Detection mode project: Tables and figures for publication

Author

Martina Brayley, Reuben Frost

Published

July 18, 2025

1 Set-Up

1.1 Table and figure structure

Main figures

Table 1: Tumour characteristics of interval and screen-detected breast cancers

Figure 1: Flow chart for selection of Generations Study breast cancer cases eligible for analyses (created in drawio (saved into the paper folder) and mermaid (memaid issue with formatting)

Figure 2: Mammographic density by detection mode and tumour characteristics

Figure 3: Mammographic density by risk factor

Figure 4: Forest plot of OR estimates for interval breast cancers based on 3 complexities of models

Supplementary material

Supplementary Table 1: Comparison of derived detection mode and regsitry detection mode

Supplementary Table 2: Multivariable model predicting log(Mammographic Density), both adjusted and non-adjusted for age at mammogram

Supplementary Table 3: OR estiamtes for interval breast cancer for 3 complexities of models

Supplementary Table 4: OR estimates for interval breast cancer for tumour characteristics

2 Main figures and tables

2.1 Table 1 - Tumour characteristics of interval and screen-detected breast cancers

by detection mode and with p-value from logistic regression model with tumour characteristics in the model (from supplementary table 1) + adjusted for time variables

Characteristic

Interval
N = 755

1

Screen-Detected
N = 1,185

1

95% CI

2

p-value

3
Age at recruitment 55.13 (7.11) 55.19 (6.93)

Age at mammogram 58.56 (5.39) 58.49 (5.37)

    Unknown 354 (47) 395 (33)

Age at diagnosis 62.32 (6.80) 61.61 (6.38) 0.99, 1.02 0.3
Time from recruitment to diagnosis (years) 7.19 (3.69) 6.42 (3.58) 1.01, 1.13 0.021
Year of diagnosis



    2004-2008 96 (13) 182 (15) —
    2009-2013 255 (34) 496 (42) 0.56, 1.17 0.3
    2014-2018 404 (54) 507 (43) 0.58, 1.75 >0.9
Invasive status



    DCIS 47 (6.2) 261 (22) —
    Invasive 708 (94) 924 (78) 0.94, 2.86 0.080
Grade



    1 88 (12) 250 (21) —
    2 335 (44) 477 (40) 1.14, 2.10 0.006
    3 264 (35) 187 (16) 1.72, 3.53 <0.001
    Missing 68 (9.0) 271 (23)

Morphology type



    Ductal 549 (73) 698 (59) —
    Lobular 95 (13) 119 (10) 0.69, 1.35 0.8
    Mixed 25 (3.3) 43 (3.6) 0.53, 1.58 0.8
    Other 39 (5.2) 64 (5.4) 0.57, 1.47 0.7
    Missing 47 (6.2) 261 (22)

Node status



    Negative 304 (40) 601 (51) —
    Positive 234 (31) 194 (16) 1.41, 2.38 <0.001
    Missing 217 (29) 390 (33)

Tumour size (mm)



    <21 336 (45) 656 (55) —
    21-50 225 (30) 162 (14) 1.55, 2.65 <0.001
    50+ 31 (4.1) 15 (1.3) 1.43, 5.73 0.003
    Missing 163 (22) 352 (30)

ER status



    Positive 525 (70) 791 (67) —
    Negative 137 (18) 87 (7.3) 1.16, 2.56 0.007
    Missing 93 (12) 307 (26)

PR status



    Positive 283 (37) 375 (32) —
    Negative 175 (23) 154 (13) 0.59, 1.18 0.3
    Missing 297 (39) 656 (55)

HER2 status



    Positive 102 (14) 92 (7.8) —
    Negative 524 (69) 725 (61) 0.71, 1.44 >0.9
    Missing 129 (17) 368 (31)

1

Mean (SD); n (%)
2

CI = Confidence Interval
3

p-value from logistic regression multivariate model of all listed variables

2.2 Figure 2 - Mammographic density by mode of detection and tumour characteristics

2.3 Figure 2 - Mammographic density by mode of detection and tumour characteristics (ALTERNATE)

2.4 Figure 3 - Mammographic density by risk factors

2.5 Figure 3 - Mammographic density by risk factors (ALTERNATE)

2.6 Figure 4 - Forest plot

png 
  2

3 Supplementary figures and tables

3.1 Supplementary Table 1

#| echo: false

# Read in data
df_mod <- readRDS("Q:/SHARED/USERS/MBrayley/Screening/data/dm_df_include_unknown.rds")

# use row percentages and rename reg_sd levels to align with dmode

dmode_crosstab_tb <- df_mod |>
  filter(source_dm2 != "reg_sd") |>
  mutate(
    
    reg_sd = if_else(is.na(reg_sd), "NA", reg_sd),
    
    reg_sd = factor(reg_sd, 
                    levels = c("N", "Y", "U", "NA"), 
                    labels = c("Interval", "Screen-Detected", "Unknown", "No Data")),
    
    d_dmode = case_when(
      ancat_dmode_v2 == "I" ~ "Interval",
      ancat_dmode_v2 == "SD" ~ "Screen-Detected",
      TRUE ~ "Other"
    ),
    
    d_dmode = factor(d_dmode, levels = c("Interval", "Screen-Detected", "Other"))
    
  ) |> 
  tbl_cross(
    row = reg_sd,
    col = d_dmode,
    percent = "cell"
  ) |> 
  bold_labels()

#dmode_crosstab_tb |> show_header_names()


dmode_crosstab_tb <- dmode_crosstab_tb |> 
  modify_header(
    label ~ "**Registry Detection Mode**"
  ) |> 
  modify_spanning_header(all_stat_cols() ~ "**Derived Detection Mode**" # Rename column variable
  ) 


dmode_crosstab_tb <- dmode_crosstab_tb |> 
  modify_table_body(
    ~ .x |> 
      filter(label != "reg_sd") |>  # Remove rows where the label matches the old variable name
      mutate(stat_1 = if_else(row_number() == n(), stat_1, gsub("\\(.*", "", stat_1)),
             stat_2 = if_else(row_number() == n(), stat_2, gsub("\\(.*", "", stat_2)),
             stat_3 = if_else(row_number() == n(), stat_3, gsub("\\(.*", "", stat_3)),
             )
  )

dmode_crosstab_tb

Registry Detection Mode

Derived Detection Mode

Interval

Screen-Detected

Other

Total
    Interval 501 0 397 898 (34%)
    Screen-Detected 33 852 119 1,004 (38%)
    Unknown 17 6 56 79 (3.0%)
    No Data 62 50 522 634 (24%)
Total 613 (23%) 908 (35%) 1,094 (42%) 2,615 (100%) 
dmode_crosstab_tb |>
  as_flex_table() |>
  flextable::save_as_docx(path = "../outputs/figures/supplement/supplementary_table_1.docx")

3.2 Supplementary Table 2

Characteristic

Unadjusted

Adjusted for age at mammogram

N = 1,191

1

Beta

95% CI

2

p-value

Beta

95% CI

2

p-value
Mode of detection






    Screen-Detected 790 (66) — —
— —
    Interval 401 (34) 0.282002231774028 0.167930063492915, 0.39607440005514 0.00000139659474806363 0.284197766718281 0.171567156954836, 0.396828376481726 0.000000846705328909703
Age at menarche






    <12 265 (24) — —
— —
    12-13 544 (50) 0.238171223121844 0.0983384629006895, 0.378003983342999 0.000860011884800794 0.245651704972748 0.107448037965322, 0.383855371980173 0.000506837628990491
    14+ 277 (26) 0.273659645561428 0.11326351824667, 0.434055772876186 0.000842779321745122 0.277007957031137 0.118508414946956, 0.435507499115319 0.00062807506044135
Oral contraception status






    No 201 (17) — —
— —
    Yes 988 (83) 0.146118722182709 0.00118871725202002, 0.291048727113398 0.0481532693133364 0.0350022594811859 -0.114006438901939, 0.184010957864311 0.644978241716587
Parity






    0 154 (13) — —
— —
    1 137 (12) -0.269912106506778 -0.490021221097815, -0.0498029919157415 0.0162853347690467 -0.265352710022793 -0.482882944762833, -0.0478224752827523 0.0168527392775608
    2 628 (53) -0.222476937106858 -0.391006219118337, -0.0539476550953792 0.00971494665428534 -0.184689259452996 -0.351799829795647, -0.0175786891103453 0.03033041449288
    3+ 272 (23) -0.18749350162982 -0.376498009913135, 0.00151100665349518 0.0518568765476234 -0.153772564008944 -0.340955584907005, 0.0334104568891175 0.107277365511506
Age at first birth






    <20 48 (4.7) — —
— —
    20-24 328 (32) 0.193565080584132 -0.0953596839255535, 0.482489845093817 0.18892865166401 0.21525675626142 -0.0709134399711788, 0.501426952494019 0.140244402567498
    25-29 407 (39) 0.201778942218894 -0.0835437157335331, 0.487101600171321 0.165525129942472 0.217273042202208 -0.0652613365254922, 0.499807420929908 0.13160116025211
    30-34 179 (17) 0.273745771898495 -0.0301426686336285, 0.577634212430618 0.0774182041987249 0.241440154807369 -0.0597037628355813, 0.54258407245032 0.115970477763797
    35+ 69 (6.7) 0.163511529318046 -0.187883936122156, 0.514906994758249 0.36141042509489 0.11376371599749 -0.234730485178377, 0.462257917173358 0.521942553647686
Breastfeeding duration (mths)






    Never breastfed 183 (18) — —
— —
    <6 342 (33) 0.0096116858510723 -0.16019239735304, 0.179415769055184 0.911579920387213 0.0106509136467754 -0.157720045044934, 0.179021872338485 0.901236280194627
    6-12 210 (20) 0.20382237033883 0.0163366660824114, 0.391308074595248 0.0331397330528889 0.19052691533787 0.0045261832447456, 0.376527647430993 0.0446894148245827
    12-24 215 (21) 0.311008827305157 0.124541031683695, 0.497476622926619 0.00109999107623419 0.281404269202552 0.0960223721396061, 0.466786166265499 0.00296285450239594
    24+ 87 (8.4) 0.189178358661432 -0.0522586192806295, 0.430615336603494 0.124468539774164 0.148512251879819 -0.0915979945831981, 0.388622498342836 0.225141900171394
Menopausal status






    Premenopausal 233 (20) — —
— —
    Postmenopausal 958 (80) -0.228071711687485 -0.364689953708645, -0.0914534696663238 0.00108587386406144 -0.0251957736820236 -0.187502181226787, 0.13711063386274 0.760749317663468
Age at menopause






    <50 210 (22) — —
— —
    50-54 639 (67) -0.0227242121317078 -0.174677831521916, 0.1292294072585 0.769218853141485 0.0141830050450056 -0.136963652148161, 0.165329662238172 0.853935016256251
    55+ 104 (11) -0.180982219420126 -0.410045664082184, 0.048081225241931 0.121345955652293 -0.103761942656546 -0.332731997557964, 0.125208112244871 0.374052446349856
MHT status






    Never 384 (32) — —
— —
    Former 316 (27) -0.0420082375797406 -0.181262314166986, 0.097245839007505 0.554056994214264 0.0231948520446346 -0.117953349604261, 0.16434305369353 0.747198522846772
    Current 254 (21) 0.476771678393426 0.328487122740197, 0.625056234046654 0.000000000398117528762592 0.50413161738305 0.356506408411325, 0.651756826354774 0.0000000000321783987589249
    Pre-menopausal 232 (20) 0.344096888889536 0.191639385807671, 0.496554391971402 0.0000103828140023481 0.17064040595861 0.00101353396492701, 0.340267277952293 0.0486489215580291
Benign breast disease






    No 787 (66) — —
— —
    Yes 404 (34) 0.4278128858167 0.315433791869286, 0.540191979764113 0.000000000000155997086523219 0.433495188374535 0.322614211173821, 0.544376165575248 0.0000000000000355522842474319
Family history of breast cancer






    No 923 (77) — —
— —
    Yes 268 (23) -0.0650775799594714 -0.195387150117223, 0.0652319901982803 0.327375200359052 -0.0761327176277542 -0.204883691610892, 0.052618256355384 0.246223611110417
BMI at age 20






    <18.5 92 (9.4) — —
— —
    18.5-25 811 (83) -0.163553810183676 -0.367757096397821, 0.0406494760304693 0.11633075270086 -0.164074807953788 -0.365504707631454, 0.0373550917238788 0.110260964468573
    25-30 58 (5.9) -0.959290454308762 -1.27050573414835, -0.648075174469173 0.00000000207853410324907 -1.02169697328346 -1.32955884161064, -0.71383510495629 0.000000000118358836522793
    30+ 14 (1.4) -1.10372988650898 -1.63622814722326, -0.571231625794703 0.0000513592707748244 -1.09249541280032 -1.61777787480039, -0.567212950800248 0.000048432321084192
BMI at recruitment






    18.5-25 572 (49) — —
— —
    <18.5 7 (0.6) 0.0410707747101447 -0.613830029458456, 0.695971578878745 0.902094684497947 0.0446464389813686 -0.599641695483414, 0.688934573446151 0.891877991046864
    25-30 387 (33) -0.494407804835037 -0.607762387652705, -0.381053222017368 0.0000000000000000357058087054683 -0.494665184116271 -0.606182718513177, -0.383147649719364 0.0000000000000000108075512754099
    30+ 209 (18) -1.01882246377623 -1.15802195109346, -0.879622976459003 0.000000000000000000000000000000000000000000340982754010251 -1.02982152011457 -1.16680768332923, -0.892835356899917 0.00000000000000000000000000000000000000000000275925728397052
Physical activity (MET h/wk)






    <9 267 (23) — —
— —
    9-17 227 (19) 0.0433783393315539 -0.125956530343687, 0.212713209006794 0.615340628176535 0.0546137492377738 -0.112605376561824, 0.221832875037371 0.521788434514142
    18+ 687 (58) 0.0890114494982962 -0.0462554921374558, 0.224278391134048 0.196933784739762 0.107758228059638 -0.0259418449208863, 0.241458301040163 0.114078589877251
Alcohol units per week






    0 248 (21) — —
— —
    1-9 323 (27) 0.146317426448321 -0.0117667439584309, 0.304401596855072 0.0696334565797344 0.149983795474338 -0.00622894745730909, 0.306196538405986 0.0598455208809154
    10-19 300 (25) 0.234048945085737 0.0733543597072513, 0.394743530464223 0.004343562985358 0.214117457074214 0.0551686948355617, 0.373066219312866 0.00832741227754792
    20-29 195 (16) 0.254804390804421 0.0755969041636866, 0.434011877445155 0.00536176286983431 0.245928045257936 0.0688195195471914, 0.423036570968681 0.00653787250745558
    30+ 125 (10) 0.106773769654616 -0.0986123079287782, 0.312159847238011 0.307953257452121 0.0894272116302483 -0.113616357100275, 0.292470780360771 0.387698711289114
Smoking status






    Never 709 (60) — —
— —
    Former 415 (35) -0.102343418249186 -0.218373869326412, 0.0136870328280405 0.0837955509967461 -0.0987226848674841 -0.213373070959757, 0.0159277012247888 0.091405279337893
    Current 66 (5.5) 0.0428386241211565 -0.198758498061786, 0.284435746304099 0.727988709131488 -0.0262489792796025 -0.266240607398537, 0.213742648839333 0.830124507491796
Invasive status






    0 191 (16) — —
— —
    1 1,000 (84) 0.0322646984954516 -0.116079267217381, 0.180608664208284 0.669656857530737 0.0674517951290527 -0.0795544125850606, 0.214458002843166 0.368185671720245
Grade






    1 211 (21) — —
— —
    2 493 (50) 0.0137343869204579 -0.137540492858141, 0.165009266699057 0.858629066378136 0.0227653542733494 -0.126984030628272, 0.172514739174971 0.765516445156324
    3 282 (29) -0.125144272186412 -0.292523997964145, 0.0422354535913198 0.142639710051171 -0.125750308357852 -0.291389041221486, 0.0398884245057822 0.136595321734372
Morphology






    Ductal 770 (77) — —
— —
    Lobular 132 (13) 0.221739689437698 0.0480830369991425, 0.395396341876253 0.01237970581926 0.233887165460177 0.0620917377591922, 0.405682593161162 0.00767235754252997
    Mixed 39 (3.9) 0.320205933080355 0.0176420543158036, 0.622769811844906 0.0380786766177862 0.353559226508413 0.054056605586556, 0.65306184743027 0.0207316342923975
    Other 59 (5.9) 0.207482236418837 -0.0415332638985121, 0.456497736736187 0.102354804917207 0.242287566876058 -0.00436039405478092, 0.488935527806896 0.0541818846073031
Node status






    Negative 548 (66) — —
— —
    Positive 277 (34) 0.0766824560085987 -0.05924186258077, 0.212606774597967 0.268464860376064 0.057379633899986 -0.0775630189165201, 0.192322286716492 0.404165783324601
Tumour size (mm)






    <21 627 (71) — —
— —
    21-50 233 (26) 0.0307185871346405 -0.112011550009116, 0.173448724278397 0.672831690712306 0.0411554183924437 -0.0997463580085658, 0.182057194793453 0.566611331139944
    50+ 25 (2.8) 0.307107631034669 -0.0722931420657307, 0.686508404135068 0.112490676817565 0.238408150284795 -0.1369502503606, 0.613766550930191 0.212882929911977
ER status






    Positive 841 (86) — —
— —
    Negative 137 (14) -0.112797121113212 -0.284103481461015, 0.0585092392345902 0.196612513545896 -0.106162861720744 -0.275793289424717, 0.0634675659832279 0.219681753921098
PR status






    Positive 422 (68) — —
— —
    Negative 199 (32) -0.00968380125505817 -0.165739681937952, 0.146372079427836 0.903048855926002 -0.00719569659502632 -0.1628252283821, 0.148433835192047 0.927681852578361
HER2 status






    Positive 119 (13) — —
— —
    Negative 768 (87) 0.0468492844206032 -0.13787786008925, 0.231576428930457 0.618781152161037 0.0781073773462307 -0.104794295475095, 0.261009050167556 0.402177633285862
1

n (%)
2

CI = Confidence Interval

3.3 Supplementary Table 3

Characteristic

Distribution by detection mode

Model 1

Model 2

Model 3

Model 4

Interval
N = 755

1

Screen-Detected
N = 1,185

1

OR

2,3

95% CI

3

p-value

OR

4,3

95% CI

3

p-value

OR

3

95% CI

3

p-value

OR

3

95% CI

3

p-value
Age at menarche













    <12 153 (20) 260 (22) — —
— —
— —
— —
    12-13 369 (49) 523 (44) 1.18970366071405 0.92394262581602, 1.53503322410981 0.179609328924742 1.14382300581778 0.884011000801894, 1.48266036253312 0.308138274041658 1.2121236949329 0.91815515453064, 1.60344160676607 0.175970314549626 1.15122941834905 0.8679881084324, 1.52954569548545 0.329589288902445
    14+ 169 (22) 286 (24) 0.894086907287415 0.666552655348388, 1.19916363837784 0.454672234932706 0.862752185921053 0.639828682989089, 1.16303688349621 0.332614746532762 0.942085765975539 0.68289042382258, 1.29981937953166 0.71621058558112 0.889948396807779 0.641410229740586, 1.23449807108156 0.484927185915177
    Missing 64 (8.5) 116 (9.8)











Oral contraceptive use













    Never 95 (13) 182 (15) — —
— —
— —
— —
    Ever 659 (87) 1,001 (84) 1.22819890285804 0.916516559421078, 1.65364420365478 0.171770728443431 1.25260417608993 0.928757629333117, 1.69726576295272 0.142755685621645 1.30742784226834 0.949704368764123, 1.80822622634752 0.102444819254853 1.32796417278707 0.9577810905919, 1.85009902762645 0.090983794093035
    Missing 1 (0.1) 2 (0.2)











Parity













    0 97 (13) 161 (14) — —
— —
— —
— —
    1 86 (11) 125 (11) 1.33621460368277 0.699568121070013, 2.5424299263455 0.377944938721181 1.43423430942869 0.74043509593909, 2.76971153933478 0.283256699633665 1.1521841296893 0.567328152471258, 2.32684688767494 0.693611084010371 1.18196773568244 0.575150726816525, 2.41751673403025 0.647694896528601
    2 408 (54) 630 (53) 1.21977248251415 0.680066651830209, 2.17738548112227 0.502604120724911 1.32170682466923 0.725599642103971, 2.39720819340982 0.359437720048704 0.998353726117664 0.526268677820562, 1.87895495850381 0.995944404209512 1.05765635753339 0.550373765333946, 2.01727169626801 0.865456868484406
    3+ 164 (22) 269 (23) 1.13136871147845 0.621270221766052, 2.04938779036766 0.684707756216023 1.23835020172697 0.669355331854847, 2.27949902488816 0.49339431730072 0.992560111126576 0.515433022352854, 1.89716100518623 0.982050959510269 1.05085691303916 0.537587908061347, 2.03930503136475 0.883886726731275
Age at first birth













    <20 35 (4.6) 45 (3.8) — —
— —
— —
— —
    20-24 197 (26) 308 (26) 0.833601518065666 0.503874445232693, 1.38795033553409 0.480225036004774 0.804216931094891 0.479904386285524, 1.35509798357244 0.409447491769801 0.765071333562332 0.442580798957426, 1.33263619906166 0.339853081199348 0.748784826636921 0.42916947178981, 1.31520646137281 0.310119198669623
    25-29 256 (34) 421 (36) 0.761937123797945 0.461507087591846, 1.26588591625276 0.289556779911419 0.747301922042956 0.447362548387807, 1.2550724354384 0.26711425817551 0.733518790138873 0.424710768699113, 1.27650275437509 0.268523378478442 0.739838025175775 0.42519268075331, 1.29607852749951 0.288093438473163
    30-34 122 (16) 174 (15) 0.861204672989829 0.498693952128368, 1.49431408070238 0.592771987652466 0.867810003824113 0.496385425702825, 1.52329303364772 0.619437023284884 0.834072009550656 0.460464779878971, 1.51998392193696 0.550748608385012 0.848271867141011 0.463959754508447, 1.55921025446389 0.593927040959584
    35+ 46 (6.1) 71 (6.0) 0.80338118757236 0.425020521413558, 1.51934056002195 0.499828054381923 0.815574560261925 0.42581002428854, 1.56237403599804 0.538097567113028 0.830853175723575 0.414986550712754, 1.66719013127597 0.600919849300964 0.861167549624382 0.425351902077958, 1.74674810421719 0.677875835969348
    Missing 99 (13) 166 (14)











Breastfeeding duration (mths)













    Never breastfed 101 (13) 179 (15) — —
— —
— —
— —
    <6 223 (30) 319 (27) 1.23618091912011 0.905757591979394, 1.69237729692915 0.18335619690558 1.21803961542764 0.886197020179118, 1.67893867822449 0.225943959146272 1.34597374371459 0.956980780142148, 1.8994187946376 0.0891063042876961 1.32441626235948 0.934751043236925, 1.88243748836197 0.115394234895675
    6-12 131 (17) 200 (17) 1.11765427257907 0.788661562775208, 1.5862628736026 0.532380952402841 1.10952399125753 0.777124021531673, 1.5863404360652 0.567795274485561 1.30963411950685 0.893496002684359, 1.92384288213298 0.16770071831326 1.30662153520436 0.884679362075393, 1.93395852869494 0.179807356508859
    12-24 131 (17) 244 (21) 0.926402691082788 0.650298247682073, 1.32124558071355 0.672292175048794 0.857026148001365 0.597454583453254, 1.23039723325099 0.40217288617132 1.00996313800387 0.685468489063955, 1.49010433401271 0.960058314563226 0.938287194943279 0.632297277462521, 1.39381760589848 0.751921468978657
    24+ 72 (9.5) 82 (6.9) 1.48475329194526 0.949021785140093, 2.32516237831303 0.08354455767818 1.44885716725202 0.91881478044759, 2.28790496598709 0.110824795520355 1.56399243495586 0.959560927149307, 2.55224467772722 0.072871723219384 1.50301296254525 0.91519584371436, 2.47245514394385 0.107712275677089
    Missing 97 (13) 161 (14)











Menopausal status













    Premenopausal 242 (32) 355 (30) — —
— —
— —
— —
    Postmenopausal 513 (68) 830 (70) 0.760399845945529 0.512289104155234, 1.12484541830218 0.171921993363351 0.81422823502238 0.544150021087268, 1.21467445225125 0.315420916367031 0.823333362325103 0.533229028277488, 1.26692357309598 0.378246441453078 0.882216343503852 0.56677738581823, 1.36890695917569 0.577207281691913
Age at menopause













    <50 116 (15) 185 (16) — —
— —
— —
— —
    50-54 332 (44) 570 (48) 0.869754325315802 0.656903588473789, 1.1540745448846 0.331359383037574 0.900024029697442 0.675176530688343, 1.20225233797251 0.473916548147113 0.874155773058709 0.642177808770967, 1.19218460803268 0.393789257168072 0.901102207758448 0.656868026148246, 1.23836707273429 0.519414668534516
    55+ 64 (8.5) 71 (6.0) 1.51783140793898 0.980264800763526, 2.35146451939717 0.0612756242567543 1.59272127502517 1.01788552002923, 2.49455817041093 0.0415924343316432 1.50027614172708 0.926361708551483, 2.42956433612642 0.0987913735594522 1.56738322207613 0.957620701112374, 2.56573697689571 0.0735851012825106
    Missing 243 (32) 359 (30)











MHT status













    Never 192 (25) 354 (30) — —
— —
— —
— —
    Former 161 (21) 281 (24) 1.01243600275851 0.763940595206877, 1.34105113310311 0.931357316301028 1.01103986390513 0.758299441956907, 1.34730074990275 0.940282227220179 0.970920294386037 0.713145055301371, 1.32073112588478 0.85102181051202 0.965699294077695 0.703802219639225, 1.32378132538388 0.82845283751702
    Current 157 (21) 192 (16) 1.4878971085758 1.10807835161908, 1.99911788397391 0.00826596443003789 1.39671657884622 1.03324116009632, 1.8887519785669 0.0298292797905461 1.5390778700278 1.11325035777821, 2.12956639846711 0.00912889513585181 1.44741342356507 1.03979326039366, 2.01590312093014 0.0284789784984772
    Missing 245 (32) 358 (30)











Benign breast disease













    Never 467 (62) 831 (70) — —
— —
— —
— —
    Ever 288 (38) 354 (30) 1.45720967268877 1.19099239422015, 1.78300283771148 0.000253319858505629 1.36526711316466 1.10921561332149, 1.6803063184009 0.00328675330165802 1.6970958815717 1.35960936938175, 2.12036033483653 0.00000304917733167562 1.59271750150706 1.26809824976672, 2.0020971176701 0.000064184681447985
Family history of breast cancer













    No 564 (75) 931 (79) — —
— —
— —
— —
    Yes 191 (25) 254 (21) 1.25533325301579 1.00224959570826, 1.57087935949805 0.0471973096913542 1.25686408472292 0.999438171093055, 1.57931931712558 0.0500469028981229 1.36649644841348 1.06799673713671, 1.74790847455645 0.0129216044984251 1.35705797194955 1.05650449645868, 1.74268943955104 0.0167229508398267
BMI at recruitment













    <18.5 8 (1.1) 3 (0.3) 3.4076648455972 0.949119375266937, 15.9722935275602 0.0775449379028145 4.11600455959613 1.10382913047262, 20.0425123929378 0.0481122881632467 3.57129263358239 0.868799876009116, 18.5277227414939 0.0934092236144049 4.20931187072697 0.995621077865878, 22.4907640391223 0.0634131518187077
    18.5-25 421 (56) 550 (46) — —
— —
— —
— —
    25-30 203 (27) 405 (34) 0.689951885086624 0.550901204426532, 0.862758208479244 0.00117701543411275 0.741785394617025 0.588471466867158, 0.933848976417751 0.0111965382670774 0.670137417056234 0.523660248670322, 0.856065475362181 0.0014044558905504 0.714177462412915 0.554520311177575, 0.918440362990233 0.00888727127288198
    30+ 116 (15) 213 (18) 0.769923588600272 0.576648105883008, 1.02447667321288 0.0743164424196542 0.86816562876314 0.642738556791943, 1.16968865715581 0.35434032331371 0.714602024554875 0.519012667739077, 0.98031935985549 0.0382019105278447 0.812642243373798 0.582853476988306, 1.12980464306963 0.218858534660015
    Missing 7 (0.9) 14 (1.2)











BMI at age 20













    <18.5 77 (10) 72 (6.1) 1.5856202107339 1.10430430316356, 2.27878095179405 0.0124823976613577 1.64701778320751 1.14054467565313, 2.38169240630936 0.00780528149794433 1.4665433598101 0.983495377259847, 2.18904899829642 0.0603288037722799 1.49946527570517 1.00114819244755, 2.24882669936747 0.0494324212015747
    18.5-25 506 (67) 828 (70) — —
— —
— —
— —
    25-30 30 (4.0) 60 (5.1) 0.903818701783611 0.553926988960262, 1.44846838254941 0.679058340572829 0.968275653391221 0.587834027652866, 1.56855270087016 0.897223168243536 0.892682794225552 0.526549930317058, 1.48968165301816 0.667917423911791 0.94830897392302 0.554296444453364, 1.59900076740794 0.843913169910404
    30+ 9 (1.2) 9 (0.8) 2.04146572593057 0.755392050496253, 5.52505462237008 0.1538060804061 2.54749600781363 0.921281522582003, 7.05765342438735 0.0681462535596732 2.60361398942739 0.8702762088005, 7.72084617288693 0.0821272074737869 3.29475856274848 1.08588591643148, 9.89415199800136 0.03236452390509
    Missing 133 (18) 216 (18)











Physical activity (MET h/wk)













    <9 153 (20) 271 (23) — —
— —
— —
— —
    9-17 145 (19) 246 (21) 1.01468463457592 0.753106851090504, 1.36677762957375 0.923576605635024 0.966441537849415 0.713128958824794, 1.30922183890615 0.825589884860799 0.902816859631625 0.651013973772385, 1.25109273349196 0.539328145424714 0.856288441065938 0.613764393674947, 1.19348694230413 0.360179469718401
    18+ 451 (60) 660 (56) 1.18080170546388 0.926402962566295, 1.50848393545762 0.181254257977065 1.15251297849091 0.899677588959638, 1.4794541164309 0.26303022140061 1.20374501269899 0.922367757654126, 1.57456026262306 0.173832853577451 1.15182449787733 0.878053759340389, 1.51394868350806 0.308840012682095
    Missing 6 (0.8) 8 (0.7)











Alcohol units per week













    0 149 (20) 258 (22) — —
— —
— —
— —
    1-9 200 (26) 317 (27) 1.0428742528384 0.786913017215229, 1.3832362717736 0.770373627547642 1.0480873566612 0.786998931423611, 1.39691905421575 0.748197959380699 1.17457330452868 0.862496173172012, 1.60183741170738 0.308023111927126 1.18041234252549 0.862983126415051, 1.616850445365 0.300141062217919
    10-19 208 (28) 290 (24) 1.20986963860263 0.910208713825632, 1.61016938958005 0.190254428937033 1.21794893903981 0.911997916988141, 1.62850061779412 0.18229827736336 1.25401361271706 0.917704346516257, 1.71583267088566 0.156009246197634 1.2321491215447 0.897204508723632, 1.69414574998991 0.197721466589367
    20-29 126 (17) 190 (16) 1.03745973533966 0.750927758774707, 1.43266789059942 0.82331585333491 1.05150548031607 0.756800889673845, 1.46035236385139 0.764432310636851 1.23598584888021 0.864905918416672, 1.7669054119804 0.244717816074036 1.23955989406293 0.862763125921893, 1.78166259252176 0.245409566431354
    30+ 72 (9.5) 130 (11) 0.888615859889604 0.609193006558286, 1.29100109997491 0.537290405591246 0.92412419353511 0.629824746244942, 1.35091424766232 0.68495707264652 0.895251621592467 0.593068592167012, 1.34671559000967 0.596576043783526 0.926231102000982 0.60990287434372, 1.40231210582815 0.718027191554571
Smoking status













    Never 453 (60) 709 (60) — —
— —
— —
— —
    Former 261 (35) 392 (33) 1.05472665309425 0.853147279173858, 1.30324420382756 0.621917809592569 1.08506735076118 0.874039063252624, 1.34654961057214 0.458828318351777 1.04325508422056 0.827099111688044, 1.31519119471219 0.720316496866688 1.08494299818355 0.8564234064552, 1.37400480752077 0.498821194925161
    Current 41 (5.4) 82 (6.9) 0.772279491088689 0.506954274604099, 1.16146543924711 0.220711077494466 0.780911300619068 0.508813930453885, 1.18432417608096 0.250167846744668 0.757357664589934 0.480542612590633, 1.17948414958873 0.224158541937614 0.771691480909111 0.486140527258159, 1.21089976092183 0.264741857999101
    Missing 0 (0) 2 (0.2)











Mammographic density (quartiles)













    1 73 (9.7) 225 (19)


— —



— —
    2 96 (13) 202 (17)


1.40359333801886 0.958731503281013, 2.06138556997554 0.082243861981025


1.45122559662967 0.959068248182952, 2.20423007174867 0.0791052983164007
    3 105 (14) 193 (16)


1.67126291484838 1.14199222552052, 2.45588230774802 0.00849741138032495


1.59854810619712 1.04858900827397, 2.44695976694155 0.0298649736907815
    4 127 (17) 170 (14)


2.15596867198532 1.45337932271584, 3.21533127587838 0.000147300559042917


2.21593145866681 1.43689513235503, 3.43680188536292 0.000344477244250443
    Missing 354 (47) 395 (33)











Invasive status













    DCIS 47 (6.2) 261 (22)





— —
— —
    Invasive 708 (94) 924 (78)





1.46589005386912 0.825286195113752, 2.61173379166584 0.192776371253556 1.52988407242605 0.851450378268118, 2.75783003999784 0.155813682185865
Grade













    1 135 (18) 511 (43)





— —
— —
    2 335 (44) 477 (40)





1.65228776336111 1.20369738049862, 2.28065277630704 0.00205098960412556 1.6563459565306 1.20048620051977, 2.29792226637725 0.00229599134709204
    3 264 (35) 187 (16)





2.8969400372751 1.99319688890557, 4.23227207152771 0.0000000301018209276548 3.03004593347443 2.06939562324207, 4.46099009894837 0.0000000150323564325463
    Missing 21 (2.8) 10 (0.8)











Morphology type













    Ductal 596 (79) 959 (81)





— —
— —
    Lobular 95 (13) 119 (10)





0.928304340399314 0.649928251007234, 1.32132223478955 0.680783308444669 0.892925814883254 0.621912561835246, 1.27765989997557 0.537129895188097
    Mixed 25 (3.3) 43 (3.6)





0.914901448182571 0.511949317196744, 1.60409091278757 0.759350689221551 0.872958520519938 0.483202838530173, 1.5476945730597 0.646192809461996
    Other 39 (5.2) 64 (5.4)





0.947804197672572 0.572984337560871, 1.55070086796382 0.832475597912653 0.892962008450491 0.53627170708766, 1.47042772758255 0.659246169548446
Node status













    Negative 351 (46) 862 (73)





— —
— —
    Positive 234 (31) 194 (16)





1.86972882559477 1.42185664846167, 2.46015510461709 0.00000758399285332401 1.8670524227725 1.41370979548745, 2.46735994497951 0.0000109830849490486
    Missing 170 (23) 129 (11)











Tumour size (mm)













    <21 383 (51) 917 (77)





— —
— —
    21-50 225 (30) 162 (14)





2.02061939821956 1.52860764037009, 2.67469272043167 0.000000817872659522611 2.05315912350181 1.54642247824637, 2.73025192149876 0.000000692241955480093
    50+ 31 (4.1) 15 (1.3)





2.84612964570436 1.40084271172151, 5.9852105713458 0.0045489736734432 2.70812647948706 1.31859788185726, 5.76386782142491 0.00780912656095206
    Missing 116 (15) 91 (7.7)











ER status













    Positive 572 (76) 1,052 (89)





— —
— —
    Negative 137 (18) 87 (7.3)





1.80087833435284 1.19638033152295, 2.72178682085939 0.00498144184800815 1.79844269931459 1.1831449360455, 2.74553233136695 0.00622431679821085
    Missing 46 (6.1) 46 (3.9)











PR status













    Positive 330 (44) 636 (54)





— —
— —
    Negative 175 (23) 154 (13)





0.834850281830667 0.579085477258884, 1.19966890219283 0.330908918164511 0.802217456233262 0.55336044518727, 1.1592144015496 0.242329740499816
    Missing 250 (33) 395 (33)











HER2 status













    Positive 149 (20) 353 (30)





— —
— —
    Negative 524 (69) 725 (61)





1.19729012865128 0.829716071001849, 1.73115480477144 0.336733445424446 1.12579727907842 0.772690706266611, 1.64294651687333 0.537682527410089
    Missing 82 (11) 107 (9.0)











1

n (%)
2

Model 1: Age at menarche, Menopause, Age at menopause, MHT status, Oral contraceptive use, Parity, Age at first birth, Breast feeding duration (mths), Benign breast disease, Family history of breast cancer, BMI at recruitment, BMI at age 20, Physical activity (MET h/wk), Alcohol units per week, Smoking status, Age at Diagnosis, Time from Recruitment to BC diagnosis, Year of diagnosis
3

OR = Odds Ratio, CI = Confidence Interval
4

Model 2: Age at menarche, Menopause, Age at menopause, MHT status, Oral contraceptive use, Parity, Age at first birth, Breast feeding duration (mths), Benign breast disease, Family history of breast cancer, BMI at recruitment, BMI at age 20, Physical activity (MET h/wk), Alcohol units per week, Smoking status, Age at Diagnosis, Time from Recruitment to BC diagnosis, Year of diagnosis Percent Mammographic density (quartiles), Time from Mammographic density measurement to BRCA diagnosis.

3.4 Supplementary Table 4

TC descriptives by detection mode, logistic regression of TC with detection mode (same principal as risk factor analysis but multiple exposures are TC)

Characteristic

Distribution by detection mode

Model

Interval
N = 755

1

Screen-Detected
N = 1,185

1

OR

2

95% CI

2

p-value
Invasive status




    DCIS 47 (6.2) 261 (22) — —
    Invasive 708 (94) 924 (78) 1.56273398206463 0.904933537692987, 2.70742861578673 0.109993944336353
Grade




    1 88 (12) 250 (21) — —
    2 335 (44) 477 (40) 1.55903670572305 1.15281756668936, 2.11980839010339 0.00423260583742127
    3 264 (35) 187 (16) 2.41000203469726 1.69225231892957, 3.4469473348734 0.00000123392411098458
    Missing 68 (9.0) 271 (23)


Morphology type




    Ductal 549 (73) 698 (59) — —
    Lobular 95 (13) 119 (10) 0.940958928592262 0.672047485372323, 1.31273016995573 0.721358799560706
    Mixed 25 (3.3) 43 (3.6) 0.915712350638881 0.529545493875612, 1.55285020812785 0.747374852746166
    Other 39 (5.2) 64 (5.4) 0.892993550217292 0.552549540995673, 1.42625681205155 0.639064325853048
    Missing 47 (6.2) 261 (22)


Node status




    Negative 304 (40) 601 (51) — —
    Positive 234 (31) 194 (16) 1.671633521459 1.29233519032716, 2.1621255691034 0.0000902577436776707
    Missing 217 (29) 390 (33)


Tumour size (mm)




    <21 336 (45) 656 (55) — —
    21-50 225 (30) 162 (14) 2.11584520475006 1.62667183799653, 2.75568872458245 0.0000000246010895845165
    50+ 31 (4.1) 15 (1.3) 3.11404685789473 1.59909408261829, 6.29819316187855 0.00108548395021369
    Missing 163 (22) 352 (30)


ER status




    Positive 525 (70) 791 (67) — —
    Negative 137 (18) 87 (7.3) 1.68324512616536 1.14228818587848, 2.48762735314283 0.008655672942842
    Missing 93 (12) 307 (26)


PR status




    Positive 283 (37) 375 (32) — —
    Negative 175 (23) 154 (13) 0.860240848297712 0.607546654977772, 1.21374489789363 0.393385393297041
    Missing 297 (39) 656 (55)


HER2 status




    Positive 102 (14) 92 (7.8) — —
    Negative 524 (69) 725 (61) 1.0261300437722 0.727760005171562, 1.4483742407957 0.883076801701265
    Missing 129 (17) 368 (31)


1

n (%)
2

OR = Odds Ratio, CI = Confidence Interval

4 Miscellaneous

4.1 Associations of BBD with TC and MOD

WARNINGS?

# Try and find associations with tumour characteristics


model <- df |>
  glm(formula = as.formula(paste0("d_bbd_lab ~", paste0(tumour_char_tr,collapse = "+"))), family = "binomial")


tbl_regression(x = model,
                 exponentiate = T) |>
  add_global_p() |>
  print()

Characteristic

OR

1

95% CI

1

p-value

d_inv_status

0.7
    0 — —
    1 1.12 0.68, 1.83
d_grade_tr

0.2
    1 — —
    2 0.87 0.65, 1.16
    3 0.68 0.48, 0.97
    Missing 0.89 0.38, 2.01
d_morph4_tr_lab

0.7
    Ductal — —
    Lobular 1.12 0.80, 1.56
    Mixed: ductal and other 1.10 0.64, 1.84
    Other 1.24 0.80, 1.91
d_pos_nodes_tr_lab

0.2
    Negative — —
    Positive 0.85 0.65, 1.12
    Missing 1.17 0.86, 1.58
d_tmsize_tr_lab

0.3
    <21 — —
    21-50 1.03 0.78, 1.35
    50+ 1.83 0.96, 3.47
    Missing 1.02 0.71, 1.44
d_er_tr_lab

0.6
    Positive — —
    Negative 1.21 0.81, 1.79
    Missing 0.98 0.59, 1.60
d_pr_tr_lab

0.9
    Positive — —
    Negative 0.91 0.64, 1.28
    Missing 0.98 0.76, 1.25
d_her2_tr_lab

0.3
    Positive — —
    Negative 0.78 0.55, 1.11
    Missing 0.90 0.57, 1.41
1

OR = Odds Ratio, CI = Confidence Interval 

# Testing BBD History association with MOD with/without removing Grade

# Full model
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab
      + d_inv_status + d_grade_tr + d_morph4_tr_lab + d_pos_nodes_tr_lab 
      + d_tmsize_tr_lab + d_er_tr_lab + d_pr_tr_lab + d_her2_tr_lab,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_bbd_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_bbd_lab


    No — —
    Yes 1.59 1.27, 2.00 <0.001
1

OR = Odds Ratio, CI = Confidence Interval

 
# Model without TC
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_bbd_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_bbd_lab


    No — —
    Yes 1.37 1.11, 1.68 0.003
1

OR = Odds Ratio, CI = Confidence Interval

 
# Including Grade
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab
      + d_grade_tr,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_bbd_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_bbd_lab


    No — —
    Yes 1.55 1.25, 1.94 <0.001
1

OR = Odds Ratio, CI = Confidence Interval

4.2 Associations of Family History of BC with TC and MOD

# Try and find associations with tumour characteristics


model <- df |>
  glm(formula = as.formula(paste0("d_fambrca_lab ~", paste0(tumour_char_tr,collapse = "+"))), family = "binomial")


tbl_regression(x = model,
                 exponentiate = T) |>
  print()

Characteristic

OR

1

95% CI

1

p-value

d_inv_status


    0 — —
    1 0.81 0.46, 1.43 0.5
d_grade_tr


    1 — —
    2 0.99 0.72, 1.37
0.9
    3 0.88 0.60, 1.30 0.5
    Missing 0.87 0.30, 2.23 0.8
d_morph4_tr_lab


    Ductal — —
    Lobular 0.80 0.54, 1.17 0.3
    Mixed: ductal and other 1.06 0.58, 1.83 0.8
    Other 0.86 0.50, 1.42 0.6
d_pos_nodes_tr_lab


    Negative — —
    Positive 1.03 0.77, 1.38 0.8
    Missing 1.02 0.72, 1.43
0.9
d_tmsize_tr_lab


    <21 — —
    21-50 1.07 0.79, 1.45 0.6
    50+ 0.88 0.38, 1.86 0.8
    Missing 0.92 0.60, 1.36 0.7
d_er_tr_lab


    Positive — —
    Negative 0.93 0.59, 1.44 0.7
    Missing 1.53 0.88, 2.58 0.12
d_pr_tr_lab


    Positive — —
    Negative 1.11 0.75, 1.62 0.6
    Missing 0.86 0.65, 1.13 0.3
d_her2_tr_lab


    Positive — —
    Negative 1.29 0.87, 1.96 0.2
    Missing 0.95 0.55, 1.64 0.9
1

OR = Odds Ratio, CI = Confidence Interval 

# Testing BBD History association with MOD with/without removing Grade

# Full model
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab
      + d_inv_status + d_grade_tr + d_morph4_tr_lab + d_pos_nodes_tr_lab 
      + d_tmsize_tr_lab + d_er_tr_lab + d_pr_tr_lab + d_her2_tr_lab,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_fambrca_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_fambrca_lab


    No — —
    Yes 1.36 1.06, 1.74 0.017
1

OR = Odds Ratio, CI = Confidence Interval

 
# Model without TC
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_fambrca_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_fambrca_lab


    No — —
    Yes 1.26 1.00, 1.58 0.050
1

OR = Odds Ratio, CI = Confidence Interval

 
# Adding grade
model <- df %>%
  glm(formula = d_dmode_n ~ d_R1menopause_lab3 + d_age_meno_tr_lab
      + d_R1hrt_tr_lab
      + d_parity_lab + d_agebirth1_tr_lab + d_age_menarche_lab + d_ocstatus2_lab
      + d_bbd_lab + d_fambrca_lab
      + d_R1alcohol_units_lab + d_R1smokingstatus_lab
      + d_bmi_entry_lab + d_R1physmet_leis_who_lab + d_bmi_20_lab + d_bf_dur_tr_lab
      + diagage + d_R1toBC_y + yeardiag
      + d_md_qrt + d_MDtoBC_lab
      + d_grade_tr,
            family = binomial(link = "logit"))

tbl_regression(x = model, exponentiate = T) |>
  modify_table_body(
    ~ .x %>%
      dplyr::filter(.data$variable == "d_fambrca_lab")
  )

Characteristic

OR

1

95% CI

1

p-value
d_fambrca_lab


    No — —
    Yes 1.36 1.07, 1.73 0.013
1

OR = Odds Ratio, CI = Confidence Interval

4.3 Counting individuals by ordering of events in MOD

MOD_order
             Interval: MD -> R1                 Interval: no MD 
                            139                             354 
             Interval: R1 -> MD       Screen-Detected: MD -> R1 
                            262                             318 
         Screen-Detected: no MD Screen-Detected: R1 -> BC -> MD 
                            395                               1 
Screen-Detected: R1 -> MD -> BC 
                            471

4.4 Summary of time differences

Summary of time from R1 to breast cancer for Interval cancers
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.07671  4.21644  7.64658  7.19002 10.19973 13.73771 

Summary of time from R1 to mammogram for Interval cancers
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-8.5765 -0.4645  1.0137  1.6663  3.9781  9.7514     354 

Summary of time from mammogram to breast cancer for Interval cancers
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.326   1.759   2.660   3.711   5.164  14.192     354 

Summary of time from R1 to breast cancer for Screen-Detected cancers
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.03014  3.43989  6.42740  6.42287  9.37260 13.86849 

Summary of time from R1 to mammogram for Screen-Detected cancers
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-9.6110 -0.8253  0.6270  1.1635  2.7815  9.6740     395 

Summary of time from mammogram to breast cancer for Screen-Detected cancers
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-0.1479  2.8445  3.0219  3.8781  5.6910 14.5452     395

4.5 Statistics for paper

Summary of mammographic density for IBC
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.50   15.20   25.90   29.03   41.70   80.60     354 
Summary of mammographic density for SDBC
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.50   10.30   20.30   23.52   34.40   79.30     395 
Percentage of missing densities
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.3861  1.0000  1.0000