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

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CONTENTS Foreword .............................................................. 5 Preface ................................................................ 7 Acknowledgements ...................................................... 9 Lists of Symbols ......................................................... 12 1 . Introduction ......................................................... 14 2 . Fundamental Measures of Disease Occurrence and Association ............. 42 3 . General Considerations for the Analysis of Case-Control Studies ........... 84 4 . Classical Methods of Analysis of Grouped Data .......................... 122 5 . Classical Methods of Analysis of Matched Data ........................... 162 6 . Unconditional Logistic Regression for Large Strata ....................... 192 7 . Conditional Logistic Regression for Matched Sets .......................... 248 Appendices ............................................................. 281
FOREWORD Epidemiological and biostatistical studies on cancer and other chronic diseases have expanded markedly since the 1950s. Moreover, as recognition of the role of environmental factors in human cancer has increased, there has been a need to develop more sophisticated approaches to identify potential etiological factors in populations living in a wide variety of environments and under very different socioeconomic conditions. Developnzents in many countries have required that appropriate governmental agencies establish regulations to control environmental cancer hazards. Such regulations may, however, have considerable social and economic impacts, which require that they be based on careful risk-benefit analyses. Epidemiological studies provide the only definitive information as to the degree of risk in man. Since malignant diseases are clearly of multifactorial origin, their investigation in man has become increasingly complex, and epidemiological and biostatistical studies on cancer require a correspondingly complex and rigorous methodology. Studies such as these are essential to the development of programmes of cancer control and prevention. Dr N.E. Breslow and Dr N.E. Day and their colleagues are to be commended on. this volume, which should prove of value not only to established workers but also to all who wish to become acquainted with the general principles of case-control studies, which are the basis of modern cancer epidemiology. John Higginson, M.D. Director, International Agency for Research on Cancer, Lyon, France
Page 2 and 3: WORLD HEALTH ORGANIZATION INTERNATI
Page 4 and 5: N.E. Breslow & N. E. Day (1980) Sta
Page 8 and 9: PREFACE Twenty years have elapsed s
Page 10 and 11: ACKNOWLEDGEMENTS Since the initial
Page 12 and 13: LIST OF PARTICIPANTS AT IARC WORKSH
Page 14 and 15: 1.1 The case-control study in cance
Page 16 and 17: INTRODUCTION 15 a specific disease
Page 18 and 19: History INTRODUCTION 17 In 1926 Lan
Page 20 and 21: INTRODUCTION 19 day human affairs c
Page 22 and 23: INTRODUCTION 21 cording to age, sex
Page 24 and 25: INTRODUCTION 1.5 Planning The case
Page 26 and 27: INTRODUCTION 25 determinants of sur
Page 28 and 29: INTRODUCTION 27 like exposed subjec
Page 30 and 31: INTRODUCTION 29 warrants careful co
Page 32 and 33: INTRODUCTION 31 strata, and formati
Page 34 and 35: INTRODUCTION 33 in which at least 1
Page 36 and 37: INTRODUCTION 35 analytical techniqu
Page 38 and 39: INTRODUCTION 37 ity; is it understo
Page 40 and 41: INTRODUCTION 39 Hutchison, G. B. (1
Page 42 and 43: 2. FUNDAMENTAL MEASURES OF DISEASE
Page 44 and 45: MEASURES OF DISEASE 43 operate prio
Page 46 and 47: MEASURES OF DISEASE 4 5 change is r
Page 48 and 49: MEASURES OF DISEASE 4 7 veillance s
Page 50 and 51: MEASURES OF DISEASE 49 Fig. 2.3 Age
Page 52 and 53: MEASURES OF DISEASE t A (t) = 2 l(n
Page 54 and 55: MEASURES OF DISEASE Table 2.4 Cumul
Page 56 and 57:
MEASURES OF DISEASE 55 exposed vers
Page 58 and 59:
MEASURES OF DISEASE 57 Example: Fig
Page 60 and 61:
MEASURES OF DISEASE 59 explore thes
Page 62 and 63:
31 5 42.5 47.5 52.5 57.5 62.5 67.5
Page 64 and 65:
MEASURES OF DISEASE 63 Fig. 2.8 Rat
Page 66 and 67:
MEASURES OF DISEASE 65 Fig. 2.9 Age
Page 68 and 69:
MEASURES OF DISEASE 67 Table 2.8 Jo
Page 70 and 71:
2.7 Logical properties of the relat
Page 72 and 73:
MEASURES OF DISEASE 71 Example: Sup
Page 74 and 75:
MEASURES OF DISEASE 73 or similar b
Page 76 and 77:
MEASURES OF DISEASE 75 categories i
Page 78 and 79:
MEASURES OF DISEASE 77 RAR = AR2-AR
Page 80 and 81:
MEASURES OF DISEASE 79 Court Brown,
Page 82 and 83:
MEASURES OF DISEASE 8 1 Plk P2 k R
Page 84 and 85:
CHAPTER I11 GENERAL CONSIDERATIONS
Page 86 and 87:
86 BRESLOW & DAY 3.2 Criteria for a
Page 88 and 89:
88 BRESLOW & DAY Fig. 3.1 Relative
Page 90 and 91:
90 BRESLOW & DAY Considerations ext
Page 92 and 93:
92 BRESLOW & DAY of this type can b
Page 94 and 95:
94 BRESLOW & DAY decreases breast c
Page 96 and 97:
96 BRESLOW & DAY E and those not ex
Page 98 and 99:
98 BRESLOW & DAY The confounding ef
Page 100 and 101:
BRESLOW & DAY Comparison of w* with
Page 102 and 103:
Factor C+ Exposure E + - BRESLOW &
Page 104 and 105:
104 BRESLOW & DAY there were limita
Page 106 and 107:
106 BRESLOW & DAY to have a strong
Page 108 and 109:
1 08 BRESLOW & DAY 3. If a factor i
Page 110 and 111:
110 BRESLOW & DAY Table 3.6 Risk fo
Page 112 and 113:
112 BRESLOW & DAY risk for some fac
Page 114 and 115:
114 BRESLOW & DAY an additional cat
Page 116 and 117:
116 BRESLOW & DAY Boice, J.D. & Mon
Page 118 and 119:
118 BRESLOW & DAY Report of the Sur
Page 120 and 121:
4. CLASSICAL METHODS OF ANALYSIS OF
Page 122 and 123:
ANALYSIS OF GROUPED DATA Table 4.1
Page 124 and 125:
ANALYSIS OF GROUPED DATA 125 such a
Page 126 and 127:
Estimation of q ANALYSIS OF GROUPED
Page 128 and 129:
ANALYSIS OF GROUPED DATA 129 for th
Page 130 and 131:
ANALYSIS OF GROUPED DATA 131 analog
Page 132 and 133:
ANALYSIS OF GROUPED DATA 133 Referr
Page 134 and 135:
ANALYSIS OF GROUPED DATA 135 Halper
Page 136 and 137:
ANALYSIS OF GROUPED DATA 137 are pa
Page 138 and 139:
ANALYSIS OF GROUPED DATA 1.39 signi
Page 140 and 141:
ANALYSIS OF GROUPED DATA 141 strata
Page 142 and 143:
ANALYSIS OF GROLIPED DATA 143 appro
Page 144 and 145:
ANALYSIS OF GROLIPED DATA 145 Table
Page 146 and 147:
ANALYSIS OF GROUPED DATA Exposure l
Page 148 and 149:
ANALYSIS OF GROUPED DATA 149 Nor wi
Page 150 and 151:
ANALYSIS OF GROUPED DATA 151 Alcoho
Page 152 and 153:
ANALYSIS OF GROUPED DATA 153 relati
Page 154 and 155:
ANALYSIS OF GROUPED DATA 155 obtain
Page 156 and 157:
ANALYSIS OF GROUPED DATA 157 Gart,
Page 158 and 159:
ANALYSIS OF GROUPED DATA 159 Cov(x,
Page 160 and 161:
CHAPTER V CLASSICAL METHODS OF ANAL
Page 162 and 163:
164 BRESLOW & DAY 5.2 Matched pairs
Page 164 and 165:
166 BRESLOW & DAY Approximate confi
Page 166 and 167:
168 BRESLOW & DAY where 2.42 = F.,,
Page 168 and 169:
170 BRESLOW & DAY The first and las
Page 170 and 171:
BRESLOW & DAY This is a special cas
Page 172 and 173:
ANALYSIS OF MATCHED DATA Case Expos
Page 174 and 175:
176 BRESLOW & DAY or limits of 3.8-
Page 176 and 177:
BRESLOW & DAY Table 5.3 Data layout
Page 178 and 179:
180 BRESLOW & DAY Case : control ra
Page 180 and 181:
182 BRESLOW & DAY = 11.82, from whi
Page 182 and 183:
184 BRESLOW & DAY observed and expe
Page 184 and 185:
BRESLOW & DAY Their solution, obtai
Page 186 and 187:
188 BRESLOW & DAY Miettinen, 0. S.
Page 188 and 189:
6. UNCONDITIONAL LOGISTIC REGRESSIO
Page 190 and 191:
LOGISTIC REGRESSION FOR LARGE STRAT
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
LOGISPIC REGRESSION FOR LARGE STRAT
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
6.8 Regression adjustment for confo
Page 224 and 225:
6.9 Analysis of continuous data LOG
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
7. CONDITIONAL LOGISTIC REGRESSION
Page 246 and 247:
LOGISTIC REGRESSION FOR MATCHED SET
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
LOGIS-I-IC REGRESSION FOR MATCHED S
Page 258 and 259:
Page 260 and 261:
Table 7.7 Matched multivariate anal
Page 262 and 263:
Page 264 and 265:
Table 7.9 (contd) C. Dose, duration
Page 266 and 267:
Page 268 and 269:
Page 270 and 271:
LOGIS'TIC REGRESSION FOR MATCHED SE
Page 272 and 273:
Table 7.12 Coefficients (f standard
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
APPENDIX I AGE (YRS) ALCOHOL (GM/DA
Page 280 and 281:
APPENDIX 11: GROUPED DATA FROM THE
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 310 and 311:
APPENDIX V SUBROUTINE SYMINVCA, N,
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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