3. general considerations for the analysis of case-control ... - IARC

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APPENDIX V SUBROUTINE SYMINVCA, N, C,W, NULLTY, IFAULT, NFAC) DIMENSION A(NFAC),C(NFAC),W(N) ALGORITHM AS7 J .R. STATIST.SOC .C .(1968) VOL. 17,N0.2 NROW=N I FAULT= 1 IF(NROW.LE.O)GOTO loo I FAlJLT=O CALL CHOLCA, NRCW, C, NLILLTY, I FAULT, MFAC) IF(IFAULT.NE.O)GOTO loo IUI\I=(NROW::(NROW+I I)/ 2 I ROW =NROW NDIAG=NN IF(C(NDIAG> .EQ .090 )GOTO 11 L=ND IAG DO 10 I=IRCW,NROW W(I )=C(L) L=L+l CON T I NUE ICOL=NROW JCOL=NN MD I AG=NN L=JCOL K= NROW IF(K .EQ. 1ROW)GOTOlZ x=x-w(K)::c(L) K=K-1 L=L-1 I F( L .GT .MD IAG)L=L-K+I GOT0 13 C(L)=X/W(IROW) IF( ICOL .EQ. IROW)GOTO 14 MD IAG=MD IAG -I COL ICOL=ICOL-1 JCOL=JCOL-1 GOT0 15 L=ND I AG DO 1 7 J= I ROW, IUROW C(L)=O .o L=L+J CONTINUE ND I AG=ND I AG - I ROW I RCWIROW-1 IF(IRCW.NE.O)GOTO 16 RE TLIRN END
PROGRAM STRAT SUBROUTIIVE CK)L(A,N, U, NULLTY, 'IFAULT, NFAC) DIMENS ION ACNFAC), UCNFAC) REAL ETA DATA ETA/0.000001/ ALGORITHM AS6 J.R.STATIS. SOC. C. (1968). VOL.17, N0.2 I FAULT= 1 IF(N.LE.O)GOTO 100 I FAULT=:! NI,ILLTY=O J= 1 K=O DO 10 ICOL=l,N L=O DO 11 IROW=l,ICOL K=K+ 1 W=A(K) M=J DO 12 I=l,IROW L=L+1 IF(1 .EQ.IROW-GOT0 13 w =W -U( L)::U(M) M=M+ 1 CONTINUE IF(IROW.EQ.ICOL)GOTO 14 :[F(u(L) .EQ.O .O)GOTO 21 u(K)=w/u(L) GOT0 11 U(K)=O .O CONTINUE I F(ABS(W) . LT .ABS(ETA::A(K))) GOTO 20 IF(w.LT.O .O)GOTO 100 U(K)=SQRT(W) GOT0 15 UCK)=O .O NULLTY=NULLTY+l J=J+I COL CONTINUE I FAULT=O RETURN END
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WORLD HEALTH ORGANIZATION INTERNATI
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N.E. Breslow & N. E. Day (1980) Sta
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CONTENTS Foreword .................
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PREFACE Twenty years have elapsed s
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ACKNOWLEDGEMENTS Since the initial
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LIST OF PARTICIPANTS AT IARC WORKSH
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1.1 The case-control study in cance
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INTRODUCTION 15 a specific disease
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History INTRODUCTION 17 In 1926 Lan
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INTRODUCTION 19 day human affairs c
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INTRODUCTION 21 cording to age, sex
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INTRODUCTION 1.5 Planning The case
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INTRODUCTION 25 determinants of sur
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INTRODUCTION 27 like exposed subjec
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INTRODUCTION 29 warrants careful co
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INTRODUCTION 31 strata, and formati
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INTRODUCTION 33 in which at least 1
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INTRODUCTION 35 analytical techniqu
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INTRODUCTION 37 ity; is it understo
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INTRODUCTION 39 Hutchison, G. B. (1
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2. FUNDAMENTAL MEASURES OF DISEASE
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MEASURES OF DISEASE 43 operate prio
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MEASURES OF DISEASE 4 5 change is r
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MEASURES OF DISEASE 4 7 veillance s
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MEASURES OF DISEASE 49 Fig. 2.3 Age
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MEASURES OF DISEASE t A (t) = 2 l(n
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MEASURES OF DISEASE Table 2.4 Cumul
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MEASURES OF DISEASE 55 exposed vers
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MEASURES OF DISEASE 57 Example: Fig
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MEASURES OF DISEASE 59 explore thes
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31 5 42.5 47.5 52.5 57.5 62.5 67.5
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MEASURES OF DISEASE 63 Fig. 2.8 Rat
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MEASURES OF DISEASE 65 Fig. 2.9 Age
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MEASURES OF DISEASE 67 Table 2.8 Jo
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2.7 Logical properties of the relat
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MEASURES OF DISEASE 71 Example: Sup
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MEASURES OF DISEASE 73 or similar b
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MEASURES OF DISEASE 75 categories i
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MEASURES OF DISEASE 77 RAR = AR2-AR
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MEASURES OF DISEASE 79 Court Brown,
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MEASURES OF DISEASE 8 1 Plk P2 k R
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CHAPTER I11 GENERAL CONSIDERATIONS
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86 BRESLOW & DAY 3.2 Criteria for a
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88 BRESLOW & DAY Fig. 3.1 Relative
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90 BRESLOW & DAY Considerations ext
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92 BRESLOW & DAY of this type can b
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94 BRESLOW & DAY decreases breast c
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96 BRESLOW & DAY E and those not ex
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98 BRESLOW & DAY The confounding ef
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BRESLOW & DAY Comparison of w* with
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Factor C+ Exposure E + - BRESLOW &
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104 BRESLOW & DAY there were limita
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106 BRESLOW & DAY to have a strong
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1 08 BRESLOW & DAY 3. If a factor i
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110 BRESLOW & DAY Table 3.6 Risk fo
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112 BRESLOW & DAY risk for some fac
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114 BRESLOW & DAY an additional cat
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116 BRESLOW & DAY Boice, J.D. & Mon
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118 BRESLOW & DAY Report of the Sur
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4. CLASSICAL METHODS OF ANALYSIS OF
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ANALYSIS OF GROUPED DATA Table 4.1
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ANALYSIS OF GROUPED DATA 125 such a
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Estimation of q ANALYSIS OF GROUPED
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ANALYSIS OF GROUPED DATA 129 for th
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ANALYSIS OF GROUPED DATA 131 analog
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ANALYSIS OF GROUPED DATA 133 Referr
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ANALYSIS OF GROUPED DATA 135 Halper
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ANALYSIS OF GROUPED DATA 137 are pa
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ANALYSIS OF GROUPED DATA 1.39 signi
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ANALYSIS OF GROUPED DATA 141 strata
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ANALYSIS OF GROLIPED DATA 143 appro
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ANALYSIS OF GROLIPED DATA 145 Table
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ANALYSIS OF GROUPED DATA Exposure l
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ANALYSIS OF GROUPED DATA 149 Nor wi
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ANALYSIS OF GROUPED DATA 151 Alcoho
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ANALYSIS OF GROUPED DATA 153 relati
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ANALYSIS OF GROUPED DATA 155 obtain
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ANALYSIS OF GROUPED DATA 157 Gart,
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ANALYSIS OF GROUPED DATA 159 Cov(x,
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CHAPTER V CLASSICAL METHODS OF ANAL
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164 BRESLOW & DAY 5.2 Matched pairs
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166 BRESLOW & DAY Approximate confi
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168 BRESLOW & DAY where 2.42 = F.,,
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170 BRESLOW & DAY The first and las
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BRESLOW & DAY This is a special cas
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ANALYSIS OF MATCHED DATA Case Expos
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176 BRESLOW & DAY or limits of 3.8-
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BRESLOW & DAY Table 5.3 Data layout
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180 BRESLOW & DAY Case : control ra
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182 BRESLOW & DAY = 11.82, from whi
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184 BRESLOW & DAY observed and expe
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BRESLOW & DAY Their solution, obtai
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188 BRESLOW & DAY Miettinen, 0. S.
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6. UNCONDITIONAL LOGISTIC REGRESSIO
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LOGISTIC REGRESSION FOR LARGE STRAT
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LOGISPIC REGRESSION FOR LARGE STRAT
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6.8 Regression adjustment for confo
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6.9 Analysis of continuous data LOG
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7. CONDITIONAL LOGISTIC REGRESSION
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LOGISTIC REGRESSION FOR MATCHED SET
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LOGIS-I-IC REGRESSION FOR MATCHED S
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Page 260 and 261: Table 7.7 Matched multivariate anal
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Page 264 and 265: Table 7.9 (contd) C. Dose, duration
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Page 272 and 273: Table 7.12 Coefficients (f standard
Page 278 and 279: APPENDIX I AGE (YRS) ALCOHOL (GM/DA
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Page 282 and 283: APPENDIX II YEAR OF DEATH AGE AT DE
Page 284 and 285: APPENDIX II YEAR OF DEATH AGE AT DE
Page 286 and 287: APPENDIX 111: MATCHED DATA FROM THE
Page 288 and 289: 292 APPENDIX Ill CASE OR CONTROL AG
Page 290 and 291: 294 APPENDIX Ill CASE OR CONTROL AG
Page 292 and 293: 296 APPENDIX Ill CASE OR CONTROL CA
Page 294 and 295: APPENDIX IV MAIN PROGRAM .C MASTER
Page 296 and 297: 300 APPENDIX IV 103 FORMAT(1H ,'NUM
Page 298 and 299: 302 APPENDIX IV C UPON CONVERGENCE
Page 300 and 301: APPENDIX IV MODEL WITH 2 VARIABLES:
Page 302 and 303: APPENDIX IV MODEL WITH 4 VARIABLES:
Page 304 and 305: APPENDIX V MAIN PROGRAM DIMENSION N
Page 306 and 307: 31 0 APPENDIX V C REFERENCES : C N.
Page 308 and 309: APPENDIX V CALL SYMINV~COVI,NP,COV,
Page 312 and 313: APPENDIX V SUBROUTINE TWIDL(X, Y, Z
Page 314 and 315: APPENDIX V MODEL WITH SINGLE VARIAB
Page 316 and 317: APPENDIX V MODEL WITH 3 VARIABLES:
Page 318 and 319: APPENDIX VI LISTING OF PROGRAM LOGO
Page 320 and 321: C C C C C C APPENDIX VI SUBPROGRAM
Page 322 and 323: APPENDIX VI SUBROUTINE CALC(NTAB,P,
Page 324 and 325: 328 APPENDIX VI SUBROUTINE MYSTCT,
Page 326 and 327: APPENDIX VI DOUBLE PRECISION FUNCTI
Page 328 and 329: 332 APPENDIX VI C C C C C C REDUCE
Page 330 and 331: MODEL WITH LINEAR EFFECT OF YEAR RE
Page 332 and 333: TABLE A 0 E 31 12 10.64 32 4 5.12 3
Page 334 and 335: 0 03 TABLE A B 0 E 0 E 9 1 6 7.06 6
Page 336 and 337: 340 SUBJECT INDEX Biological monito
Page 338 and 339: 342 SUBJECT INDEX Confounding risk
Page 340 and 341: 344 SUBJECT INDEX Grouped data anal
Page 342 and 343: 346 SUBJECT INDEX Matched case-cont
Page 344 and 345: 348 SUB.IECT INDEX Poisson variabil
Page 346: 350 SUB-IECT INDEX Tobacco consumpt
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