Biomedical subjects

D Pee

Publications and source records attributed to D Pee.

At least 19 recordsLinked to original sources

PubMed2001

Pseudo-likelihood estimates of the cumulative risk of an autosomal dominant disease from a kin-cohort study.

Wacholder et al. [1998: Am J Epidemiol 148:623-629] and Struewing et al. [1997: N Engl J Med 336:1401-1408] have recently proposed a design called the kin-cohort design to estimate the probability of developing disease (penetrance) associated with an autosomal dominant gene. In this design, volunteers (probands) agree to be genotyped and one also determines the disease history (phenotype) of first-degree relatives of the proband. They used this design to estimate that the chance of developing breast cancer by age 70 in Ashkenazi Jewish women who carried mutations of the genes BRCA1 or BRCA2 was 0.56, a figure that was lower than previously estimated from highly affected families. The method that they used to estimate the cumulative risk of breast cancer, while asymptotically correct, does not necessarily produce monotone estimates in small samples. To obtain monotone, weakly parametric estimates, we consider separate piecewise exponential models for carriers and non-carriers. As the number of intervals on which constant hazards are assumed increases, however, the maximum likelihood score equations become unstable and difficult to solve. We, therefore, developed alternative pseudo-likelihood procedures that are readily solvable for piecewise exponential models with many intervals. We study these techniques through simulations and a re-analysis of a portion of the data used by Struewing et al. [1997] and discuss possible extensions.

Adolescent↗

PubMed2000

Score tests for familial correlation in genotyped-proband designs.

In the genotyped-proband design, a proband is selected based on an observed phenotype, the genotype of the proband is observed, and then the phenotypes of all first-degree relatives are obtained. The genotypes of these first-degree relatives are not observed. Gail et al. [(1999) Genet Epidemiol] discuss likelihood analysis of this design under the assumption that the phenotypes are conditionally independent of one another given the observed and unobserved genotypes. Li and Thompson [(1997) Biometrics 53:282-293] give an example where this assumption is suspect, thus suggesting that it is important to develop tests for conditional independence. In this paper, we develop a score test for the conditional independence assumption in models that might include covariates or observation of genotypes for some of the first degree relatives. The problem can be cast more generally as one of score testing in the presence of missing covariates. A standard analysis would require specifying a distribution for the covariates, which is not convenient and could lead to a lack of model-robustness. We show that by considering a natural conditional likelihood, and basing the score test on it, a simple analysis results. The methods are applied to a study of the penetrance for breast cancer of BRCA1 and BRCA2 mutations among Ashkenazi Jews.

Alleles↗

PubMed1999

Validation studies for models projecting the risk of invasive and total breast cancer incidence.

BACKGROUND: In 1989, Gail and colleagues developed a model for estimating the risk of breast cancer in women participating in a program of annual mammographic screening (designated herein as model 1). A modification of this model to project the absolute risk of developing only invasive breast cancer is referred to herein as model 2. We assessed the validity of both models by employing data from women enrolled in the Breast Cancer Prevention Trial. METHODS: We used data from 5969 white women who were at least 35 years of age and without a history of breast cancer. These women were in the placebo arm of the trial and were screened annually. The average follow-up period was 48.4 months. We compared the observed number of breast cancers with the predicted numbers from the models. RESULTS: In terms of absolute risk, the ratios of total expected to observed numbers of cancers (95% confidence intervals [CIs]) were 0.84 (0. 73-0.97) for model 1 and 1.03 (0.88-1.21) for model 2, respectively. Within the age groups of 49 years or less, 50-59 years, and 60 years or more, the ratios of expected to observed numbers of breast cancers (95% CIs) for model 1 were 0.91 (0.73-1.14), 0.96 (0.73-1. 28), and 0.66 (0.52-0.86), respectively. Thus, model 1 underestimated breast cancer risk in women more than 59 years of age. For model 2, the risk ratios (95% CIs) were 0.93 (0.72-1.22), 1.13 (0.83-1.55), and 1.05 (0.80-1.41), respectively. Both models exhibited a tendency to overestimate risk for women classified in the higher quintiles of predicted 5-year risk and to underestimate risk for those in the lower quintiles of the same. CONCLUSION: Despite some limitations, these methods provide useful information on breast cancer risk for women who plan to participate in an annual mammographic screening program.

Age Factors↗

PubMed1999

Survival after breast cancer in Ashkenazi Jewish BRCA1 and BRCA2 mutation carriers.

BACKGROUND: Studies of survival following breast and ovarian cancers in BRCA1 and/or BRCA2 mutation carriers have yielded conflicting results. We undertook an analysis of a community-based study of Ashkenazi Jews to investigate the effect of three founder mutations in BRCA1 and BRCA2 on survival among patients with breast or ovarian cancer. METHODS: We collected blood samples and questionnaire data from 5318 Ashkenazi Jewish volunteers. The blood samples were tested for 185delAG (two nucleotide deletion) and 5382insC (single nucleotide insertion) mutations in BRCA1 and the 6174delT (single nucleotide deletion) mutation in BRCA2. To estimate survival differences in the affected relatives according to their BRCA1 and/or BRCA2 mutation carrier status, we devised and applied a novel extension of the kin-cohort method. RESULTS: Fifty mutation carriers reported that 58 of their first-degree relatives had been diagnosed with breast cancer and 10 with ovarian cancer; 907 noncarriers reported 979 first-degree relatives with breast cancer and 116 with ovarian cancer. Kaplan-Meier estimates of median survival after breast cancer were 16 years (95% confidence interval [CI] = 11-40) in the relatives of carriers and 18 years (95% CI = 15-22) in the relatives of noncarriers, a difference that was not statistically significant (two-sided P = .87). There was also no difference in survival times among the 126 first-degree relatives with ovarian cancer. We found no survival difference between patients with breast or ovarian cancer who were inferred carriers of BRCA1 and/or BRCA2 mutations and noncarriers. CONCLUSIONS: Carriers of BRCA1 and BRCA2 mutations appeared to have neither better nor worse survival prognosis.

Adult↗

PubMed1999

Designing studies to estimate the penetrance of an identified autosomal dominant mutation: cohort, case-control, and genotyped-proband designs.

One can obtain population-based estimates of the penetrance of a measurable mutation from cohort studies, from population-based case-control studies, and from genotyped-proband designs (GPD). In a GPD, we assume that representative individuals (probands) agree to be genotyped, and one then obtains information on the phenotypes of first-degree relatives. We also consider an extension of the GPD in which a relative is genotyped (GPDR design). In this paper, we give methods and tables for determining sample sizes needed to achieve desired precision for penetrance estimates from such studies. We emphasize dichotomous phenotypes, but methods for survival data are also given. In an example based on the BRCA1 gene and parameters given by Claus et al. [(1991) Am J Hum Genet 48:232-242], we find that similar large numbers of families need to be studied using the cohort, case-control, and GPD designs if the allele frequency is known, though the GPDR design requires fewer families, and, if one can study mainly probands with disease, the GPD design also requires fewer families. If the allele frequency is not known, somewhat larger sample sizes are required. Surprisingly, studies with mixtures of families of affected and non-affected probands can sometimes be more efficient than studies based exclusively on affected probands when the allele frequency is unknown. We discuss the feasibility and validity of these designs and point out that GPD and GPDR designs are more susceptible to a bias that results when the tendency for an individual to volunteer to be a proband or to be a subject in a cohort or case-control study depends on the phenotypes of his or her relatives.

Bias↗

PubMed1999

Increased risk of hepatocellular carcinoma in male hepatitis B surface antigen carriers with chronic hepatitis who have detectable urinary aflatoxin metabolite M1.

We followed 145 men with chronic hepatitis B virus (HBV) hepatitis for 10 years to determine whether exposure to aflatoxin, or concomitant exposure to hepatitis C virus (HCV), or family history of hepatocellular carcinoma (HCC) increased the risk of developing HCC. We collected 8 monthly urine samples before beginning follow-up and pooled them to detect aflatoxin metabolite M1 (AFM1). AFM1 was detected in 78 (54%) of the subjects. The risk of HCC was increased 3.3-fold (with a 95% confidence interval of 1.2-8.7) in those with detectable AFM1 (above 3.6 ng/L). This relative risk was adjusted for age and for HCV status. The attributable risk from exposure to detectable AFM1 was 0.553 (0.087, 0.94). The relative risk of fatal cirrhosis for those with elevated AFM1 was 2.8 (0.6, 14.3), and the odds of having a persistently elevated alanine transaminase (ALT) were 2.5-fold greater in those with detectable AFM1 (P =.007). Concomitant infection with HCV increased the risk of HCC 5.8-fold (2. 0-17), adjusted for age and AFM1 status. A family history of HCC increased the risk of HCC 5.6-fold, adjusted for age and AFM1. Four men with detectable AFM1 and HCC all had missense mutation in codon 249 of the p53 gene in cancer tissues. This study shows that exposure to AFM1 can account for a substantial part of the risk of HCC in men with chronic HBV hepatitis and adds importantly to the evidence that HCV and family history of HCC increase the risk of HCC in men with chronic HBV hepatitis.

Adolescent↗

PubMed1999

Kin-cohort designs for gene characterization.

BACKGROUND: In the kin-cohort design, a volunteer with or without disease (the proband) agrees to be genotyped, and one obtains information on the history of a disease in first-degree relatives of the proband. From these data, one can estimate the penetrance of an autosomal dominant gene, and this technique has been used to estimate the probability that Ashkenazi Jewish women with specific mutations of BRCA1 or BRCA2 will develop breast cancer. METHODS: We review the advantages and disadvantages of the kin-cohort design and focus on dichotomous outcomes, although a few results on time-to-disease onset are presented. We also examine the effects of violations of assumptions on estimates of penetrance. We consider selection bias from preferential sampling of probands with heavily affected families, misclassification of the disease status of relatives, violation of Hardy-Weinberg equilibrium, violation of the assumption that family members' phenotypes are conditionally independent given their genotypes, and samples that are too small to ensure validity of asymptotic methods. RESULTS AND CONCLUSIONS: The kin-cohort design has several practical advantages, including comparatively rapid execution, modest reductions in required sample sizes compared with cohort or case-control designs, and the ability to study the effects of an autosomal dominant mutation on several disease outcomes. The design is, however, subject to several biases, including the following: selection bias that arises if a proband's tendency to participate depends on the disease status of relatives, information bias from inability of the proband to recall the disease histories of relatives accurately, and biases that arise in the analysis if the conditional independence assumption is invalid or if samples are too small to justify standard asymptotic approaches.

Alleles↗

PubMed1998

The kin-cohort study for estimating penetrance.

A cross-sectional study may be more feasible than a cohort or case-control study for examining the effect of a genetic mutation on cancer penetrance outside of cancer families. The kin-cohort design uses volunteer probands selected from a population with a relatively high frequency of the mutations of interest. By considering the cancer risk in first-degree relatives of mutation-positive and -negative probands as a weighted average of the risk in carriers and noncarriers, with weights calculated assuming a known mode of inheritance, one can infer the penetrance of the mutations. The estimates of penetrance by age 70 years for three specific mutations in the BRCA1 and BRCA2 genes common among Ashkenazi Jews for the first occurrence of breast or ovary cancer is 63%. The kin-cohort design can be a useful tool for quickly estimating penetrance from volunteers in a setting in which the mutation prevalence is relatively high.

Adult↗

PubMed1997

Survival after AIDS diagnosis in a cohort of hemophilia patients. Multicenter Hemophilia Cohort Study.

We studied factors affecting survival after the diagnosis of AIDS in a cohort of 1253 patients with hemophilia. The nature of the AIDS-defining condition was found to be as important as age at seroconversion and CD4+ lymphocyte level in predicting survival. A multivariate analysis yielded estimates of median survival for groups defined by age at seroconversion (0 through 15, 16 through 69), CD4+ lymphocyte count (<100 cells/microl versus > or = 100 cells/microl), and 10 AIDS-defining disease groups. Estimates of median survival after a single AIDS-defining condition ranged from 3 to 51 months, depending on the diseases. Median survival after a second AIDS-defining condition was about 1.5- to 2.0-fold shorter than after an initial, isolated AIDS-defining condition. HIV-related neurologic disease (i.e., AIDS dementia complex or multifocal leukoencephalopathy) was a notable exception. It correlated with the shortest estimates of median survival (3 to 9 months), and this poor prognosis was no worse for patients who had a second AIDS-defining condition. The results of this analysis were consistent in most respects with other published analyses of factors affecting survival. These findings may be useful in the clinical care of persons with AIDS and in estimating the number of persons alive who have had a particular AIDS-defining disease.

Acquired Immunodeficiency Syndrome↗

PubMed1997

Statistical design of calibration studies.

We investigated some design aspects of calibration studies. The specific situation addressed was one in which a large group is evaluated with a food-frequency questionnaire and a smaller calibration study is conducted through use of repeated food records or recalls, with the subjects in the calibration study constituting a random sample of those in the large group. In designing a calibration study, one may use large sample sizes and few food records per individual or smaller samples and more records per subject. Neither strategy is always preferable. Instead, the optimal method for a given study depends on the survey instrument used (24-h recalls or multiple-day food records) and the variables of interest.

Computer Simulation↗

PubMed1997

Design aspects of calibration studies in nutrition, with analysis of missing data in linear measurement error models.

Motivated by an example in nutritional epidemiology, we investigate some design and analysis aspects of linear measurement error models with missing surrogate data. The specific problem investigated consists of an initial large sample in which the response (a food frequency questionnaire, FFQ) is observed and then a smaller calibration study in which replicates of the error prone predictor are observed (food records or recalls, FR). The difference between our analysis and most of the measurement error model literature is that, in our study, the selection into the calibration study can depend on the value of the response. Rationale for this type of design is given. Two major problems are investigated. In the design of a calibration study, one has the option of larger sample sizes and fewer replicates or smaller sample sizes and more replicates. Somewhat surprisingly, neither strategy is uniformly preferable in cases of practical interest. The answers depend on the instrument used (recalls or records) and the parameters of interest. The second problem investigated is one of analysis. In the usual linear model with no missing data, method of moments estimates and normal-theory maximum likelihood estimates are approximately equivalent, with the former method in most use because it can be calculated easily and explicitly. Both estimates are valid without any distributional assumptions. In contrast, in the missing data problem under consideration, only the moments estimate is distribution-free, but the maximum likelihood estimate has at least 50% greater precision in practical situations when normality obtains. Implications for the design of nutritional calibration studies are discussed.

Aged↗

PubMed1996

On design considerations and randomization-based inference for community intervention trials.

This paper discusses design considerations and the role of randomization-based inference in randomized community intervention trials. We stress that longitudinal follow-up of cohorts within communities often yields useful information on the effects of intervention on individuals, whereas cross-sectional surveys can usefully assess the impact of intervention on group indices of health. We also discuss briefly special design considerations, such as sampling cohorts from targeted subpopulations (for example, heavy smokers), matching the communities, calculating sample size, and other practical issues. We present randomization tests for matched and unmatched cohort designs. As is well known, these tests necessarily have proper size under the strong null hypothesis that treatment has no effect on any community response. It is less well known, however, that the size of randomization tests can exceed nominal levels under the 'weak' null hypothesis that intervention does not affect the average community response. Because this weak null hypothesis is of interest in community intervention trials, we study the size of randomization tests by simulation under conditions in which the weak null hypothesis holds but the strong null hypothesis does not. In unmatched studies, size may exceed nominal levels under the weak null hypothesis if there are more intervention than control communities and if the variance among community responses is larger among control communities than among intervention communities; size may also exceed nominal levels if there are more control than intervention communities and if the variance among community responses is larger among intervention communities. Otherwise, size is likely near nominal levels. To avoid such problems, we recommend use of the same numbers of control and intervention communities in unmatched designs. Pair-matched designs usually have size near nominal levels, even under the weak null hypothesis. We have identified some extreme cases, unlikely to arise in practice, in which even the size of pair-matched studies can exceed nominal levels. These simulations, however, tend to confirm the robustness of randomization tests for matched and unmatched community intervention trials, particularly if the latter designs have equal numbers of intervention and control communities. We also describe adaptations of randomization tests to allow for covariate adjustment, missing data, and application to cross-sectional surveys. We show that covariate adjustment can increase power, but such power gains diminish as the random component of variation among communities increases, which corresponds to increasing intraclass correlation of responses within communities. We briefly relate our results to model-based methods of inference for community intervention trials that include hierarchical models such as an analysis of variance model with random community effects and fixed intervention effects. Although we have tailored this paper to the design of community intervention trials, many of the ideas apply to other experiments in which one allocates groups or clusters of subjects at random to intervention or control treatments.

Clinical Trials as Topic↗

PubMed1996

Reproducibility studies and interlaboratory concordance for assays of serum hormone levels: estrone, estradiol, estrone sulfate, and progesterone.

We conducted studies to measure sources of assay variability for estrone, estradiol, estrone sulfate, and progesterone for postmenopausal women (n = 5) and for women in the mid-follicular (n = 5) and mid-luteal (n = 5) phases of the menstrual cycle. A single blood sample from each woman was divided into 2.5-ml aliquots and stored at -70 degrees C, and sets of two aliquots were sent at monthly intervals to each of three laboratories (four for progesterone). Each aliquot was analyzed in duplicate. Thus, within each menstrual category, we were able to estimate the components of variance due to variation among women, variation among aliquots, variation among duplicate measurements, and variation among the 4 analysis days. Using the logarithm of assay measurements, we estimated the percentage of variance attributable to variation among women in each menstrual category, 100 rho, is the estimated intraclass correlation. For each assay, 100 rho exceeded 90% for mid-follicular and mid-luteal women. For postmenopausal women, values of 100 rho exceed 84% for estrone in two laboratories. Values of 100 rho were lower for progesterone in postmenopausal women, although a value of 84% was estimated from one laboratory. These studies indicate that estrogen assays over a period of 3 months permit reliable comparisons among women in a given menstrual category. Progesterone measurements are likewise reliable for women in the mid-follicular and mid-luteal phases but somewhat less satisfactory for postmenopausal women. These assessments of variability pertain only to laboratory techniques and do not allow for secular variation in intra-woman hormone levels. Moreover, although these measurements tend to be reliable enough for making comparisons among women, estimates of coefficients of variation for estrogens are about 10% for mid-follicular and mid-luteal phase women and about 11-20% for postmenopausal women. Coefficients of variation for progesterone are about 10% for mid-luteal, 20% for mid-follicular, and 30% for postmenopausal women.

Analysis of Variance↗

PubMed1995

Determining the value of additional surrogate exposure data for improving the estimate of an odds ratio.

We consider the design of both cohort and case-control studies in which an initial ('stage 1') sample of complete data on an error-free disease indicator (D), a correct ('gold standard') dichotomous exposure measurement (X) and an error-prone exposure measurement (Z) are available. We calculate the amount of additional information on the odds ratio relating D to X that one can obtain from a second ('stage 2') sample of measurements only on D and Z. If one allows for differential measurement error in Z, there is often little advantage in having more than four times as much data in stage 2 data as in stage 1. With the assumption that a non-differential measurement error model is reasonable, larger amounts of stage 2 data can be useful. Simulations indicate that stage 1 samples of modest size (50 cases in case-control studies and 50 failures in cohort studies) yield sufficiently reliable estimates of needed parameters to assist in determining an appropriate size for the stage 2 sample. These ideas apply in settings either where the amount of stage 1 data is limited and fixed by external constraints or where one has gathered stage 1 data in advance to avoid collecting superfluous stage 2 data.

Carcinogens↗

PubMed1995

Interplay between design and analysis for behavioral intervention trials with community as the unit of randomization.

This paper outlines an approach for the design and analysis of randomized controlled trials investigating community-based interventions for behavioral change aimed at health promotion. The approach is illustrated using the Community Intervention Trial for Smoking Cessation (COMMIT), conducted from 1988 to 1993, involving 11 pairs of communities in North America, matched on geographic location, size, and sociodemographic factors. The situation discussed is when assignment to intervention is done at the community level; for COMMIT, the very nature of the intervention required this. The number of communities as a key determinant of the statistical power of the trial. The use of matched pairs of communities can achieve a gain in statistical efficiency. Randomization is used to obtain an unbiased assessment of the intervention effect; randomization also provides the basis for statistical analysis. Permutation tests (and corresponding test-based confidence intervals), using community as the unit of analysis, follow directly from the randomization distribution. Within this framework, individual-level covariates can be used for imputation of missing values and for adjusting analyses of intervention effect.

Health Behavior↗

PubMed1993

Serum mucin antigens CASA and MSA in tumors of the breast, ovary, lung, pancreas, bladder, colon, and prostate. A blind trial with 420 patients.

BACKGROUND: The tumor markers CASA (cancer-associated serum antigen) and MSA (mammary serum antigen) have previously been shown to be useful in the clinical management of ovarian and breast carcinoma, respectively, but have not been assessed in other types of cancer. These assays were compared with carcinoembryonic antigen (CEA) and prostate-specific antigen (PSA) in a blind trial using sera from the Mayo Clinic-National Cancer Institute (NCI) Diagnostic Serum Bank. METHODS: CASA and MSA were assessed retrospectively in a blind trial using 465 serum samples from the Mayo Clinic-NCI Diagnostic Serum Bank representing malignant and benign disease of the breast, ovary, lung, pancreas, bladder, colon, and prostate and age-matched and gender-matched healthy control donors. CASA, MSA, and PSA levels were determined using commercially available kits, and CEA values and clinical details were later provided by the Mayo Clinic. RESULTS: CASA and MSA showed good reproducibility in 45 duplicate samples. CASA values were significantly elevated in the serum of patients with malignant tumors of the breast (44%), ovary (58%), lung (56%), prostate (48%), and bladder (54%), but not in those with benign conditions of these organs or pancreatic or colon cancer. MSA levels were only elevated significantly in cancers of the breast (52%) and ovary (58%). CASA showed significantly better sensitivity than either CEA (20%) or MSA (25%) in the detection of lung cancer, whereas CEA showed significantly superior detection of colon cancers (78%). CASA was not as sensitive as PSA in prostate cancer (48% versus 96%), but gave superior specificity in nonmalignant conditions of the prostate (93% versus 70%), although this was not statistically significant. CONCLUSIONS: The commercial CASA and MSA assays are reliable and reproducible tests for these tumor markers. In addition to ovarian cancer, CASA is also elevated significantly in many patients with breast, lung, prostate, and bladder cancer and has potential clinical use in patients with these tumors. The use of the MSA assay appears restricted to breast cancer.

Aged↗

PubMed1992

Early menopause in long-term survivors of cancer during adolescence.

OBJECTIVE: We attempted to investigate the risk of early menopause after treatment for cancer during childhood or adolescence. STUDY DESIGN: We interviewed 1067 women in whom cancer was diagnosed before age 20, who were at least 5-year survivors, and who were still menstruating at age 21. Self-reported menopause status in survivors was compared with that in 1599 control women. RESULTS: Cancer survivors, with disease diagnosed between ages 13 and 19, had a risk of menopause four times greater than that of controls during the ages 21 to 25; the risk relative to controls declined thereafter. Significantly increased relative risks of menopause during the early 20s occurred after treatment with either radiotherapy alone (relative risk 3.7) or alkylating agents alone (relative risk 9.2). During ages 21 to 25 the risk of menopause increased 27-fold for women treated with both radiation below the diaphragm and alkylating agent chemotherapy. By age 31, 42% of these women had reached menopause compared with 5% for controls. CONCLUSION: Treatment for cancer during adolescence carries a substantial risk for early menopause among women still menstruating at age 21. Increasing use of radiation and chemotherapy, together with the continued trend toward delayed childbearing, suggests that these women should be made aware of their smaller window of fertility so that they can plan their families accordingly.

Adult↗

PubMed1991

Evaluation of serum sialic acid and carcinoembryonic antigen for the detection of early-stage colorectal cancer.

Various expressions of elevated serum sialic acid (total sialic acid, TSA: lipid-associated sialic acid, LASA; LASA/TSA; TSA normalized to total protein, TSA/TP) have been evaluated and compared with increased serum carcinoembryonic antigen (CEA) levels for the detection of early-stage colorectal cancer. This evaluation was done blindly on a coded panel of 320 sera from staged colorectal cancer patients and controls provided by the Mayo Clinic--National Cancer Institute Diagnostic Bank. Unlike the findings of a previous preliminary study (Tautu et al., JNCI 80:1333-1337, 1988), the ratio of LASA/TSA was not useful for detecting early-stage (Dukes A and B) colorectal cancer. However, TSA and TSA/TP values were significantly elevated in each colorectal cancer subgroup compared with normal controls. TSA and TSA/TP values displayed a marginally better discriminatory power than CEA values in the case of Dukes A subgroup with respect to normal controls. CEA still appears to be the best single overall marker for discriminating between colorectal cancers and controls. However, multiple marker analysis using CEA and TSA (and related markers) appears to be more sensitive than CEA alone for detecting colorectal cancer.

Adult↗