Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions.

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.

Epidemiologic patterns of biliary tract cancer in the United States: 2001-2015.

BACKGROUND: Biliary tract cancer (BTC) includes intrahepatic cholangiocarcinoma (ICC), extrahepatic cholangiocarcinoma, gallbladder cancer, and ampulla of Vater cancer (AVC). Although BTC is rare in the US, incidence is increasing and elevated in certain populations. This study examined BTC epidemiology in the US by age, sex, race/ethnicity, geographic region, and anatomic site. METHODS: BTC incidence, prevalence, mortality, and survival from 2001 to 2015 were evaluated using the National Cancer Institute's Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention's National Program of Cancer Registries databases. Incidence and mortality rates were calculated and reported as age-standardized rates. Data were assessed by age, anatomic sites, geographic region, and race/ethnicity, and a joinpoint regression model was used to predict trends for age-adjusted BTC incidence and mortality rates. RESULTS: BTC incidence increased during the study period (annual percent change = 1.76, 95% confidence interval [1.59-1.92]), with the highest increase in ICC (6.65 [6.11-7.19]). Incidence of unspecified BTC initially increased but has recently begun to drop. Hispanic, Asian/Pacific Islander, Black, or American Indian/Alaska Native race/ethnicity was associated with higher BTC mortality rates than White race/ethnicity. Patients with ICC had the highest mortality rate (age-standardized rate = 1.87/100,000 person-years [1.85-1.88]). Five-year survival was 15.2% for all BTC, ranging from 8.5% (ICC) to 34.5% (AVC), and patients with distant disease at diagnosis had lower survival (3%) compared with those with regional (19.1%) or locally advanced disease (31.5%). CONCLUSIONS: BTC incidence increased, survival was low across all subtypes, and mortality was greatest in patients with ICC. This underscores the serious, increasing unmet need among patients with BTC. Treatment options are limited, although clinical studies investigating immunotherapy, targeted therapies, and alternative chemotherapy combinations are ongoing. Epidemiological insights may improve patient care and inform the integration of novel therapies for BTC.

Comparing Go/No-Go Decision-Making Properties Between Single Arm Phase II Trial Designs in Oncology.

INTRODUCTION: Simon's design has been widely used in oncology to conduct single arm phase II trials and to make Go/No-Go development decision. Other authors have proposed designs with decision-making frameworks that include a third, "Consider" outcome. For results in the Consider zone, a final Go/No-Go development decision must still be made; however it is typically a subjective decision based on the totality of data and the development landscape. Under this framework, the probability of continuing development when the candidate therapy is truly ineffective or the probability of stopping development when the candidate therapy is truly effective is undefined. METHODS: We use a motivating example to compare end of trial decision-making between Simon's two-stage approach and a Multilevel outcome approach. We present the minimum and maximum development decision error probabilities by varying whether candidates that end in the Consider zone would ultimately continue with development or not. RESULTS: The Multilevel approach typically requires fewer patients, but the risk of making an incorrect drug development decision is inflated above the statistically defined Type I and Type II error rates. Compared to a Type I error rate of 20%, the Multilevel trial's maximum probability of moving forward with an ineffective therapy is 22%, 27%, and 36% for Consider zone sizes of 10%, 20%, and 30%, respectively. CONCLUSION: The Multilevel approach provides flexibility in interpreting moderate efficacy results. However, the flexibility is accomplished with a lower sample size and corresponding uncertainty in the trial outcome that increases the risk of incorrect drug development decisions.

Worldwide Incidence and Mortality of Biliary Tract Cancer.

Background and Aims: Biliary tract cancer (BTC) consists of a group of hepatic and perihepatic tumors that are in close proximity but are anatomically different, including gallbladder cancer (GBC), cholangiocarcinoma (extrahepatic and intrahepatic [ICC]), and ampulla of Vater cancer (AVC). Most epidemiologic research has focused on 1 or more anatomic subtypes, or does not differentiate BTC from hepatocellular carcinoma or other primary liver cancers. Here, we provide a descriptive update on global incidence and mortality rates for BTC, overall and by anatomic subtypes. Methods: Age-standardized rates (per 100,000 person-years) were derived from the International Agency for Research on Cancer, Cancer Incidence in Five Continents, Volume XI (2008-2012; 22 countries), and the World Health Organization Mortality Database (2006-2016; 38 countries). Results: BTC incidence varied by country, with the highest in Chile (14.35) and the lowest in Vietnam (1.25). Mortality rates for BTC were highest for the Republic of Korea (11.64) and lowest for the Republic of Moldova (1.65). BTC mortality rates increased over time in 24 of 34 countries. Patients aged ≥75 years had 5-10 times higher mortality rates than the overall BTC rate in all countries. In most countries, incidence rates were highest for GBC, and mortality rates highest for ICC, while both were lowest for AVC. Females had and died from GBC more frequently than males. For ICC, extrahepatic cholangiocarcinoma, and AVC, males trended toward higher incidence and mortality rates. Conclusion: The increasing incidence and mortality trends reported here indicate a need for improved prevention and treatment for all BTC subtypes.

Modulation of Cardiometabolic Disease Markers by Type I Interferon Inhibition in Systemic Lupus Erythematosus.

OBJECTIVE: Neutrophil dysregulation and the type I interferon (IFN) axis have been proposed to contribute to premature cardiovascular disease, a leading cause of mortality in patients with systemic lupus erythematosus (SLE). In the present study, we evaluated the ability of anifrolumab, a type I IFN receptor-blocking antibody, to reduce neutrophil extracellular trap (NET) formation and modulate cardiometabolic disease markers in comparison to placebo. METHODS: Study subjects comprised patients with moderate-to-severe SLE who were enrolled in phase IIb of the MUSE trial (A Phase II, Randomized Study to Evaluate the Efficacy and Safety of MEDI-546 in Subjects with Systemic Lupus Erythematosus), with healthy individuals as controls. Blood samples were collected from SLE patients (n = 305) and healthy controls (n = 10-20) before the initiation of treatment (baseline) and from SLE patients after they had been treated with 300 mg of anifrolumab (n = 99) or placebo (n = 102). Baseline IFN gene signature test status was determined, and the IFN gene signature (21-gene panel) was monitored over time. Serum proteins were measured by multiplex immunoassay or ultrasensitive Simoa assay. NET complexes, cholesterol efflux capacity (CEC), and glycoprotein acetylation (GlycA) and other lipid parameters were assessed in plasma. RESULTS: Formation of NET complexes and levels of tumor necrosis factor (TNF) and interleukin-10 (IL-10) were correlated with extent of type I IFN pathway activity. NET complexes and IL-10 levels were up-regulated in SLE patients compared to healthy controls (P < 0.008). The cardiometabolic disease markers CEC and GlycA were also found to be dysregulated in patients with SLE (P < 0.001 versus healthy controls). Type I IFN receptor inhibition with anifrolumab significantly reduced NET complexes and GlycA and improved CEC compared to baseline (P < 0.05) whereas no improvements were seen with placebo. Levels of TNF and IL-10 were reduced with anifrolumab compared to placebo (P < 0.05). CONCLUSION: These data support a key role for type I IFNs in modulating factors contributing to SLE vasculopathy and suggest that inhibition of this pathway could decrease cardiovascular risk in individuals with SLE.

Comparisons of outlier tests for potency bioassays.

Potency bioassays are used to measure biological activity. Consequently, potency is considered a critical quality attribute in manufacturing. Relative potency is measured by comparing the concentration-response curves of a manufactured test batch with that of a reference standard. If the curve shapes are deemed similar, the test batch is said to exhibit constant relative potency with the reference standard, a critical requirement for calibrating the potency of the final drug product. Outliers in bioassay potency data may result in the false acceptance/rejection of a bad/good sample and, if accepted, may yield a biased relative potency estimate. To avoid these issues, the USP<1032> recommends the screening of bioassay data for outliers prior to performing a relative potency analysis. In a recently published work, the effects of one or more outliers, outlier size, and outlier type on similarity testing and estimation of relative potency were thoroughly examined, confirming the USP<1032> outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP<1010>, are evaluated and compared in this work through computer simulation. Two novel outlier detection methods are also proposed. The effects of outlier removal on similarity testing and estimation of relative potency were evaluated, resulting in recommendations for best practice.

A note on the determination of non-inferiority margins with application in oncology clinical trials.

The goal of a non-inferiority trial is to evaluate whether the effect of an experimental treatment is not inferior to that of the active control. Determination of an appropriate non-inferiority margin is critical to the demonstration of non-inferiority. A commonly used method is called the fixed-margin approach recommended by the FDA. The fixed-margin approach consists of two steps: first the lower limit of the 1 - α * two-sided confidence interval (CI) of the active-control effect versus placebo is calculated from relevant historical trials or meta-analysis; second, the non-inferiority margin is obtained as a fraction of the lower confidence limit of the control effect to preserve partial control effect. An alternative method is to use the point estimate, instead of the lower confidence limit, of the active-control effect. The fixed-margin approach based on the lower limit may be ultra-conservative with unconditional Type 1 error rate much smaller than target α / 2 level, while the margin based on the point estimate is liberal. We derive the Type 1 error rate as a function of variances of the effect estimates in the historical and the current non-inferiority trials. We also propose an alternative approach for the non-inferiority margin that maintains the target Type 1 error rate. For the endpoint of landmark survival, we conduct simulations to compare the fixed-margin methods and the proposed method. For illustration, we apply the proposed method to an oncology non-inferiority clinical trial to determine an alternative non-inferiority margin.

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model.

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.

Relationship Between Benralizumab Exposure and Efficacy for Patients With Severe Eosinophilic Asthma.

We evaluated the relationship between benralizumab (30 mg every 4 and 8 weeks (Q4W, Q8W)) pharmacokinetic (PK) exposure and end points of asthma exacerbation rates (AERs) and change from baseline in prebronchodilator forced expiratory volume in 1 second (FEV1 ) for patients with severe, uncontrolled eosinophilic asthma in the SIROCCO/CALIMA phase III trials. In empirical assessment, AER ratios in SIROCCO were similar across PK quartiles. However, the lowest PK quartile in CALIMA had reduced efficacy; low CALIMA placebo AER possibly confounded this result. In population modeling, estimated benralizumab 90% effective concentration for AER reduction was 927 ng/mL, below the Q8W dosage steady-state average PK concentration (1,066 ng/mL). Benralizumab treatment resulted in more rapid FEV1 improvement vs. placebo (estimated half-maximum time: 7.6 vs. 18 days); this response was greater for patients with greater baseline eosinophil counts. These results confirmed 30 mg Q8W is the optimal benralizumab dosage for patients with severe eosinophilic asthma.

Effect of a statistical outlier in potency bioassays.

The USP<1032> guidelines recommend the screening of bioassay data for outliers prior to performing a relative potency (RP) analysis. The guidelines, however, do not offer advice on the size or type of outlier that should be removed prior to model fitting and calculation of RP. Computer simulation was used to investigate the consequences of ignoring the USP<1032> guidance to remove outliers. For biotherapeutics and vaccines, outliers in potency data may result in the false acceptance/rejection of a bad/good lot of drug product. Biological activity, measured through a potency bioassay, is considered a critical quality attribute in manufacturing. If the concentration-response potency curve of a test sample is deemed to be similar in shape to that of the reference standard, the curves are said to exhibit constant RP, an essential criterion for the interpretation of a RP. One or more outliers in the concentration-response data, however, may result in a failure to declare similarity or may yield a biased RP estimate. Concentration-response curves for test and reference were computer generated with constant RP from four-parameter logistic curves. Single outlier, multiple outlier, and whole-curve outlier scenarios were explored for their effects on the similarity testing and on the RP estimation. Though the simulations point to situations for which outlier removal is unnecessary, the results generally support the USP<1032> recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.

Selection of a Ligand-Binding Neutralizing Antibody Assay for Benralizumab: Comparison with an Antibody-Dependent Cell-Mediated Cytotoxicity (ADCC) Cell-Based Assay.

Assessment of anti-drug antibodies (ADAs) for neutralizing activity is important for the clinical development of biopharmaceuticals. Two types of neutralizing antibody (NAb) assays (competitive ligand-binding assay [CLBA] and cell-based assay [CBA]) are commonly used to characterize neutralizing activities. To support the clinical development of benralizumab, a humanized, anti-interleukin-5 receptor α, anti-eosinophil monoclonal antibody, we developed and validated a CLBA and a CBA. The CLBA and CBA were compared for sensitivity, drug tolerance, and precision to detect NAbs in serum samples from clinical trials. The CLBA was more sensitive (27.1 and 37.5 ng/mL) than the CBA (1.02 and 1.10 µg/mL) in detecting NAbs to benralizumab for the polyclonal and monoclonal ADA controls, respectively. With the same polyclonal ADA control, the CLBA detected 250 ng/mL of ADA in the presence of 100 ng/mL of benralizumab, whereas the CBA detected 1.25 µg/mL of ADA in the presence of 780 ng/mL of benralizumab. In 195 ADA-positive samples from 5 studies, 63.59% (124/195) and 16.9% (33/195) were positive for NAb as measured by the CLBA and the CBA, respectively. ADA titers were strongly correlated (Pearson's correlation coefficient r = 0.91; n = 195) with CLBA titers. Moreover, the CLBA titer correlated with CBA percentage inhibition in the CBA-positive samples (Spearman's coefficient r = 0.50; n = 33). Our data demonstrated advantages of the CLBA in various aspects and supported the choice of the CLBA as a NAb assay for the phase III trials.

Determination of acceptance criteria and sample sizes for accelerated stability comparability studies for biologics.

Changes of manufacturing processes are common. It is required by the regulatory agencies that manufacturers establish adequate and appropriate comparability between pre-change and post-change products. The goals of comparability assessments are to demonstrate the comparability and consistency of product quality before and after change and to demonstrate that the changes do not have an adverse effect on safety and efficacy of the drug products. Accelerated or stressed stability studies may shed light on drug quality under stressed environmental conditions and on product differences in the degradation pathways. Comparability of accelerated stability data may provide further evidence on the impact of process change. Equivalence test has been recommended to demonstrate the comparability of stability profiles for accelerated stability studies. Selection of appropriate acceptance criteria for determining comparability is one of the most challenging steps in the comparability studies. Because of the inherent heterogeneity of biologics, the stability profiles may vary considerably from batch to batch. It is more challenging to set the acceptance criteria for comparing the accelerated stability data for biologics. In this article, we present an approach for determining the acceptance criteria and necessary sample sizes for accelerated comparability studies for biologics.

Evaluation of Different Estimation Methods for Accuracy and Precision in Biological Assay Validation.

Biological assays (bioassays) are procedures to estimate the potency of a substance by studying its effects on living organisms, tissues, and cells. Bioassays are essential tools for gaining insight into biologic systems and processes including, for example, the development of new drugs and monitoring environmental pollutants. Two of the most important parameters of bioassay performance are relative accuracy (bias) and precision. Although general strategies and formulas are provided in USP<1033>, a comprehensive understanding of the definitions of bias and precision remain elusive. Additionally, whether there is a beneficial use of data transformation in estimating intermediate precision remains unclear. Finally, there are various statistical estimation methods available that often pose a dilemma for the analyst who must choose the most appropriate method. To address these issues, we provide both a rigorous definition of bias and precision as well as three alternative methods for calculating relative standard deviation (RSD). All methods perform similarly when the RSD ≤10%. However, the USP estimates result in larger bias and root-mean-square error (RMSE) compared to the three proposed methods when the actual variation was large. Therefore, the USP method should not be used for routine analysis. For data with moderate skewness and deviation from normality, the estimates based on the original scale perform well. The original scale method is preferred, and the method based on log-transformation may be used for noticeably skewed data.LAY ABSTRACT: Biological assays, or bioassays, are essential in the development and manufacture of biopharmaceutical products for potency testing and quality monitoring. Two important parameters of assay performance are relative accuracy (bias) and precision. The definitions of bias and precision in USP 〈1033〉 are elusive and confusing. Another complicating issue is whether log-transformation should be used for calculating the intermediate precision. It is often challenging for analysts to pick the most appropriate estimation method. To address these issues, we give a rigorous definition of bias and precision and provide three alternative methods of calculating RSD. We demonstrate that the RSD formula in USP 〈1033〉 tends to have larger bias and root-mean-square error (RMSE) than the three proposed methods.

Non-normal random effects models for immunogenicity assay cut point determination.

Administration of biological therapeutics can generate undesirable immune responses that may induce anti-drug antibodies (ADAs). Immunogenicity can negatively affect patients, ranging from mild reactive effect to hypersensitivity reactions or even serious autoimmune diseases. Assessment of immunogenicity is critical as the ADAs can adversely impact the efficacy and safety of the drug products. Well-developed and validated immunogenicity assays are required by the regulatory agencies as tools for immunogenicity assessment. Key to the development and validation of an immunogenicity assay is the determination of a cut point, which serves as the threshold for classifying patients as ADA positive(reactive) or negative. In practice, the cut point is determined as either the quantile of a parametric or nonparametric empirical distribution. The parametric method, which is often based on a normality assumption, may lead to biased cut point estimates when the normality assumption is violated. The non-parametric method, which yields unbiased estimates of the cut point, may have low efficiency when the sample size is small. As the distribution of immune responses are often skewed and sometimes heavy-tailed, we propose two non-normal random effects models for cut point determination. The random effects, following a skew-t or log-gamma distribution, can incorporate the skewed and heavy-tailed responses and the correlation among repeated measurements. Simulation study is conducted to compare the proposed method with the current normal and nonparametric alternatives. The proposed models are also applied to a real dataset generated from assay validation studies.

Fracture risk assessment in older adults using a combination of selected quantitative computed tomography bone measures: a subanalysis of the Age, Gene/Environment Susceptibility-Reykjavik Study.

Bone mineral density (BMD) and geometric bone measures are individually associated with prevalent osteoporotic fractures. Whether an aggregate of these measures would better associate with fractures has not been examined. We examined relationships between self-reported fractures and selected bone measures acquired by quantitative computerized tomography (QCT), a composite bone score, and QCT-acquired dual-energy X-ray absorptiometry-like total femur BMD in 2110 men and 2682 women in the Age, Gene/Environment Susceptibility-Reykjavik Study. The combined bone score was generated by summing gender-specific Z-scores for 4 QCT measures: vertebral trabecular BMD, femur neck cortical thickness, femur neck trabecular BMD, and femur neck minimal cross-sectional area. Except for the latter measure, lower scores for QCT measures, singly and combined, showed positive (p < 0.05) associations with fractures. Results remained the same in stratified models for participants not taking bone-promoting medication. In women on bone-promoting medication, greater femur neck cortical thickness and trabecular BMD were significantly associated with fracture status. However, the association between fracture and combined bone score was not stronger than the associations between fracture and individual measures or total femur BMD. Thus, the selected measures did not all similarly associate with fracture status and did not appear to have an additive effect on fracture status.

Linear mixed models for multiple outcomes using extended multivariate skew-t distributions.

Multivariate outcomes with heavy skewness and thick tails often arise from clustered experiments or longitudinal studies. Linear mixed models with multivariate skew-t (MST) distributions for the random effects and the error terms is a popular tool of robust modeling for such outcomes. However the usual MST distribution only allows a common degree of freedom for all marginal distributions, which is only appropriate when each marginal has the same amount of tail heaviness. In this paper, we introduce a new class of extended MST distributions, which allow different degrees of freedom and thereby can accommodate heterogeneity in tail-heaviness across outcomes. The extended MST distributions yield a flexible family of models for multivariate outcomes. The hierarchical representation of the MST distribution allows MCMC methods to be easily applied to compute the parameter estimates. The proposed model is applied to data from two biomedical studies: one on bivariate markers of AIDS progression and the other on sexual behavior from a longitudinal study.

Predicting county-level cancer incidence rates and counts in the USA.

Many countries, including the USA, publish predicted numbers of cancer incidence and death in current and future years for the whole country. These predictions provide important information on the cancer burden for cancer control planners, policymakers and the general public. Based on evidence from several empirical studies, the joinpoint (segmented-line linear regression) model (JPM) has been adopted by the American Cancer Society to estimate the number of new cancer cases in the USA and in individual states since 2007. Recently, cancer incidence in smaller geographic regions such as counties, and local policy makers are increasingly interested with Federal Information Processing Standard code regions. The natural extension is to directly apply the JPM to county-level cancer incidence data. The direct application has several drawbacks and its performance has not been evaluated. To address the concerns, we developed a spatial random-effects JPM for county-level cancer incidence data. The proposed model was used to predict both cancer incidence rates and counts at the county level. The standard JPM and the proposed method were compared through a validation study. The proposed method outperformed the standard JPM for almost all cancer sites, especially for moderate or rare cancer sites and for counties with small population sizes. As an application, we predicted county-level prostate cancer incidence rates and counts for the year 2011 in Connecticut.

A class of transformation covariate regression models for estimating the excess hazard in relative survival analysis.

Relative survival is the standard measure of excess mortality due to cancer in population-based cancer survival studies. In relative survival analysis, the observed hazard for cancer patients is the sum of the expected hazard for the general cancer-free population and the excess hazard associated with a cancer diagnosis. Previous models for relative survival analysis have assumed that the excess hazard rate is related to covariates by additive or multiplicative regression models. In this paper, a transformation covariate regression model is developed for estimation of the excess hazard rate, which includes both the additive and the multiplicative regression models as special cases. The baseline excess hazard rate and time-dependent hazard ratios can be approximated by means of regression splines, and the parameter estimates can be obtained using a standard statistical package. As is demonstrated through simulation, the proposed transformation hazards model provides a reasonably good fit to typical relative survival data. For illustration purposes, the sex difference in relative survival for lung and bronchus cancer patients is examined using data from population-based cancer registries (1973-2003).

Age-related disease association of endogenous γ-H2AX foci in mononuclear cells derived from leukapheresis.

The phosphorylated form of histone H2AX (γ-H2AX) forms immunohistochemically detectable foci at DNA double strand breaks. In peripheral blood mononuclear cells (PBMCs) derived from leukapheresis from patients enrolled in the Baltimore Longitudinal Study of Aging, γ-H2AX foci increased in a linear fashion with regards to age, peaking at ~57 years. The relationship between the frequency of γ-H2AX foci and age-related pathologies was assessed. We found a statistically significant (p = 0.023) 50% increase in foci in PBMCs derived from patients with a known history of vitamin D deficiency. In addition, there were trends toward increased γ-H2AX foci in patients with cataracts (34% increase, p<0.10) and in sleep apnea patients (44%, p<0.10). Among patients ≥57 y/o, we found a significant (p = 0.037) 36% increase in the number of γ-H2AX foci/cell for patients with hypertension compared to non-hypertensive patients. Our results support a role for increased DNA damage in the morbidity of age-related diseases. γ -H2AX may be a biomarker for human morbidity in age-related diseases.

Chapter 3: Cohort life tables by smoking status, removing lung cancer as a cause of death.

The purpose of this study was to develop life tables by smoking status removing lung cancer as a cause of death. These life tables are inputs to studies that compare the effectiveness of lung cancer treatments or interventions, and provide a way to quantify time until death from causes other than lung cancer. The study combined actuarial and statistical smoothing methods, as well as data from multiple sources, to develop separate life tables by smoking status, birth cohort, by single year of age, and by sex. For current smokers, separate life tables by smoking quintiles were developed based on the average number of cigarettes smoked per day by birth cohort. The end product is the creation of six non-lung-cancer life tables for males and six tables for females: five current smoker quintiles and one for never smokers. Tables for former smokers are linear combinations of the appropriate table based on the current smoker quintile before quitting smoking and the never smoker probabilities, plus added covariates for the smoking quit age and time since quitting.