ORAU TEAM

Dose Reconstruction

Project for NIOSH

Oak Ridge Associated Universities I Dade Moeller & Associates I MJW Corporation

Page 1 of 161

Document Title:

Use of Claimant Datasets for Coworker Modeling

Document Number: ORAUT-OTIB-0075

Revision: 00

Effective Date: 05/25/2009

Type of Document OTIB

Supersedes: None

Subject Expert(s): Janice P. Watkins

Site Expert(s): None

Approval: Signature on File Approval Date: 05/18/2009

Thomas R. LaBone, Document Owner

Concurrence: Signature on File Concurrence Date: 05/12/2009

John M. Byrne, Objective 1 Manager

Concurrence: Signature on File Concurrence Date: 05/12/2009

Edward F. Maher, Objective 3 Manager

Concurrence: Signature on File Concurrence Date: 05/21/2009

Kate Kimpan, Project Director

Approval: Signature on File Approval Date: 05/25/2009

James W. Neton, Associate Director for Science

 New Total Rewrite Revision Page Change

FOR DOCUMENTS MARKED AS A TOTAL REWRITE, REVISION, OR PAGE CHANGE, REPLACE THE PRIOR 

REVISION AND DISCARD / DESTROY ALL COPIES OF THE PRIOR REVISION. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 2 of 161

PUBLICATION RECORD

EFFECTIVE 

DATE

REVISION

NUMBER DESCRIPTION

05/25/2009 00 New document to present the results of a study of Y-12 Plant worker 

and claimant data to determine the feasibility of using claimant data 

in place of complete coworker data for reconstruction of doses at 

sites that do not have complete coworker data. Includes Mound and 

SRS datasets. Incorporates formal internal and NIOSH review 

comments. Training required: As determined by the Objective

Manager. Initiated by Thomas R. LaBone.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 3 of 161

TABLE OF CONTENTS

SECTION TITLE

Acronyms and Abbreviations ...............................................................................................................

PAGE

5

1.0 Introduction ..............................................................................................................................6

2.0 Purpose ...................................................................................................................................7

3.0 Description of the Y-12 Data ....................................................................................................8

4.0 Modeling of Data....................................................................................................................14

5.0 Random Samples ..................................................................................................................19

5.1 Theory and Methods...................................................................................................19

5.2 Y-12 Results...............................................................................................................21

6.0 Mound Plutonium Urine Bioassay Data and Results .............................................................. 22

7.0 SRS Tritium Dose Data and Results ...................................................................................... 29

8.0 Summary and Conclusions .................................................................................................... 33

9.0 Attributions and Annotations .................................................................................................. 34

References ........................................................................................................................................35

ATTACHMENT A, LOGNORMAL PROBABILITY PLOTS, COMPLETE Y-12 DATASET.................. 36

ATTACHMENT B, LOGNORMAL PROBABILITY PLOTS, CLAIMANT Y-12 DATASET ................... 56

ATTACHMENT C, 95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS.....................................................76

ATTACHMENT D, LOGNORMAL PROBABILITY PLOTS, COMPLETE MOUND DATASET............ 96

ATTACHMENT E, LOGNORMAL PROBABILITY PLOTS, CLAIMANT MOUND DATASET ........... 112

ATTACHMENT F, 95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS............................................ 128

ATTACHMENT G, LOGNORMAL PROBABILITY PLOTS, COMPLETE SRS DATASET................ 144

ATTACHMENT H, LOGNORMAL PROBABILITY PLOTS, CLAIMANT SRS DATASET ................. 150

ATTACHMENT I, 95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS.................................................. 156
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 4 of 161

LIST OF TABLES

TABLE TITLE

3-1 Summary of samples and workers in claimant and complete Y-12 datasets by year..............

PAGE

13

4-1 Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for Y-12 .................................................................. 16

5-1 Summary of the percentiles for the claimant and complete Y-12 datasets for 1953................ 21

6-1 Summary of samples and workers in claimant and complete Mound datasets by year........... 23

6-2 Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for Mound............................................................... 28

7-1 Summary of workers in claimant and complete SRS datasets by year ................................... 31

7-2 Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for SRS .................................................................. 31

LIST OF FIGURES

FIGURE TITLE

3-1 Number of samples submitted per year by all Y-12 workers.....................................................

PAGE

8

3-2 Number of Y-12 workers monitored in each year ..................................................................... 9

3-3 Empirical probability density plot of samples per Y-12 worker................................................ 10

3-4 Number of samples submitted per year by Y-12 claimants..................................................... 11

3-5 Number of Y-12 claimants monitored in each year................................................................. 12

3-6 Empirical probability density plot of samples per Y-12 claimant.............................................. 12

4-1 Lognormal probability plot of the complete Y-12 dataset for 1953 .......................................... 14

4-2 Lognormal probability plot of the claimant Y-12 dataset for 1953 ........................................... 15

4-3 Comparison of 50th percentiles for the complete and claimant Y-12 datasets........................ 17

4-4 Comparison of 84th percentiles for the complete and claimant Y-12 datasets........................ 17

5-1 Bootstrap analysis of slope and intercept for 1953 complete Y-12 dataset with a sample 

of 731 workers .......................................................................................................................21

6-1 Number of samples submitted per year by all Mound workers ............................................... 24

6-2 Number of Mound workers monitored in each year ................................................................ 24

6-3 Empirical probability density plot of samples per Mound worker............................................. 25

6-4 Number of samples submitted per year by all Mound claimants............................................. 25

6-5 Number of Mound claimants monitored in each year.............................................................. 26

6-6 Empirical probability density plot of samples per Mound claimant .......................................... 26

6-7 Comparison of 50th percentiles for the complete and claimant Mound datasets .................... 27

6-8 Comparison of 84th percentiles for the complete and claimant Mound datasets .................... 27

7-1 Number of SRS workers monitored in each year.................................................................... 30

7-2 Number of SRS claimants monitored in each year................................................................. 30

7-3 Comparison of 50th percentiles for the complete and claimant SRS datasets ........................ 32

7-4 Comparison of 84th percentiles for the complete and claimant SRS datasets ........................ 32
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 5 of 161

ACRONYMS AND ABBREVIATIONS

CER Center for Epidemiologic Research

CSV comma-separated values (file type)

d day

DOE U.S. Department of Energy

dpm disintegrations per minute

EEOICPA Energy Employees Occupational Illness Compensation Program Act of 2000

GM geometric mean

GSD geometric standard deviation

HPRED Health Protection Radiation Exposure Database

NIOSH National Institute for Occupational Safety and Health

ORAU Oak Ridge Associated Universities

ORISE Oak Ridge Institute for Science and Engineering

SRDB Ref ID Site Research Database Reference Identification (number)

SRS Savannah River Site

TIB technical information bulletin

U.S.C. United States Code

§ section or sections
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 6 of 161

1.0

Technical information bulletins (TIBs) are not official determinations made by the National Institute for 

Occupational Safety and Health (NIOSH) but are rather general working documents that provide 

historic background information and guidance to assist in the preparation of dose reconstructions at 

particular sites or categories of sites. They will be revised in the event additional relevant information 

is obtained about the affected site(s). TIBs may be used to assist NIOSH staff in the completion of 

individual dose reconstructions.

INTRODUCTION

In this document, the word “facility” is used as a general term for an area, building, or group of 

buildings that served a specific purpose at a site. It does not necessarily connote an “atomic weapons 

employer facility” or a “Department of Energy (DOE) facility” as defined in the Energy Employees 

Occupational Illness Compensation Program Act of 2000 [EEOICPA; 42 U.S.C. § 7384l(5) and (12)]. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 7 of 161

2.0

In the dose reconstruction process, coworker models are used to assign doses to workers who were 

not monitored at certain times during their employment (ORAUT 2005a). Coworker models are 

typically developed from essentially complete datasets of worker monitoring data. For example, the 

collection of all uranium urine bioassay data from 1950 to 1988 for the workers at the Oak Ridge Y-12 

Plant (Y-12) was used to develop a coworker model for unmonitored workers at Y-12 (ORAUT

2005b). There are sites where complete worker monitoring datasets are not available to develop 

coworker models. Complete datasets are not available at these sites because not all of the necessary 

monitoring results have been transcribed from hard-copy records to an electronic format suitable for 

analysis. Because of the considerable cost and effort that would be involved with creating complete 

datasets, there is interest in developing coworker models from a sample of the complete dataset. 

PURPOSE

An ideal sample would consist of randomly selected workers from the entire monitored worker 

population. However, random sampling is resource intensive in this situation. One readily obtainable 

sample of the complete dataset for a site is the bioassay data of the workers (energy employees) who 

are named in a claim under EEOICPA. These workers are referred to in this paper as the claimants 

for a site. Strictly speaking, claimant datasets are samples of convenience rather than random 

samples. Nevertheless, claimant datasets are random enough for this purpose in the same sense 

that pseudo-random computer-generated numbers are adequate for most applications that require 

random numbers. Therefore, the purpose of this document is to present the results of a study to 

determine if claimant datasets can be treated as random samples from the complete datasets from 

which they were drawn for the purpose of developing coworker models. The study consisted of taking 

sites where well-defined complete datasets are available and comparing these datasets with the 

claimant datasets. Such complete datasets are available for the following sites, data types, and 

periods:

Y-12 Plant Uranium urine bioassay 1950–1988

Mound Laboratory Plutonium urine bioassay 1960–1990

Savannah River Site (SRS) Tritium dose 1991–2001

The first step in the study was a direct, descriptive comparison of annual 50th and 84th percentiles 

that were obtained from slopes and intercepts of lognormal probability models for the complete and 

the claimant datasets1

Sections 3 to 5 explain this logic in detail using the Y-12 data and present the Y-12 results. 

Sections 6 and 7 briefly discuss the data and present results for the Mound Laboratory and SRS, 

respectively. 

. Then a bootstrap analysis was used to determine whether the claimant 

dataset behaves like a random sample from the complete dataset in relation to the values of the slope 

and intercept of the fits to the data. If there is no significant difference between the claimant and 

random datasets, it is assumed that the same coworker model would be obtained, on average, from 

the claimant dataset as from an actual random sample. 

 

1 The statistical software R (RDCT 2008) was used for all calculations. The R codes are available as SRDB Ref ID 63507.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 8 of 161

3.0

Two Y-12 datasets are discussed in this section. The first is the complete dataset, which is all of the 

uranium urine bioassay data for all monitored Y-12 workers from 1950 to 1988. This dataset

DESCRIPTION OF THE Y-12 DATA

2 was 

developed by the Oak Ridge Institute for Science and Engineering (ORISE) Center for Epidemiologic 

Research (CER) for use in epidemiology studies (Watkins et al. 1993) and has undergone detailed 

verification of its integrity. A member of the complete dataset is referred to as a worker. There are 

7537 workers3

1950 1954 1958 1962 1966 1970 1974 1978 1982 1986

Year

Number of Uranium Results per Year

0 10000 20000 30000 40000 50000

in the complete dataset who submitted a total of 467757 uranium urine samples. The 

year-by-year breakdowns of the total number of submitted samples and total number of monitored

workers are shown in Figures 3-1 and 3-2, respectively.

Figure 3-1. Number of samples submitted per year by all 

Y-12 workers. 

The total number of submitted samples per worker ranged from a minimum of 1 to a maximum of 875 

in the 39-year monitoring period; the median was 18 samples. An empirical probability density plot4

 

2 The complete dataset was extracted from the ORISE CER database and supplied in 10 separate comma-separated values 

(CSV) files.

of 

the samples per worker is shown in Figure 3-3. The curve in the plot is a probability density function, 

which means that the area under the curve is equal to 1 and the area under the curve between two 

points a and b on the curve is the fraction of workers who submitted between a and b samples. As 

can be clearly seen in Figure 3-3, the distribution of samples per worker is skewed to the high end. 

This means a randomly selected worker from this dataset could have as few as 1 sample or as many 

3 Five workers with extremely high urine results were excluded from the complete dataset because their data inflated the 

variance of the complete dataset to the extent that the claimant dataset would always agree with the complete dataset.

4 See Section 5.6 of Modern Applied Statistics with S (Venables and Ripley 2004) for a discussion of empirical 

(nonparametric) probability density plots. These plots are smoothed representations of the underlying data and 

therefore show a small probability of negative samples.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 9 of 161

as 875, but it is more likely the worker would have submitted somewhere around 18 samples than 800 

samples.

1950 1954 1958 1962 1966 1970 1974 1978 1982 1986

Year

Number of Workers per Year

0 500 1000 1500 2000 2500

Figure 3-2. Number of Y-12 workers monitored in each 

year. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 10 of 161

0 200 400 600 800

0.000 0.005 0.010 0.015 0.020

Total Number of Samples per Worker

Density

Figure 3-3. Empirical probability density plot of samples 

per Y-12 worker. 

The second dataset5 consists of all the uranium urine bioassay data for Y-12 workers who were 

monitored at any time during the period from 1950 to 1988 and who are named as an energy 

employee in a claim under EEOICPA. For brevity, this dataset is referred to as the claimant dataset, 

and a member of the claimant dataset is referred to as a claimant. Note that this is not the same 

meaning for claimant as used in EEOICPA6

The claimant dataset is a subset of the complete dataset; that is, all of the claimants are workers. A 

total of 731 claimants

. 

7

 

5 The claimant data were extracted from the ORISE CER database and supplied as one CSV file that was separate and 

distinct from those for the complete dataset.

submitted 67923 uranium urine samples. The total number of submitted 

samples per claimant ranged from a minimum of 1 to a maximum of 769 in the 39-year monitoring 

period; the median was 46 samples. The year-by-year breakdowns of the number of submitted 

samples and number of monitored claimants are shown in Figure 3-4 and Figure 3-5, respectively. 

The empirical probability density plot of samples per claimant is shown in Figure 3-6. 

6 Under EEOICPA, a claimant is the individual who filed a claim and is seeking compensation. The claimant can be the 

energy employee or a survivor of an energy employee. 

7 Out of 1,971 total claimants for Y-12, 731 have uranium urine bioassay results in the complete dataset and 1,240 do not 

(presumably because they were not monitored).
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 11 of 161

1950 1954 1958 1962 1966 1970 1974 1978 1982 1986

Year

Number of Uranium Results per Year

0 2000 4000 6000 8000

Figure 3-4. Number of samples submitted per year by 

Y-12 claimants. 

A summary of the numbers of samples and workers in the claimant and complete datasets is given in 

Table 3-1. It is worth noting that the ratio of claimants to all workers for a given year (the fourth 

column in Table 3-1) is approximately the same as the ratio of number of claimant-submitted samples 

to the total number of samples in that year (the last column in Table 3-1). This indicates that the 

claimants submitted samples at the same rate as the rest of the workers, which suggests that they 

were not monitored differently than other workers. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 12 of 161

1950 1954 1958 1962 1966 1970 1974 1978 1982 1986

Year

Number of Claimants per Year

0 100 200 300 400

Figure 3-5. Number of Y-12 claimants monitored in each 

year. 

0 200 400 600 800

0.000 0.002 0.004 0.006 0.008

Total Number of Samples per Claimant

Density

Figure 3-6. Empirical probability density plot of samples 

per Y-12 claimant. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 13 of 161

Table 3-1. Summary of samples and workers in claimant and complete Y-12 datasets by year. 

Year

Workers Samples

Complete

dataset

Claimant

dataset Ratio

Complete

dataset

Claimant 

dataset Ratio

1950 169 11 0.07 1127 94 0.08

1951 370 38 0.10 4175 564 0.14

1952 396 46 0.12 13414 1575 0.12

1953 493 63 0.13 14312 1655 0.12

1954 1327 199 0.15 23564 3573 0.15

1955 1244 177 0.14 29160 4417 0.15

1956 1123 186 0.17 30083 4913 0.16

1957 1448 227 0.16 33551 5307 0.16

1958 1805 290 0.16 38848 6028 0.16

1959 2277 376 0.17 47340 7455 0.16

1960 2193 354 0.16 46387 7594 0.16

1961 2120 337 0.16 21216 3168 0.15

1962 2004 307 0.15 19371 2741 0.14

1963 1933 292 0.15 20709 2925 0.14

1964 1924 298 0.15 15013 2279 0.15

1965 1559 256 0.16 6734 1066 0.16

1966 1366 219 0.16 6849 1073 0.16

1967 1191 187 0.16 5369 858 0.16

1968 1181 177 0.15 5676 883 0.16

1969 1417 209 0.15 7161 1085 0.15

1970 892 119 0.13 4107 543 0.13

1971 998 134 0.13 4631 607 0.13

1972 583 95 0.16 2896 436 0.15

1973 763 131 0.17 4520 717 0.16

1974 663 108 0.16 3827 666 0.17

1975 654 108 0.17 2896 535 0.18

1976 658 105 0.16 2896 512 0.18

1977 653 100 0.15 2878 498 0.17

1978 674 98 0.15 2893 474 0.16

1979 823 111 0.13 3141 472 0.15

1980 945 98 0.10 3799 464 0.12

1981 992 100 0.10 4215 401 0.10

1982 1184 82 0.07 5175 377 0.07

1983 1285 78 0.06 4786 292 0.06

1984 1320 77 0.06 5404 284 0.05

1985 1255 67 0.05 5356 218 0.04

1986 1096 47 0.04 5672 212 0.04

1987 957 35 0.04 3958 119 0.03

1988 1312 40 0.03 4648 129 0.03
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4.0

Using the methodology in ORAUT-OTIB-0019, Analysis of Coworker Bioassay Data for Internal Dose 

Assignment (ORAUT 2005a), the complete dataset for 1953 is presented in Figure 4-1 in the form of a 

lognormal probability plot. The year 1953 was chosen as an example because it is one of the years 

that has relatively good agreement between the complete and claimant datasets. The data are fit with 

a straight line and the slope m, intercept b, and linear correlation coefficient R2 that were calculated 

from the fit are given on the plot. The geometric mean (GM) and geometric standard deviation (GSD) 

of the data are: 

MODELING OF DATA

dpm/d6613 6142 GM .ee

.b === (4-1)

.3 83 343.1 GSD ee === m (4-2)

Figure 4-1. Lognormal probability plot of the complete 

Y-12 dataset for 1953. 

The statistics of interest for the coworker model are the 50th and 84th percentiles. The 50th 

percentile of the data is equal to the GM, and the 84th percentile is equal to the product of the GM 

and the GSD: 

• 50th percentile = 13.66 dpm/d

• 84th percentile = 52.29 dpm/d

These are parametric percentiles, which means that they derive from the parameters of the lognormal 

model that is fit to the data rather than the data themselves. The validity of the parametric percentiles 

depends in part on how well a lognormal probability model fits the data. Ultimately, the 50th and 84th

percentiles are modeled by an internal dosimetrist and uranium intake rates are calculated for each 

set of data. The intake rate that is typically calculated from the 50th percentile data is assigned for the 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 15 of 161

period of interest, and the ratio of the 84th-percentile intake rate to the 50th-percentile intake rate 

gives the GSD of the intake.

There are over 14000 urine bioassay results that are represented in Figure 4-1, which is an unusually 

large number of results to model. The model in ORAUT-OTIB-0029, Internal Dosimetry Coworker 

Data for Y-12 (ORAUT 2005b), took advantage of the large dataset by breaking the data down into 

months rather than years, which is useful when applying the intakes to an individual. However, for 

this report the annual data was evaluated. 

The lognormal probability plot for the 1953 claimant dataset is given in Figure 4-2. The relevant 

statistics from this plot are:

dpm/d7713 6232 GM .e . == (4-3)

1.328 GSD e = = 3.77 (4-4)

Figure 4-2. Lognormal probability plot of the claimant Y-12 

dataset for 1953. 

The 50th and 84th percentiles are then:

• 50th percentile = 13.77 dpm/d 

• 84th percentile = 51.98 dpm/d 

The annual lognormal probability plots for complete and claimant datasets for 1950 to 1988 are given 

in Attachments A and B, respectively, and the results of the fits are given in Table 4-1. Graphical 

comparisons of the annual 50th and 84th percentiles for the complete and claimant datasets from 

Table 4-1 are shown in Figures 4-3 and 4-4. In general, there is excellent agreement between the 

percentiles that derive from the complete dataset and those from the claimant dataset.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 16 of 161

Table 4-1. Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for Y-12 . The corresponding plots are in Attachments A and 

B.

Year

Complete

slope

Complete

intercept

Claimant

slope

Claimant

intercept

Complete

50th

percentile

Complete

84th

percentile

Claimant

50th

percentile

Claimant

84th

percentile

1950 1.6215 1.9090 1.2029 0.6286 6.75 34.14 1.88 6.24

1951 1.5994 1.7217 1.5734 1.3214 5.59 27.69 3.75 18.08

1952 1.0897 2.8263 1.0757 2.7016 16.88 50.20 14.90 43.70

1953 1.3426 2.6142 1.3281 2.6228 13.66 52.29 13.77 51.98

1954 1.5665 2.4603 1.5690 2.4342 11.71 56.08 11.41 54.77

1955 1.5465 1.7700 1.5643 1.8130 5.87 27.56 6.13 29.29

1956 1.4653 1.9330 1.4397 1.9308 6.91 29.91 6.89 29.09

1957 1.5518 1.9214 1.5245 1.9083 6.83 32.24 6.74 30.96

1958 1.4826 2.2972 1.4747 2.2587 9.95 43.81 9.57 41.82

1959 1.5065 2.2805 1.4821 2.3356 9.78 44.13 10.34 45.50

1960 1.5350 1.8949 1.5459 1.9289 6.65 30.87 6.88 32.29

1961 1.5615 1.6741 1.4966 1.6546 5.33 25.42 5.23 23.36

1962 1.4304 2.3453 1.4191 2.2999 10.44 43.63 9.97 41.22

1963 1.3881 1.9952 1.4020 1.9503 7.35 29.47 7.03 28.57

1964 1.4890 1.3073 1.5135 1.2786 3.70 16.38 3.59 16.31

1965 1.6947 1.3007 1.6693 1.3539 3.67 19.99 3.87 20.56

1966 1.6758 1.6940 1.7764 1.5620 5.44 29.07 4.77 28.17

1967 1.4888 1.7652 1.4861 1.7095 5.84 25.89 5.53 24.42

1968 1.3763 1.9625 1.3093 1.9703 7.12 28.19 7.17 26.56

1969 1.5109 1.5885 1.6410 1.3632 4.90 22.18 3.91 20.17

1970 1.2354 2.2760 1.2283 2.1886 9.74 33.49 8.92 30.47

1971 1.2082 2.2166 1.2266 2.1807 9.18 30.72 8.85 30.18

1972 1.0933 2.5560 1.1360 2.4023 12.88 38.45 11.05 34.41

1973 1.1803 2.6400 1.1592 2.6621 14.01 45.62 14.33 45.67

1974 1.1264 2.3589 1.0790 2.4090 10.58 32.63 11.12 32.72

1975 1.1407 2.3286 1.1124 2.3718 10.26 32.11 10.72 32.60

1976 1.2762 2.1444 1.3575 2.0913 8.54 30.59 8.10 31.46

1977 1.1862 2.3546 1.2050 2.4402 10.53 34.50 11.48 38.29

1978 1.4275 1.8822 1.4318 1.9348 6.57 27.38 6.92 28.98

1979 1.4511 1.7151 1.3193 1.7743 5.56 23.72 5.90 22.05

1980 1.5264 1.7382 1.5066 1.6421 5.69 26.17 5.17 23.31

1981 1.5451 1.6040 1.5781 1.4832 4.97 23.32 4.41 21.35

1982 1.4890 1.5554 1.6356 1.4665 4.74 21.00 4.33 22.25

1983 1.5642 1.3947 1.5739 1.1822 4.03 19.28 3.26 15.74

1984 1.7090 1.2271 2.0629 1.1379 3.41 18.84 3.12 24.55

1985 1.8115 0.6876 1.8104 0.7032 1.99 12.17 2.02 12.35

1986 1.9661 0.5077 1.8868 0.3159 1.66 11.87 1.37 9.05

1987 1.7238 0.9503 1.5815 0.7263 2.59 14.50 2.07 10.05

1988 1.7764 0.8923 1.8287 0.3489 2.44 14.42 1.42 8.83
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 17 of 161

0

2

4

6

8

10

12

14

16

18

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990

Year

50th Percentile Uranium in Urine (dpm/day)

Complete

Claimant

Figure 4-3. Comparison of 50th percentiles for the complete and claimant Y-12 

datasets. 

0

10

20

30

40

50

60

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990

Year

84th Percentile Uranium in Urine (dpm/day)

Complete

Claimant

Figure 4-4. Comparison of 84th percentiles for the complete and claimant Y-12 

datasets. 

In this specific case (i.e., for Y-12 uranium urine bioassay from 1950 to 1988) the sample consisting of 

the claimant dataset gives estimates of slope and intercept that appear to agree quite well with the 

slope and intercepts of the complete dataset. This raises the question of whether the claimant 

dataset can be expected to give good estimates of the slope and intercept for all sites, radionuclides, 

and years. 

To answer this question, consider the situation in which neither the complete dataset nor the claimant 

dataset are available in a database. The urine bioassay data for all 7537 workers could be entered 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 18 of 161

into a database and then used to calculate the slopes and intercepts for the complete dataset for each 

year. This approach requires the most effort, but it would give the best available answer. 

Another approach that requires less effort is to pick a number of the workers as a sample

Given that the slopes and intercepts of the sample are unlikely to be exactly equal to the slopes and 

intercepts of the complete dataset, it is desirable that:

and 

calculate the slopes and intercepts from the sample. The ideal result would be that the slopes and 

intercepts of the sample would be the same as the slopes and intercepts of the complete dataset. 

However, because each sample contains only part of the complete dataset, the slopes and intercepts 

from a sample will almost certainly vary from those from the complete dataset and from another 

sample. This variation is referred to as the sampling error. 

• On average, the slopes and intercepts of the sample are equal to the slopes and intercepts 

from the complete dataset.

• The sampling error is acceptably small.

Another way of saying the first bullet is that the slopes and intercepts of the sample should be an 

unbiased estimate of the slopes and intercepts of the complete dataset. Unbiased estimates of the 

slopes and intercepts can be obtained from properly collected random samples, and the likely size of 

the sampling error decreases as the size of this random sample increases. Therefore, to generalize 

the conclusion that the claimant dataset is useful for estimating the slopes and intercepts of the 

complete dataset, it must be shown that the claimant dataset can be treated as if it were a random 

sample.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 19 of 161

5.0

The claimant datasets are best described as samples of convenience; that is, they were not 

constructed through a designed random sampling of the complete dataset. Nevertheless, this section 

shows that, in general, claimant datasets can be used as if they were true random samples from the 

complete dataset. Verifying that the claimant datasets behave like true random samples is important

because coworker models that are based on random samples will, on average, provide the same 

coworker models that would have been derived from the complete dataset. Random sampling 

provides a statistical basis for stating that the sample is representative of the complete dataset and 

eliminates selection bias. Therefore, if the claimant dataset is large enough and has properties very 

much like those of a truly random sample from the complete dataset then, in relation to the estimates 

of the lognormal parameters, meaningful statements can be made about the sampling errors that are 

associated with the percentiles without having access to the complete dataset. 

RANDOM SAMPLES

5.1 THEORY AND METHODS

There are n = 7537 monitored workers in the complete dataset, each of which has a mostly unique set 

of bioassay data. The slopes and intercepts of the complete dataset are the true slopes and 

intercepts, and they are the parameters to be estimated from a sample of the complete dataset (the 

claimant dataset is such a sample). For example, the slope and intercept of the complete dataset for 

1953 is (1.343, 2.614). The claimant dataset of k = 731 workers (some of whom do not have results 

in 1953) has a slope and intercept of (1.328, 2.623) for 1953. If the complete dataset was not 

available, the slope and intercept of the claimant dataset for 1953 would be the logical choice to be 

the estimate of the complete dataset slope and intercept for 1953. The natural question at this point is 

how good an estimate is the slope and intercept of the claimant data; that is, how close is it to the 

slope and intercept of the complete dataset? 

One way to answer this question begins with an examination of the distribution of annual slopes and 

intercepts that can be calculated from all possible samples of k = 731 claimants of the n = 7537 

monitored workers. There are:

8

7537! 1040 2.8189 10

731!(7537 731)!

C = = ⋅ − (5-1)

equally probable ways of selecting 731 different workers from a population of 7537 workers. Each of 

the C combinations of the 731 workers can potentially have a different collection of bioassay results9

with its own slope and intercept for each year. Note that only one of these C ways (the claimant 

dataset) was actually observed (realized). In theory, the slope and intercept for each of the C

datasets for each year could be calculated, and the ranges where most (95% for example) of the 

slopes and intercepts occurred could be established. For a given year, the estimated slope and 

intercept would be considered to be statistically

While theoretically possible, the calculations above are not feasible because of the immense number 

of samples of size k = 731 that can be drawn from the complete dataset. However, the 95% range 

the same as the true slope and intercept if the 

estimated slope and intercept fall in the range where 95% of the results would be observed.

 

8 The number of combinations, n choose k or nCk, was calculated with Maxima 5.15.0 (SourceForge 2008). Note that the 

order of the workers does not count and a particular worker only appears once in each sample of 731 workers.

9 Selecting workers rather than bioassay results is an example of clustered sampling.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 20 of 161

can be estimated using the following simulation, which is a type of bootstrap analysis (Efron and 

Tibshirani 1998; Davison and Hinkley 1997): 

1. Assume that there are n workers in the complete dataset and k claimants in the claimant 

dataset, where n > k and the claimant dataset is a subset of the complete dataset. 

2. Calculate the slope and intercept of the lognormal fit to the bioassay results for the claimant 

dataset for each year. Do the same for the complete dataset and plot these two points. 

3. Randomly draw k = 731 workers without replacement10

4. Repeat step 3 m times (e.g., m = 1000 times). Note that each time the experiment is run a 

new set of k bootstrap workers are drawn and new slopes and intercepts are calculated for 

each year. 

from the complete dataset. Fit a 

lognormal model to the bioassay results for the k workers for each year.

5. Plot the m points for each year at their slope x and intercept y coordinates. 

6. Calculate a 95% joint confidence ellipse of all m slopes and intercepts for the bootstrap 

workers in each year.

For example, the results of the bootstrap analysis for k = 731 and m = 1000 for 1953 are shown in 

Figure 5-1, where: 

• The solid red point is the slope and intercept (1.343, 2.614) of the complete dataset.

• The solid blue point is the slope and intercept (1.328, 2.623) of the claimant dataset.

• The open pink points are the slopes and intercepts of the fits to the m random sets of 731 

workers taken from the complete dataset.

• The ellipse is the 95% joint normal confidence ellipse. 

• The numbers in the corners are the full scale of the respective axes in terms of positive and 

negative percentages of the slope or intercept of the complete dataset. 

The desired result is that the slope and intercept of the claimant data are close to the slope and 

intercept of the complete data. One way to gauge how close the two points are is to see if the slope 

and intercept of the claimant data falls in the 95% confidence ellipse of the bootstrap samples. When 

they do, it is concluded that the slope and intercept of the claimant data is statistically

 

10 “Without replacement” means that no workers appear more than once in any given sample of k workers.

no different than 

the slope and intercept of a true random sample of 731 workers from the complete dataset. As can 

be seen in Figure 5-1, there was excellent agreement for 1953. If there is a strong tendency for the 

slopes and intercepts of the claimant data to fall in the 95% confidence ellipse each year, this would 

be evidence to support the assumption that the claimant dataset behaves as if it were a random 

sample from the complete dataset. In addition, it is important to note that two sets of slopes and 

intercepts can be statistically different but practically the same. For example, Table 5-1 lists a 

summary of the percentiles for the claimant and complete datasets for 1953. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 21 of 161

Table 5-1. Summary of the percentiles for the 

claimant and complete Y-12 datasets for 1953. 

Dataset 50th percentile 84th percentile

Claimant 13.77 51.98

Complete 13.66 52.29

Whether or not the percentiles of the claimant dataset for 1953 are different statistically , in 

consideration of how these percentiles are to be used by an internal dosimetrist, there is clearly no 

difference of practical significance. 

Figure 5-1. Bootstrap analysis of slope and intercept for 1953 

complete Y-12 dataset with a sample of 731 workers. 

5.2 Y-12 RESULTS

Based on the discussion in the previous section, the general strategy was to draw 731 unique workers 

at random11

 

11 Sample without replacement.

from the 7537 workers in the complete dataset and determine the slope and intercept of 

the fit. The results of the m = 1000 iterations are presented in scatter plots like the one shown in 

Figure 5-1 for 1953. The results of the fits for 1950 to 1988 are summarized in Table 4-1, and the 

plots are given in Attachment C. The slopes and intercepts of the claimant datasets fall in the 95% 

confidence ellipse for 34 of the 39 years. The slopes and intercepts for 1950, 1951, 1984, 1987, and 

1988 fall outside of the 95% confidence ellipse. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 22 of 161

6.0

For the Mound Laboratory a single plutonium urine bioassay dataset, PURECON, was provided for 

1960 to 1990 along with a list that identified claimants in the dataset. This list was used to extract 

claimants from the complete dataset to construct the claimant dataset.

MOUND PLUTONIUM URINE BIOASSAY DATA AND RESULTS

The PURECON database was established in 1991 by work performed by the University of Lowell 

Research Foundation. This work was undertaken to meet the requirements of DOE Order 5480.11, 

Radiation Protection for Occupational Workers (DOE 1988), which became effective on January 1, 

1989. Mound staff recognized the need to develop more reliable internal dosimetry recordkeeping for 

plutonium; this resulted in the construction of a computer database from the original raw data. 

Subsequent quality assurance and repair work on the PURECON database has been performed (and 

documented) by the MJW Corporation during its work on the Pre-1989 Dose Assessment Project 

(MJW 2002). PURECON is considered to be a complete dataset.

There are 2070 workers in the complete dataset who submitted a total of 53340 plutonium urine

samples. Urine samples that were influenced by chelation therapy were not included in the analysis. 

The year-by-year breakdowns of the total number of submitted samples and total number of 

monitored workers are shown in Figures 6-1 and 6-2, respectively. The total number of submitted 

samples per worker ranged from a minimum of 1 to a maximum of 235 in the 31-year monitoring 

period; the median was 12 samples. An empirical probability density plot of the samples per worker is 

shown in Figure 6-3. 

The claimant dataset is a subset of the complete dataset; that is, all of the claimants are workers. A 

total of 225 claimants submitted 8849 plutonium urine samples. The total number of submitted 

samples per claimant ranged from a minimum of 1 to a maximum of 200 in the 31-year monitoring 

period; the median was 24 samples. The year-by-year breakdowns of the number of submitted 

samples and number of monitored claimants are shown in Figures 6-4 and 6-5, respectively. The 

empirical probability density plot of samples per claimant is shown in Figure 6-6. 

A summary of the number of samples and workers in the claimant and complete datasets is given in 

Table 6-1. A summary of the slopes and intercepts of the fits to the complete and claimant data is 

given in Table 6-2. The lognormal probability plots for the complete and claimant datasets are given 

in Attachments D and E, respectively. 

Following the general procedure established for the Y-12 data, k = 225 unique workers were drawn at 

random without replacement from the n = 2070 workers in the complete dataset and the slope and 

intercept of the fit determined. The results of the m = 1000 iterations of this process are presented in 

scatter plots for 1960 to 1990 in Attachment F. The slopes and intercepts of the claimant datasets fall 

inside the 95% confidence ellipse for 29 out of 31 years. The slopes and intercepts for 1983 and 

1984 fall outside of the 95% confidence ellipses. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 23 of 161

Table 6-1. Summary of samples and workers in claimant and complete Mound 

datasets by year. 

Year

Workers Samples

Complete

Dataset

Claimant

Dataset Ratio

Complete

Dataset

Claimant

Dataset Ratio

1960 88 12 0.14 336 51 0.15

1961 178 30 0.17 802 135 0.17

1962 292 44 0.15 1385 247 0.18

1963 487 76 0.16 2009 345 0.17

1964 662 91 0.14 2625 418 0.16

1965 715 100 0.14 3203 536 0.17

1966 822 121 0.15 3520 602 0.17

1967 781 112 0.14 3611 567 0.16

1968 652 102 0.16 2815 485 0.17

1969 466 75 0.16 2425 422 0.17

1970 605 97 0.16 2716 418 0.15

1971 531 92 0.17 2264 378 0.17

1972 495 85 0.17 2568 470 0.18

1973 505 88 0.17 2177 392 0.18

1974 442 78 0.18 1940 353 0.18

1975 391 72 0.18 1533 297 0.19

1976 363 70 0.19 1134 207 0.18

1977 405 70 0.17 1270 227 0.18

1978 409 76 0.19 1321 227 0.17

1979 447 85 0.19 1188 239 0.20

1980 445 82 0.18 1181 232 0.20

1981 461 83 0.18 1235 246 0.20

1982 440 72 0.16 1198 211 0.18

1983 444 67 0.15 1247 202 0.16

1984 446 66 0.15 1262 174 0.14

1985 461 62 0.13 1325 163 0.12

1986 435 57 0.13 1151 134 0.12

1987 415 55 0.13 818 96 0.12

1988 391 52 0.13 793 103 0.13

1989 430 55 0.13 899 116 0.13

1990 603 69 0.11 1389 156 0.11
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 24 of 161

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990

Year

Number of Plutonium Results per Year

0 1000 2000 3000 4000

Figure 6-1. Number of samples submitted per year by all 

Mound workers. 

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990

Year

Number of Workers per Year

0 200 400 600 800 1000

Figure 6-2. Number of Mound workers monitored in each 

year. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 25 of 161

0 50 100 150 200 250

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035

Total Number of Samples per Worker

Density

Figure 6-3. Empirical probability density plot of samples per 

Mound worker. 

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990

Year

Number of Plutonium Results per Year

0 200 400 600 800

Figure 6-4. Number of samples submitted per year by all 

Mound claimants. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 26 of 161

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990

Year

Number of Claimants per Year

0 20 40 60 80 100 120 140

Figure 6-5. Number of Mound claimants monitored in each 

year. 

0 50 100 150 200

0.000 0.005 0.010 0.015

Total Number of Samples per Claimant

Density

Figure 6-6. Empirical probability density plot of samples per 

Mound claimant. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 27 of 161

0.00

0.05

0.10

0.15

0.20

0.25

1955 1960 1965 1970 1975 1980 1985 1990 1995

Year

50th Percentile Pu in Urine (pCi/day)

Complete

Claimant

Figure 6-7. Comparison of 50th percentiles for the complete and claimant Mound 

datasets. 

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1955 1960 1965 1970 1975 1980 1985 1990 1995

Year

84th Percentile of Pu in Urine (pCi/day)

Complete

Claimant

Figure 6-8. Comparison of 84th percentiles for the complete and claimant Mound 

datasets. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 28 of 161

Table 6-2. Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for Mound. The corresponding plots are in Attachments D 

and E.

Year

Complete

slope

Complete

intercept

Claimant

slope

Claimant

intercept

Complete

50th

Percentile

Complete

84th

Percentile

Claimant

50th

Percentile

Claimant

84th

Percentile

1960 1.7763 -1.5862 1.8095 -2.0435 0.204710 1.209380 0.129575 0.791393

1961 2.1146 -2.5365 1.8071 -3.1797 0.079142 0.655775 0.041599 0.253446

1962 1.6158 -2.6065 1.4565 -2.5661 0.073796 0.371351 0.076833 0.329692

1963 1.8854 -2.7882 1.5736 -2.8320 0.061533 0.405427 0.058897 0.284125

1964 1.2949 -2.1031 1.3343 -1.9731 0.122076 0.445635 0.139021 0.527915

1965 1.6706 -2.3940 1.6368 -2.1236 0.091267 0.485102 0.119601 0.614590

1966 1.3078 -2.5246 1.4308 -2.2846 0.080088 0.296159 0.101819 0.425814

1967 1.4166 -2.6593 1.3872 -2.4689 0.070001 0.288612 0.084677 0.339009

1968 1.4494 -2.8382 1.4152 -2.8119 0.058528 0.249357 0.060091 0.247422

1969 2.1301 -2.8659 1.6939 -2.6241 0.056933 0.479156 0.072503 0.394479

1970 1.6636 -3.6645 1.5592 -3.5080 0.025617 0.135208 0.029957 0.142445

1971 1.7206 -3.7142 1.6911 -3.7395 0.024375 0.136210 0.023766 0.128944

1972 1.7577 -3.7794 1.5781 -3.4781 0.022837 0.132439 0.030865 0.149560

1973 1.7519 -3.6106 1.6661 -3.5463 0.027035 0.155879 0.028831 0.152553

1974 1.5019 -3.2137 1.3124 -2.9578 0.040208 0.180547 0.051931 0.192933

1975 1.9122 -4.2239 1.6938 -3.9566 0.014642 0.099094 0.019129 0.104061

1976 1.6938 -4.0184 1.6080 -4.0822 0.017982 0.097821 0.016870 0.084226

1977 1.6241 -4.0770 1.5031 -3.8166 0.016959 0.086048 0.022003 0.098920

1978 2.0539 -5.1288 1.7285 -4.9391 0.005924 0.046193 0.007161 0.040330

1979 1.9136 -4.8783 1.8700 -4.6350 0.007610 0.051579 0.009706 0.062974

1980 1.6979 -4.2114 1.6884 -3.9503 0.014826 0.080988 0.019249 0.104152

1981 1.6252 -3.8792 1.5381 -3.6495 0.020666 0.104976 0.026004 0.121072

1982 1.7509 -4.3251 1.4625 -3.8362 0.013232 0.076214 0.021575 0.093133

1983 1.9152 -4.6603 1.5766 -3.9017 0.009464 0.064241 0.020207 0.097767

1984 1.7055 -4.6980 1.5008 -4.0907 0.009113 0.050160 0.016728 0.075025

1985 2.2658 -6.0526 2.3053 -5.4686 0.002352 0.022669 0.004217 0.042285

1986 2.1396 -5.6411 1.9829 -5.0589 0.003549 0.030153 0.006353 0.046143

1987 1.8579 -5.3456 2.0961 -5.0362 0.004769 0.030570 0.006498 0.052859

1988 2.0619 -5.5173 2.1948 -4.9160 0.004017 0.031575 0.007329 0.065796

1989 2.3594 -6.1590 2.6004 -5.6553 0.002114 0.022378 0.003499 0.047124

1990 2.5491 -7.0146 2.5931 -6.2503 0.000899 0.011499 0.001930 0.025805
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 29 of 161

7.0

The ideal type of data with which to develop a coworker model is dose data. For example, dose data 

are used to develop external dose coworker models. In the case of external coworker models the 

relevant data, if available, are reported in terms of dose. However, the datasets for most internal dose 

coworker models (such as Y-12 and Mound) consist of urine bioassay data. The required effort to 

convert all of the uranium and plutonium urine bioassay data to doses for each individual is 

prohibitive, so the coworker modeling is performed on the bioassay data. One case where it is 

feasible

SRS TRITIUM DOSE DATA AND RESULTS

12 to convert urine bioassay data to individual doses is for tritium urine bioassay data. 

Therefore, the SRS analysis used tritium dose data rather than tritium urine bioassay data. These 

doses were calculated from the tritium urine bioassay dataset that was extracted from the SRS 

HPRED13

The tritium dose dataset for this analysis is for 1991 to 2001. There are 10711 workers in the 

complete dose dataset and 451 claimants in the claimant dose dataset. The original urine bioassay 

dataset from HPRED contained 521,798 individual bioassay results. It is important to note that while 

an individual might have hundreds of tritium urine samples in a given year, there is only one annual 

dose. 

database, which is considered complete for the period of 1990 onward. 

The year-by-year breakdowns of the total number of monitored workers and claimants are shown in 

Figures 7-1 and 7-2, respectively. A summary of the data in these plots is given in Table 7-1. A 

summary of the slopes and intercepts of the fits to the complete and claimant dose data is given in 

Table 7-2, and plots of these data are given in Figures 7-3 and 7-4. The lognormal probability plots 

for the complete and claimant datasets are given in Attachments G and H, respectively. 

Once again following the general procedure established for the Y-12 data, k = 451 unique workers 

were drawn at random without replacement from the n = 10711 workers in the complete dataset and 

the slope and intercept of the fit determined. The results of the m = 1000 iterations of this process are 

presented in scatter plots for 1991 to 2001 in Attachment I. The slopes and intercepts of all 11 

claimant datasets are within14

 

12 The methodology of ORAUT-OTIB-0011, Technical Information Bulletin: Tritium Calculated and Missed Dose Estimates

(ORAUT 2004), can be implemented in a computer program that is capable of quickly calculating dose from tritium urine 

bioassay datasets without manual intervention.

the 95% confidence ellipses. 

13 Health Protection Radiation Exposure Database. 14 The point is considered to be within the ellipse if it falls on the ellipse.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 30 of 161

1991 1993 1995 1997 1999 2001

Year

Number of Workers per Year

0 1000 2000 3000 4000 5000

Figure 7-1. Number of SRS workers monitored in each year. 

1991 1993 1995 1997 1999 2001

Year

Number of Claimants per Year

0 50 100 150 200 250

Figure 7-2. Number of SRS claimants monitored in each 

year. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 31 of 161

Table 7-1. Summary of workers in claimant and complete 

SRS datasets by year.

Year

Workers in

complete dataset

Workers in

claimant dataset Ratio

1991 4303 212 0.049

1992 3939 195 0.050

1993 3130 161 0.051

1994 1667 74 0.044

1995 1355 60 0.044

1996 1367 57 0.042

1997 1414 59 0.042

1998 1200 47 0.039

1999 953 30 0.031

2000 599 20 0.033

2001 472 16 0.034

Table 7-2. Summary of the slopes and intercepts of a fit to a lognormal probability model and the 

corresponding 50th and 84th percentiles for SRS. The corresponding plots are in Attachments G and 

H.

Year

Complete

slope

Complete

intercept

Claimant

slope

Claimant

intercept

Complete

50th

Percentile

Complete

84th

Percentile

Claimant

50th

Percentile

Claimant

84th

Percentile

1991 0.8152 1.3796 0.8207 1.4210 3.97 8.98 4.14 9.41

1992 0.6738 1.1651 0.6488 1.2351 3.21 6.29 3.44 6.58

1993 0.6499 1.0756 0.6956 1.0538 2.93 5.62 2.87 5.75

1994 0.6639 1.2011 0.6862 1.1921 3.32 6.46 3.29 6.54

1995 0.5997 1.1877 0.5610 1.1159 3.28 5.97 3.05 5.35

1996 0.5460 1.2287 0.4993 1.2180 3.42 5.90 3.38 5.57

1997 0.5025 1.2346 0.4754 1.1480 3.44 5.68 3.15 5.07

1998 0.4789 1.2311 0.4636 1.0961 3.43 5.53 2.99 4.76

1999 0.5014 1.1111 0.4378 1.0928 3.04 5.02 2.98 4.62

2000 0.4347 1.1685 0.6381 1.1710 3.22 4.97 3.23 6.11

2001 0.4514 1.1059 0.3570 1.0784 3.02 4.75 2.94 4.20
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 32 of 161

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1990 1992 1994 1996 1998 2000 2002

Year

50th Percentile Annual Dose (mrem)

Complete

Claimant

Figure 7-3. Comparison of 50th percentiles for the complete and claimant SRS 

datasets.

0

1

2

3

4

5

6

7

8

9

10

1990 1992 1994 1996 1998 2000 2002

Year

84th Percentile Annual Dose (mrem)

Complete

Claimant

Figure 7-4. Comparison of 84th percentiles for the complete and claimant SRS 

datasets.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 33 of 161

8.0

In the dose reconstruction process, coworker models are used to assign doses to workers who were 

not monitored at certain times during their employment. Coworker models of the 50th- and 

84th-percentile bioassay results for specific periods are typically developed from essentially complete 

datasets of worker monitoring data. However, there are sites where complete worker monitoring 

datasets are not available for developing coworker models. Because of the considerable cost and 

effort necessary to create a complete dataset, there is interest in developing coworker models from a 

sample of the complete dataset. There is no technical reason this cannot be carried out provided that 

the sample is selected by a random process and is sufficiently large. Samples that are collected in 

this way provide unbiased estimates of the percentiles of the complete dataset and permit estimation 

of the uncertainty in the percentile estimates.

SUMMARY AND CONCLUSIONS

A sample of the complete dataset that is readily obtainable is the bioassay data of the workers who 

have a claim under EEOICPA that names them as the energy employee (i.e., the claimants for a site 

who comprise the claimant dataset). However, the claimant datasets were not intentionally selected 

by an appropriate sampling process, so it is not clear that they would provide unbiased estimates of 

the percentiles of the complete dataset. It would be ideal to show that the claimant datasets behave 

essentially like a random sample in relation to the parameters that are entered into coworker models. 

The approach taken here to show this was: 

1. Select sites with well-defined claimant and complete datasets.

2. Examine the distribution of slopes and intercepts from randomly selected datasets the same 

size as the claimant dataset that are drawn from the complete dataset.

3. Use this distribution to determine how likely it would be to observe slopes and intercepts like 

those from the claimant dataset.

4. If the slopes and intercepts from the claimant dataset are indistinguishable from those from the 

random samples, then the claimant dataset can be used as a random sample of the complete 

dataset and can be used to develop coworker models.

This proof-of-principle technique was used to show that the claimant datasets from Y-12, Mound, and 

SRS can, in general, be used as if they were random samples for the purpose of estimating the 

slopes and intercepts of lognormal fits to the data. Further, this exercise can be used as a technical 

justification for applying this assumption to other sites for which complete datasets are not available. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 34 of 161

9.0

All information requiring identification was addressed via references integrated into the reference 

section of this document.

ATTRIBUTIONS AND ANNOTATIONS
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 35 of 161

REFERENCES

Efron, B., and R. J. Tibshirani, 1998, An Introduction to the Bootstrap, CRC Press, Boca Raton, 

Florida. 

Davison, A. C., and D. V. Hinkley, 1997, Bootstrap Methods and their Application, Cambridge 

University Press, New York, New York. 

DOE (U.S. Department of Energy), 1988, Radiation Protection for Occupational Workers, Order 

5480.11, Office of Environment, Safety and Health, Washington D.C., December 21. [SRDB 

Ref ID: 8073]

ORAUT (Oak Ridge Associated Universities Team), 2004, Technical Information Bulletin: Tritium 

Calculated and Missed Dose Estimates, ORAUT-OTIB-0011, Rev. 00, Oak Ridge, Tennessee, 

June 29.

ORAUT (Oak Ridge Associated Universities Team), 2005a, Analysis of Coworker Bioassay Data for 

Internal Dose Assignment, ORAUT-OTIB-0019, Rev. 01, Oak Ridge, Tennessee, October 7.

ORAUT (Oak Ridge Associated Universities Team), 2005b, Internal Dosimetry Coworker Data for Y12, ORAUT-OTIB-0029, Rev. 00, Oak Ridge, Tennessee, April 5. 

MJW (MJW Corporation), 2002, Pre-1989 Dose Assessment Project, Phase I Final Report, Volume 1

(Non-SUD Version), Williamsville, New York. [SRDB Ref ID: 8475] 

RDCT (R Development Core Team), 2008, R: A language and environment for statistical computing, 

R Foundation for Statistical Computing, Vienna, Austria. [http://www.R-project.org] 

SourceForge, 2008, Maxima Manual, Mountain View, California. [http://maxima.sourceforge.net]

Venables, W. N., and B. D. Ripley, 2004, Modern Applied Statistics with S, Statistics and Computing, 

Springer, New York, New York.

Watkins, J. P., J. L. Reagan, D. L. Cragle, E. L. Frome, C. M. West, D .J. Crawford-Brown, and W. G. 

Tankersley, 1993, Data Collection, Validation, and Description for the Oak Ridge Nuclear 

Facilities Mortality Study, ORISE 93/J-42, Oak Ridge Institute for Science and Education, Oak 

Ridge, Tennessee, October. [SRDB Ref ID: 10133]

.
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 36 of 161

ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 1 of 20

This attachment presents lognormal probability plots for the complete Y-12 uranium urine bioassay 

dataset for 1950 to 1988. The slope and intercept for the line of best fit to the data are given on the 

plot. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 37 of 161

ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 2 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 3 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 4 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 5 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 6 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 7 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 8 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 9 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 10 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 11 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 12 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 13 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 14 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 15 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 16 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 17 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 18 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 19 of 20
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ATTACHMENT A

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE Y-12 DATASET

Page 20 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 1 of 20

This attachment presents lognormal probability plots for the claimant Y-12 uranium urine bioassay 

dataset for 1950 to 1988. The slope and intercept for the line of best fit to the data are given on the 

plot. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 57 of 161

ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 2 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 3 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 4 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 5 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 6 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 7 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 8 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 9 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 10 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 11 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 12 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 13 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 14 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 15 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 16 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 17 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 18 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 19 of 20
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ATTACHMENT B

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT Y-12 DATASET

Page 20 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 1 of 20

This attachment presents lognormal probability plots for a sample of 731 unique workers that was 

drawn at random without replacement from the 7537 workers in the complete

• The solid red point is the slope and intercept of the complete dataset.

dataset. The slope and 

intercept of the plot for each year from 1950 to 1988 was calculated based on this data. This process 

was repeated m = 1000 times to generate the 95% confidence ellipses. In each plot:

• The solid blue point is the slope and intercept of the claimant dataset.

• The open pink points are the slopes and intercepts of the fits to the m random sets of 731 

workers taken from the complete dataset.

• The ellipse is the 95% joint normal confidence ellipse. 

• The numbers in the corners are the full scale of the respective axes in terms of +/- the slope or 

intercept of the complete dataset. 

These plots are presented to support the idea that the claimant dataset is basically no different than a 

random draw from the complete dataset. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 77 of 161

ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 2 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 3 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 4 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 5 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 6 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 7 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 8 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 9 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 10 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 11 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 12 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 13 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 14 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 15 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 16 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 17 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 18 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 19 of 20
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ATTACHMENT C

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE Y12 DATASET, 731 WORKERS

Page 20 of 20
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 1 of 16

This attachment presents lognormal probability plots for the complete Mound plutonium urine 

bioassay dataset for 1960 to 1990. The slope and intercept for the line of best fit to the data are given 

on the plot. 
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 2 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 3 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 4 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 5 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 6 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 7 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 8 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 9 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 10 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 11 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 12 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 13 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 14 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 15 of 16
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ATTACHMENT D

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE MOUND DATASET

Page 16 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 1 of 16

This attachment presents lognormal probability plots for the claimant Mound plutonium urine bioassay 

dataset for 1960 to 1990. The slope and intercept for the line of best fit to the data are given on the 

plot. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 113 of 161

ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 2 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 3 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 4 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 5 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 6 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 7 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 8 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 9 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 10 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 11 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 12 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 13 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 14 of 16
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ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 15 of 16
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 127 of 161

ATTACHMENT E

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT MOUND DATASET

Page 16 of 16
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 128 of 161

ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 1 of 16

This attachment presents lognormal probability plots for a sample of 225 unique workers that was 

drawn at random without replacement from the 2070 workers in the complete

• The solid red point is the slope and intercept of the complete dataset.

dataset. The slope and 

intercept of the plot for each year from 1960 to 1990 was calculated based on this data. This process 

was repeated m = 1000 times to generate the 95% confidence ellipses. In each plot:

• The solid blue point is the slope and intercept of the claimant dataset.

• The open pink points are the slopes and intercepts of the fits to the m random sets of 225 

workers taken from the complete dataset.

• The ellipse is the 95% joint normal confidence ellipse. 

• The numbers in the corners are the full scale of the respective axes in terms of +/- the slope or 

intercept of the complete dataset. 

These plots are presented to support the idea that the claimant dataset is basically no different than a 

random draw from the complete dataset. 
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 129 of 161

ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 2 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 3 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 4 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 5 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 6 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 7 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 8 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 9 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 10 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 11 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 12 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 13 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 14 of 16
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 142 of 161

ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 15 of 16
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ATTACHMENT F

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE MOUND DATASET, 225 WORKERS

Page 16 of 16
Document No. ORAUT-OTIB-0075 Revision No. 00 Effective Date: 05/25/2009 Page 144 of 161

ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

Page 1 of 6 

This attachment presents lognormal probability plots for the complete SRS tritium dose dataset for 

1991 to 2001. The slope and intercept for the line of best fit to the data are given on the plot. 
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ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

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ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

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ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

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ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

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ATTACHMENT G

LOGNORMAL PROBABILITY PLOTS, 

COMPLETE SRS DATASET

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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

Page 1 of 6 

This attachment presents lognormal probability plots for the claimant SRS tritium dose dataset for 

1991 to 2001. The slope and intercept for the line of best fit to the data are given on the plot. 
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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

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ATTACHMENT H

LOGNORMAL PROBABILITY PLOTS, 

CLAIMANT SRS DATASET

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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

Page 1 of 6 

This attachment presents lognormal probability plots for a sample of 451 unique workers that was 

drawn at random without replacement from the 10711 workers in the complete

• The solid red point is the slope and intercept of the complete dataset.

dataset. The slope 

and intercept of the plot for each year from 1991 to 2001 was calculated based on this data. This 

process was repeated m = 1000 times to generate the 95% confidence ellipses. In each plot:

• The solid blue point is the slope and intercept of the claimant dataset.

• The open pink points are the slopes and intercepts of the fits to the m random sets of 451 

workers taken from the complete dataset.

• The ellipse is the 95% joint normal confidence ellipse. 

• The numbers in the corners are the full scale of the respective axes in terms of +/- the slope or 

intercept of the complete dataset. 

These plots are presented to support the idea that the claimant dataset is basically no different than a 

random draw from the complete dataset.
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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

Page 2 of 6 
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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

Page 3 of 6 
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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

Page 4 of 6 
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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

Page 5 of 6 
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ATTACHMENT I

95% CONFIDENCE ELLIPSES FOR SLOPE AND INTERCEPT, 

COMPLETE SRS DATASET, 451 WORKERS

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