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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

A minimum version of log-rank test for testing the existence of cancer cure using relative survival data.

Cancer survival is one of the most important measures to evaluate the effectiveness of treatment and early diagnosis. The ultimate goal of cancer research and patient care is the cure of cancer. As cancer treatments progress, cure becomes a reality for many cancers if patients are diagnosed early and get effective treatment. If a cure does exist for a certain type of cancer, it is useful to estimate the time of cure. For cancers that impose excess risk of mortality, it is informative to understand the difference in survival between cancer patients and the general cancer-free population. In population-based cancer survival studies, relative survival is the standard measure of excess mortality due to cancer. Cure is achieved when the survival of cancer patients is equivalent to that of the general population. This definition of cure is usually called the statistical cure, which is an important measure of burden due to cancer. In this paper, a minimum version of the log-rank test is proposed to test the equivalence of cancer patients' survival using the relative survival data. Performance of the proposed test is evaluated by simulation. Relative survival data from population-based cancer registries in SEER Program are used to examine patients' survival after diagnosis for various major cancer sites.

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.

Is age-related decline in lean mass and physical function accelerated by obstructive lung disease or smoking?

BACKGROUND: and aims Cross-sectional studies suggest that obstructive lung disease (OLD) and smoking affect lean mass and mobility. A study was undertaken to investigate whether OLD and smoking accelerate the ageing-related decline in lean mass and physical functioning. METHODS: 260 patients with OLD (mean±SD forced expiratory volume in 1 s (FEV1) 63±18% predicted), 157 smoking controls (FEV(1) 95±16% predicted), 866 former-smoking controls (FEV1 100±16% predicted) and 891 never-smoking controls (FEV1 104±17% predicted) participating in the Health, Aging and Body Composition (ABC) Study were studied. At baseline the mean age was 74±3 years and participants reported no functional limitations. Baseline and 7-year longitudinal data of body composition (by dual-energy x-ray absorptiometry), muscle strength (by hand and leg dynamometry) and Short Physical Performance Battery (SPPB) were investigated. RESULTS: Compared with never-smoking controls, patients with OLD and smoking controls had a significantly lower weight, fat mass, lean mass and bone mineral content (BMC) at baseline (p<0.05). While the loss of weight, fat mass, lean mass and strength was comparable between patients with OLD and never-smoking controls, the SPPB declined 0.12 points/year faster in men with OLD (p=0.01) and BMC declined 4 g/year faster in women with OLD (p=0.02). In smoking controls only lean mass declined 0.1 kg/year faster in women (p=0.03) and BMC 8 g/year faster in men (p=0.02) compared with never-smoking controls. CONCLUSIONS: Initially well-functioning older adults with mild-to-moderate OLD and smokers without OLD have a comparable compromised baseline profile of body composition and physical functioning, while 7-year longitudinal trajectories are to a large extent comparable to those observed in never-smokers without OLD. This suggests a common insult earlier in life related to smoking.

Combining multiple continuous tests for the diagnosis of kidney impairment in the absence of a gold standard.

Receiver operating characteristic (ROC) curves are commonly used to summarize the classification accuracy of diagnostic tests. It is not uncommon in medical practice that multiple diagnostic tests are routinely performed or multiple disease markers are available for the same individuals. When the true disease status is verified by a gold standard (GS) test, a variety of methods have been proposed to combine such potential correlated tests to increase the accuracy of disease diagnosis. In this article, we propose a method of combining multiple diagnostic tests in the absence of a GS. We assume that the test values and their classification accuracies are dependent on covariates. Simulation studies are performed to examine the performance of the combination method. The proposed method is applied to data from a population-based aging study to compare the accuracy of three screening tests for kidney function and to estimate the prevalence of moderate kidney impairment.

Estimating age-specific incidence of dementia using prevalent cohort data.

In prospective cohort studies individuals are usually recruited according to a certain cross-sectional sampling criterion. The prevalent cohort is defined as a group of individuals who are alive but possibly with disease at the beginning of the study. It is appealing to incorporate the prevalent cases to estimate the incidence rate of disease before the enrollment. The method of back calculation of incidence rate has been used to estimate the incubation time from HIV infection to AIDS. The time origin is defined as the time of HIV infection. In aging cohort studies, the primary time scale is age of disease onset, subjects have to survive certain years to be enrolled into the study, thus creating left truncation (delay entry). The current methods usually assume that either the disease incidence is rare or the excess mortality due to disease is small compared to the healthy subjects. By far the validity of the results based on these assumptions has not been examined. In this paper, a simple alternative method is proposed to estimate dementia incidence rate before enrollment using prevalent cohort data with left truncation. Furthermore simulations are used to examine the performance of the estimation of disease incidence under different assumptions of disease incidence rates and excess mortality hazards due to disease. As application, the method is applied to the prevalent cases of dementia from the Honolulu Asia Aging Study to estimate dementia incidence rate and to assess the effect of hypertension, Apoe 4 and education on dementia onset.

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.

Misreporting of energy intake in the elderly using doubly labeled water to measure total energy expenditure and weight change.

BACKGROUND: One of the major problems in dietary assessment is inaccuracy in reporting diet. OBJECTIVE: To examine the association between self-reported energy intake (EI) by food frequency questionnaire (FFQ) and energy expenditure (EE), measured by doubly labeled water (DLW), among older persons. DESIGN: EE was assessed in 298 high-functioning, community-dwelling older adults (70-79 years of age) over a 2-week period using DLW. Dietary intake was assessed using a Block FFQ. The ratio between reported EI and total energy expenditure (TEE) was calculated. Misreporting was defined as follows: participants with an EI/TEE ratio of <0.77 were categorized as low energy reporters, while participants with an EI/TEE ratio >1.28 were categorized as high energy reporters. Participants with an EI/TEE ratio of 0.77-1.28 were categorized as "true" energy reporters. One-year percent weight change prior to EE visit was used as another validation indicator. Participants who were low energy reporters but lost >2% of their body weight were categorized as undereaters. RESULTS: Two hundred ninety-six participants provided both FFQ and DLW measurements. Forty-three percent of participants were low energy reporters; among them, almost 30% lost weight and, therefore, were categorized as undereaters. The undereaters consumed significantly fewer calories. No difference in the frequency of low energy reporting was detected between genders or racial groups. Underreporters had significantly higher body weight than "true" or high reporters. Undereaters tended to have higher body mass index than the underreporters. CONCLUSIONS: Undereating is prevalent in the elderly and may be falsely perceived as underreporting. It should be further addressed and characterized in future studies.

Joint modeling for cognitive trajectory and risk of dementia in the presence of death.

Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal aging. It has been known that cognitive impairment occurs long before the diagnosis of dementia. For individuals who develop dementia, it is important to determine the time when the rate of cognitive decline begins to accelerate and the subsequent gap time to dementia diagnosis. For normal aging individuals, it is also useful to understand the trajectory of cognitive function until their death. A Bayesian change-point model is proposed to fit the trajectory of cognitive function for individuals who develop dementia. In real life, people in older ages are subject to two competing risks, e.g., dementia and dementia-free death. Because the majority of people do not develop dementia, a mixture model is used for survival data with competing risks, which consists of dementia onset time after the change point of cognitive function decline for demented individuals and death time for nondemented individuals. The cognitive trajectories and the survival process are modeled jointly and the parameters are estimated using the Markov chain Monte Carlo method. Using data from the Honolulu Asia Aging Study, we show the trajectories of cognitive function and the effect of education, apolipoprotein E 4 genotype, and hypertension on cognitive decline and the risk of dementia.

A Bayesian MCMC approach to survival analysis with doubly-censored data.

Doubly-censored data refers to time to event data for which both the originating and failure times are censored. In studies involving AIDS incubation time or survival after dementia onset, for example, data are frequently doubly-censored because the date of the originating event is interval-censored and the date of the failure event usually is right-censored. The primary interest is in the distribution of elapsed times between the originating and failure events and its relationship to exposures and risk factors. The estimating equation approach [Sun, et al. 1999. Regression analysis of doubly censored failure time data with applications to AIDS studies. Biometrics 55, 909-914] and its extensions assume the same distribution of originating event times for all subjects. This paper demonstrates the importance of utilizing additional covariates to impute originating event times, i.e., more accurate estimation of originating event times may lead to less biased parameter estimates for elapsed time. The Bayesian MCMC method is shown to be a suitable approach for analyzing doubly-censored data and allows a rich class of survival models. The performance of the proposed estimation method is compared to that of other conventional methods through simulations. Two examples, an AIDS cohort study and a population-based dementia study, are used for illustration. Sample code is shown in the appendix.