Cancer constitutes an enormous burden on society in more and less economically developed countries alike. The occurrence of cancer is increasing because of the growth and aging of the population, as well as an increasing prevalence of established risk factors such as smoking, overweight, physical inactivity, and changing reproductive patterns associated with urbanization and economic development. Based on GLOBOCAN estimates, about 14.1 million new cancer cases and 8.2 million deaths occurred in 2012 worldwide. Over the years, the burden has shifted to less developed countries, which currently account for about 57% of cases and 65% of cancer deaths worldwide. Lung cancer is the leading cause of cancer death among males in both more and less developed countries, and has surpassed breast cancer as the leading cause of cancer death among females in more developed countries; breast cancer remains the leading cause of cancer death among females in less developed countries. Other leading causes of cancer death in more developed countries include colorectal cancer among males and females and prostate cancer among males. In less developed countries, liver and stomach cancer among males and cervical cancer among females are also leading causes of cancer death. Although incidence rates for all cancers combined are nearly twice as high in more developed than in less developed countries in both males and females, mortality rates are only 8% to 15% higher in more developed countries. This disparity reflects regional differences in the mix of cancers, which is affected by risk factors and detection practices, and/or the availability of treatment. Risk factors associated with the leading causes of cancer death include tobacco use (lung, colorectal, stomach, and liver cancer), overweight/obesity and physical inactivity (breast and colorectal cancer), and infection (liver, stomach, and cervical cancer). A substantial portion of cancer cases and deaths could be prevented by broadly applying effective prevention measures, such as tobacco control, vaccination, and the use of early detection tests.

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Global cancer statistics, 2012

SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelih ood. Some key ironh: Estimating equation; Generalized linear model; Longitudinal data; Quasi-likelihood; Repeated measures.

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Longitudinal data analysis using generalized linear models

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program), the Centers for Disease Control and Prevention (National Program of Cancer Registries), and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2016, 1,685,210 new cancer cases and 595,690 cancer deaths are projected to occur in the United States. Overall cancer incidence trends (13 oldest SEER registries) are stable in women, but declining by 3.1% per year in men (from 2009-2012), much of which is because of recent rapid declines in prostate cancer diagnoses. The cancer death rate has dropped by 23% since 1991, translating to more than 1.7 million deaths averted through 2012. Despite this progress, death rates are increasing for cancers of the liver, pancreas, and uterine corpus, and cancer is now the leading cause of death in 21 states, primarily due to exceptionally large reductions in death from heart disease. Among children and adolescents (aged birth-19 years), brain cancer has surpassed leukemia as the leading cause of cancer death because of the dramatic therapeutic advances against leukemia. Accelerating progress against cancer requires both increased national investment in cancer research and the application of existing cancer control knowledge across all segments of the population.

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Cancer statistics, 2016

Context Despite decades of accumulated observational evidence, the balance of risks and benefits for hormone use in healthy postmenopausal women remains uncertain Objective To assess the major health benefits and risks of the most commonly used combined hormone preparation in the United States Design Estrogen plus progestin component of the Women's Health Initiative, a randomized controlled primary prevention trial (planned duration, 85 years) in which 16608 postmenopausal women aged 50-79 years with an intact uterus at baseline were recruited by 40 US clinical centers in 1993-1998 Interventions Participants received conjugated equine estrogens, 0625 mg/d, plus medroxyprogesterone acetate, 25 mg/d, in 1 tablet (n = 8506) or placebo (n = 8102) Main outcomes measures The primary outcome was coronary heart disease (CHD) (nonfatal myocardial infarction and CHD death), with invasive breast cancer as the primary adverse outcome A global index summarizing the balance of risks and benefits included the 2 primary outcomes plus stroke, pulmonary embolism (PE), endometrial cancer, colorectal cancer, hip fracture, and death due to other causes Results On May 31, 2002, after a mean of 52 years of follow-up, the data and safety monitoring board recommended stopping the trial of estrogen plus progestin vs placebo because the test statistic for invasive breast cancer exceeded the stopping boundary for this adverse effect and the global index statistic supported risks exceeding benefits This report includes data on the major clinical outcomes through April 30, 2002 Estimated hazard ratios (HRs) (nominal 95% confidence intervals [CIs]) were as follows: CHD, 129 (102-163) with 286 cases; breast cancer, 126 (100-159) with 290 cases; stroke, 141 (107-185) with 212 cases; PE, 213 (139-325) with 101 cases; colorectal cancer, 063 (043-092) with 112 cases; endometrial cancer, 083 (047-147) with 47 cases; hip fracture, 066 (045-098) with 106 cases; and death due to other causes, 092 (074-114) with 331 cases Corresponding HRs (nominal 95% CIs) for composite outcomes were 122 (109-136) for total cardiovascular disease (arterial and venous disease), 103 (090-117) for total cancer, 076 (069-085) for combined fractures, 098 (082-118) for total mortality, and 115 (103-128) for the global index Absolute excess risks per 10 000 person-years attributable to estrogen plus progestin were 7 more CHD events, 8 more strokes, 8 more PEs, and 8 more invasive breast cancers, while absolute risk reductions per 10 000 person-years were 6 fewer colorectal cancers and 5 fewer hip fractures The absolute excess risk of events included in the global index was 19 per 10 000 person-years Conclusions Overall health risks exceeded benefits from use of combined estrogen plus progestin for an average 52-year follow-up among healthy postmenopausal US women All-cause mortality was not affected during the trial The risk-benefit profile found in this trial is not consistent with the requirements for a viable intervention for primary prevention of chronic diseases, and the results indicate that this regimen should not be initiated or continued for primary prevention of CHD

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Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results from the Women's Health Initiative randomized controlled trial

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

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Finding the missing heritability of complex diseases

The Clayton-Oakes bivariate failure time model is extended to dimensions m > 2 in a manner that allows unspecified marginal survivor functions for all dimensions less than m. Special cases that allow unspecified marginal survivor functions of dimension q with q < m, while making some provisions for dependencies of dimension greater than q, are also described.

Higher Dimensional Clayton-Oakes Models for Multivariate Failure Time Data.

Prentice and Sheppard (1995, Biometrika 82, 113-125) proposed a method for estimating relative risks associated with poorly measured exposures using disease rates from multiple populations and exposure and confounding factor data from sample surveys of persons in each population. The method involved an assumption of independence of disease rates across populations, conditional on exposures and confounding factors. Here, this assumption is relaxed by allowing dependencies among the disease rates within a partition of the populations, perhaps defined on the basis of geographic proximity or cultural similarity. Such dependencies could, for example, derive from unmeasured risk factors that are shared among neighboring populations. Dependencies within an element of the partition are modeled by allowing a common correlation between the disease rates of contiguous populations and by inducing correlations between the rates for noncontiguous populations using a multivariate lognormal assumption. Estimating equations are proposed for relative risk parameter estimation, and robustness and efficiency properties are assessed through simulations. The relaxed estimation procedure is shown to yield useful efficiency gains when moderate or strong disease rate dependencies are present, especially when disease rate variances are large relative to binomial variance.

On the accommodation of disease rate correlations in aggregate data studies of disease risk factors.

In an invited editorial, Dr Shapiro proposes that vaginal bleeding leading to unblinding and subsequent detection bias explains the breast cancer increase seen with estrogen plus progestin in the Women's Health Initiative (WHI) clinical trial (1) . In the context of a uniform detection program of protocol-mandated annual mammography and breast examinations, such a proposal is medically implausible. Dr Shapiro suggests detection bias would identify a larger number of 'slowly growing tumors that would otherwise remain clinically silent'. The findings of more advanced cancers with increased deaths from breast cancer in the estrogen plus progestin group refute this conjecture. During early post-intervention phases of both WHI hormone therapy trials, when breast cancer detection bias is asserted by Dr Shapiro because participants had been informed of randomization assignment, breast cancer incidence rates were lower (rather than higher) than during intervention. Thus, Dr Shapiro's claims are directly refuted by findings from the WHI randomized clinical trials. Health-care providers should be aware that randomized clinical trial evidence supports estrogen plus progestin increasing breast cancer incidence and deaths from breast cancer. In contrast, among women with prior hysterectomy, randomized clinical trial evidence supports estrogen alone reducing breast cancer incidence and deaths from breast cancer.

Reliable evidence from placebo-controlled, randomized, clinical trials for menopausal hormone therapy's influence on incidence and deaths from breast cancer

Ross L. Prentice

Papers

Higher Dimensional Clayton-Oakes Models for Multivariate Failure Time Data.

On the accommodation of disease rate correlations in aggregate data studies of disease risk factors.

Reliable evidence from placebo-controlled, randomized, clinical trials for menopausal hormone therapy's influence on incidence and deaths from breast cancer

Intake biomarkers and the chronic disease nutritional epidemiology research agenda.

Wiley Series in Probability and Statistics