Analysis Newsletter, No. 35 Table
23 Fig. 2: hwmodulev2 3. Reconfigurable HPC 3.1 hw/sw hw/sw hw/sw FPGA PC FPGA PC FPGA HPC FPGA FPGA hw/sw hw/sw hw- Module FPGA hwmodule hw/sw FPGA h
23 FPGA CUDA Performance Comparison of FPGA Array with CUDA on Poisson Equation (lijiang@sekine-lab.ei.tuat.ac.jp), (kazuki@sekine-lab.ei.tuat.ac.jp), (takahashi@sekine-lab.ei.tuat.ac.jp), (tamukoh@cc.tuat.ac.jp),
COE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate Scho
COE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate School of Economics and Institute of Economic Research
スライド 1
SAS による二項比率の差の非劣性検定の正確な方法について 武藤彬正宮島育哉榊原伊織株式会社タクミインフォメーションテクノロジー Eact method of non-inferiority test for two binomial proportions using SAS Akimasa Muto Ikuya Miyajima Iori Sakakibara Takumi Information
12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
Y X X Y1 X 2644 Y1 Y2 Y1 Y3 Y1 Y1 Y1 Y2 Y3 Y2 Y3 Y1 Y1 Y2 Y3 Y1 Y2 Y3 Y1 X Lexis X Y X X2 X3 X2 Y2 Y1 Y1
2 20 I II III 1 2 392 3 4 IV Makiko Noto / 3 3 1 1 5 52 1 2 2 2 3 9 11 5 31 1037 227 7 4 1 4 Y1 1 1 5 1965 2000 302 050 2012 autumn / No.393 051 2 5 Y1 9 9 4 X X Y1 X 2644 Y1 Y2 Y1 Y3 Y1 Y1 Y1 Y2 Y3 Y2
研究シリーズ第40号
165 PEN WPI CPI WAGE IIP Feige and Pearce 166 167 168 169 Vector Autoregression n (z) z z p p p zt = φ1zt 1 + φ2zt 2 + + φ pzt p + t Cov( 0 ε t, ε t j )= Σ for for j 0 j = 0 Cov( ε t, zt j ) = 0 j = >
医学教育研究とは 2011_10_14 v1
- - how to overview - overview - - - - - Harden RM (1986). Medical Education, 20: 522-531 PBL Description: What was done? 64% Justification: Did it work? 29% Clarification: Why or how did it work 7% Cook
0-表紙
openehr EHR openehr openehr CEN/ISO 13606 HL7 CDA Health Record An extract of an EHR Extract of EHR Multi document message Folder Composition (versions) Folder Composition (+/- versions) Document (Version
Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth
Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,
JHN Journal Club 手稲渓仁会病院
JHOSPITALIST network GUIDE-IT trial; The Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure multicentre randomised clinical trial 20171010 1 70 (EF 30%), (PCI), (HbA1c
Journal Club 2017/05/30 Tomohiro Matsumoto PGY-2
Journal Club 2017/05/30 Tomohiro Matsumoto PGY-2 本 の論 March 21, 2017 2 Introduc)on 3 : (CIN) 3 25% 44µmol/L(0.5mg/dL) CIN 4 European Society of Urogenital Radiology. ESUR guidelines on contrast media,
医薬品開発の意思決定における Bayesian Posterior Probability の適用例 ~ Random-Walk Metropolis vs. No-U-Turn Sampler ~ 作井将 清水康平 舟尾暢男 武田薬品工業株式会社日本開発センター生物統計室 Using Bayesi
医薬品開発の意思決定における Bayesian Posterior Probability の適用例 ~ Random-Walk Metropolis vs. No-U-Turn Sampler ~ 作井将 清水康平 舟尾暢男 武田薬品工業株式会社日本開発センター生物統計室 Using Bayesian Posterior Probability for Go/No-Go Decision Making
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,
X X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
untitled
19 CSPOR CRC/2009.8.8-9 2009 8 8 ( ) - CSPOR CRC SEMINAR- twatanab@oncoloplan.com http://www.oncoloplan.com - Toru Watanabe MD - 2 Phase I: Dose Finding trial Phase II : Efficacy and Safety trial Phase
本文.indd
CQ ACC/AHA PECO PatientExposure Comparison OutcomePECO low-density lipoprotein cholesterol LDL C cholesterol cholesterol intensive OR agressive CQ target LDL C PubMed Systematic Reviews Clinical Study
10_08.dvi
476 67 10 2011 pp. 476 481 * 43.72.+q 1. MOS Mean Opinion Score ITU-T P.835 [1] [2] [3] Subjective and objective quality evaluation of noisereduced speech. Takeshi Yamada, Shoji Makino and Nobuhiko Kitawaki
kubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :
kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)
mt_4.dvi
( ) 2006 1 PI 1 1 1.1................................. 1 1.2................................... 1 2 2 2.1...................................... 2 2.1.1.......................... 2 2.1.2..............................
時間割引：双曲割引と弱加法性
Discussion aper No. 666 June 2006 The Institute of Social and Economic Research Osaka University 6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan Time Discounting: Declining Impatience and Interval Effect
C/NC : committed/noncommitted
C/NC : committed/noncommitted 110 time post-icd implant 1) The Cardiac Arrhythmia Suppression Trial (CAST) Investigators Preliminary report : Effect of encainaide and flecainide on mortality in a
診療ガイドラインのカラクリ
EBM EBM 2-3 (evidence-based medicine: EBM) EBM 1 1 1 2 B burden belief bargain barrier A. JAMA EBMer JAMA EBM 3 EBM 3 EBM 3 3 B. peer review Field(IOM) Shaneyfelt ( Guidelines Qualities Assessment Questionnaire:
PROC PWENCODE IN=sastrust1 ; RUN ;
PROC PWENCODE IN=sastrust1 ; RUN ; 1 PROC PWENCODE IN="sastrust1" ; 2 RUN ; {sas001}c2fzdhj1c3qx /* */ LIBNAME audit 'Lev1 SASMain MetadataServer audit repos1'; /* ID */ PROC PRINT DATA=audit.person; VAR
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
R Commanderを用いたデータ解析
1 / 82 R Commander Kengo NAGASHIMA Laboratory of Biostatistics, Department of Parmaceutical Technochemistry, Josai University 2010 1 5 R R Commander 2 / 82 R, "The Comprehensive R Archive Network (CRAN)",
1999 02 03 Ver. 1.00 1999 07 09 Ver. 1.01 UID, 2003 10 01 Ver. 1.02 Storage SCU DICOM 3.0 Conformance Statement Ver.1.01 1999/07/09 1 0... 3 1... 3 1.1... 3 1.2... 3 1.3... 3 2... 4 2.1... 4 2.1.1... 4
2012_00表紙
02 Network Program 05 1 2 3 4 5 6 1 13 60 5,800 06 07 Program 10 12 Program 14 Program 18 20 Program 22 24 Program 25 26 27 28 29 30 31 32 Program 33 34 35 40 Program 41 42 43 44 45 46 Program
untitled
Quantitative Risk Assessment on the Public Health Impact of Pathogenic Vibrio parahaemolyticus in Raw Oyster 1 15 5 23 48 2 21 1 16 1 16 1 11 3 1 3 4 23 1 2 16 12 16 5 6 Hazard IdentificationExposure
VM-53PA1取扱説明書
VM-53PA1 VM-53PA1 VM-53 VM-53A VM-52 VM-52A VM-53PA1 VM-53PA1 VM-53A CF i ii VM-53 VM-53A VM-52 VM-52A CD-ROM iii VM-53PA1 Microsoft Windows 98SE operating system Microsoft Windows 2000 operating system
untitled
2 IMDRFInternational Medical Device Regulators Forum/SaMD WG/N12FINAL:2014 Title "Software as a Medical Device": Possible Framework for Risk Categorization and Corresponding Considerations Authoring
- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...
取 扱 説 明 書 - - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...9...11 - - - - - - - - - - - - - - - - -
i Armitage Q. Bonferroni 1 SAS ver9.1.3 version up 2 *1 *2 FWE *3 2.1 vs vs vs 2.2 5µg 10µg 20µg 5µg 10µg 20µg vs 5µg vs 10µg vs 20µg *1 *2 *3 FWE 1
i Armitage Q Boferroi SAS ver93 versio up * * FWE *3 vs vs vs 5µg 0µg 0µg 5µg 0µg 0µg vs 5µg vs 0µg vs 0µg * * *3 FWE 3 A B C D E (i A B C D E (ii A B C D E (iii A B C D E (iv A B C D A < B C D A < B
com.ibm.etools.egl.jsfsearch.tutorial.doc.ps
EGL JSF ii EGL JSF EGL JSF.. 1................. 1 1:.... 3 Web.......... 3........... 3........ 4......... 7 2:...... 7..... 7 SQL.... 8 JSF.... 10 Web.... 12......... 13 3: OR....... 14 OR... 14.15 OR.....
̤Äê
SNS 1, IT.,.,.,., SNS,,,..,,.,,,.,.,,. 2 1 6 1.1................................................ 6 1.2................................................ 6 1.3...............................................