Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

BACKGROUND: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. METHODS: The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. RESULTS: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively. CONCLUSION: In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Evolution of cooperation in social dilemmas with assortment in finite populations.

We investigate conditions for the evolution of cooperation in social dilemmas in finite populations with assortment of players by group founders and general payoff functions for cooperation and defection within groups. Using a diffusion approximation in the limit of a large population size that does not depend on the precise updating rule, we show that the first-order effect of selection on the fixation probability of cooperation when represented once can be expressed as the difference between time-averaged payoffs with respect to effective time that cooperators and defectors spend in direct competition in the different group states. Comparing this fixation probability to its value under neutrality and to the corresponding fixation probability for defection, we deduce conditions for the evolution of cooperation. We show that these conditions are generally less stringent as the level of assortment increases under a wide range of assumptions on the payoffs such as additive, synergetic or discounted benefits for cooperation, fixed cost for cooperation and threshold benefit functions. This is not necessarily the case, however, when payoffs in pairwise interactions are multiplicatively compounded within groups.

Incidences of colorectal adenomas and cancers under colonoscopy surveillance suggest an accelerated "Big Bang" pathway to CRC in three of the four Lynch syndromes.

BACKGROUND: Colorectal cancers (CRCs) in the Lynch syndromes have been assumed to emerge through an accelerated adenoma-carcinoma pathway. In this model adenomas with deficient mismatch repair have an increased probability of acquiring additional cancer driver mutation(s) resulting in more rapid progression to malignancy. If this model was accurate, the success of colonoscopy in preventing CRC would be a function of the intervals between colonoscopies and mean sojourn time of detectable adenomas. Contrary to expectations, colonoscopy did not decrease incidence of CRC in the Lynch syndromes and shorter colonoscopy intervals have not been effective in reducing CRC incidence. The prospective Lynch Syndrome Database (PLSD) was designed to examine these issues in carriers of pathogenic variants of the mis-match repair (path_MMR) genes. MATERIALS AND METHODS: We examined the CRC and colorectal adenoma incidences in 3,574 path_MLH1, path_MSH2, path_MSH6 and path_PMS2 carriers subjected to regular colonoscopy with polypectomy, and considered the results based on sojourn times and stochastic probability paradigms. RESULTS: Most of the path_MMR carriers in each genetic group had no adenomas. There was no association between incidences of CRC and the presence of adenomas. There was no CRC observed in path_PMS2 carriers. CONCLUSIONS: Colonoscopy prevented CRC in path_PMS2 carriers but not in the others. Our findings are consistent with colonoscopy surveillance blocking the adenoma-carcinoma pathway by removing identified adenomas which might otherwise become CRCs. However, in the other carriers most CRCs likely arised from dMMR cells in the crypts that have an increased mutation rate with increased stochastic chaotic probabilities for mutations. Therefore, this mechanism, that may be associated with no or only a short sojourn time of MSI tumours as adenomas, could explain the findings in our previous and current reports.

Overdiagnosis in lung cancer screening: Estimates from the German Lung Cancer Screening Intervention Trial.

Overdiagnosis is a major potential harm of lung cancer screening; knowing its potential magnitude helps to optimize screening eligibility criteria. The German Lung Screening Intervention Trial ("LUSI") is a randomized trial among 4052 long-term smokers (2622 men), 50.3 to 71.9 years of age from the general population around Heidelberg, Germany, comparing five annual rounds of low-dose computed tomography (n = 2029) with a control arm without intervention (n = 2023). After a median follow-up of 9.77 years postrandomization and 5.73 years since last screening, 74 participants were diagnosed with lung cancer in the control arm and 90 in the screening arm: 69 during the active screening period; of which 63 screen-detected and 6 interval cancers. The excess cumulative incidence in the screening arm (N = 16) represented 25.4% (95% confidence interval: -11.3, 64.3] of screen-detected cancer cases (N = 63). Analyzed by histologic subtype, excess incidence in the screening arm appeared largely driven by adenocarcinomas. Statistical modeling yielded an estimated mean preclinical sojourn time (MPST) of 5.38 (4.76, 5.88) years and a screen-test sensitivity of 81.6 (74.4%, 88.8%) for lung cancer overall, all histologic subtypes combined. Based on modeling, we further estimated that about 48% (47.5% [43.2%, 50.7%]) of screen-detected tumors have a lead time ≥4 years, whereas about 33% (32.8% [28.4%, 36.1%]) have a lead time ≥6 years, 23% (22.6% [18.6%, 25.7%]) ≥8 years, 16% (15.6% [12.2%, 18.3%]) ≥10 years and 11% (10.7% [8.0%, 13.0%]) ≥12 years. The high proportions of tumors with relatively long lead times suggest a major risk of overdiagnosis for individuals with comparatively short remaining life expectancies.

A multistate survival model of the natural history of cancer using data from screened and unscreened population.

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen-detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous-time Markov model and the corresponding likelihood function. The model allows for the use of interval-censored, left-truncated and right-censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age-varying hazards and age-varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro-simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately.

Fasting Blood Glucose Levels Provide Estimate of Duration and Progression of Pancreatic Cancer Before Diagnosis.

BACKGROUND & AIMS: It is unclear how long pancreatic ductal adenocarcinomas (PDACs) are present before diagnosis. Patients with PDAC usually develop hyperglycemia and diabetes before the tumor is identified. If early invasive PDACs are associated with hyperglycemia, the duration of hyperglycemia should associate with the time that they have had the tumor. METHODS: We collected data on patients with PDACs from medical databases in Olmsted County, Minnesota, from 2000 through 2015 and from the Mayo Clinic's tumor registry from January 1, 1976, through January 1, 2017. We compared glycemic profiles of patients with PDAC (cases) compared with patients without cancer, matched for age and sex (controls). We analyzed temporal fasting blood glucose (FBG) profiles collected for 60 months before patients received a PDAC diagnosis (index date) (n = 219) (cohort A), FBG profiles of patients with resected PDAC (n = 526) stratified by tumor volume and grade (cohort B), and temporal FBG profiles of patients with resected PDACs from whom long-term FBG data were available (n = 103) (cohort C). The primary outcome was to estimate duration of presence of invasive PDAC before its diagnosis based on hyperglycemia, defined as significantly higher (P < .05) FBG levels in cases compared with controls. RESULTS: In cohort A, the mean FBG did not differ significantly between cases and controls 36 months before the index date. Hyperglycemia was first noted 36 to 30 months before PDAC diagnosis in all cases, those with or without diabetes at baseline and those with or without resection at diagnosis. FBG level increased until diagnosis of PDAC. In cohort B, the mean FBG did not differ significantly in controls vs cases with PDACs below 1.0 mL. The smallest tumor volume associated with hyperglycemia was 1.1 to 2.0 mL; FBG level increased with tumor volume. FBG varied with tumor grade: well- or moderately differentiated tumors (5.8 mL) produced the same FBG levels as smaller, poorly differentiated tumors (1.5 mL) (P < .001). In cohort C, the duration of prediagnostic hyperglycemia for cases with large-, medium-, or small-volume PDACs was 36 to 24, 24 to 12, and 12 to 0 months, respectively. PDAC resection resolved hyperglycemia, regardless of tumor location. CONCLUSIONS: In a case-control study of patients with PDAC from 2 databases, we associated FBG level with time to PDAC diagnosis and tumor volume and grade. Patients are hyperglycemic for a mean period of 36 to 30 months before PDAC diagnosis; this information might be incorporated into strategies for early detection.

Estimands to quantify prolonged hospital stay associated with nosocomial infections.

BACKGROUND: Length of stay evaluations are very common to determine the burden of nosocomial infections. However, there exist fundamentally different methods to quantify the prolonged length of stay associated with nosocomial infections. Previous methodological studies emphasized the need to account for the timing of infection in order to differentiate the length of stay before and after the infection. METHODS: We derive four different approaches in a simple multi-state framework, display their mathematical relationships in a multiplicative as well as additive way and apply them to a real cohort study (n=756 German intensive-care unit patients of whom 124 patients acquired a nosocomial infection). RESULTS: The first approach ignores the timing of infection and quantifies the difference of eventually infected and eventually uninfected; it is 12.31 days in the real data. The second approach compares the average sojourn time with infection with the average sojourn time of being hypothetically uninfected; it is 2.12 days. The third one compares the average length of stay of a population in a world with nosocomial infections with a population in a hypothetical world without nosocomial infections; it is 0.35 days. Finally, approach four compares the mean residual length of stay between currently infected and uninfected patients on a daily basis; the difference is 1.77 days per infected patient. CONCLUSIONS: The first approach should be avoided because it compares the eventually infected with the eventually uninfected, but has no prospective interpretation. The other approaches differ in their interpretation but are suitable because they explicitly distinguish between the pre- and post-time of the nosocomial infection.

Spectrum Handoffs Based on Preemptive Repeat Priority Queue in Cognitive Radio Networks.

Cognitive radio can significantly improve the spectrum efficiency, and spectrum handoff is considered as an important functionality to guarantee the quality of service (QoS) of primary users (PUs) and the continuity of data transmission of secondary users (SUs). In this paper, we propose an analytical framework based on a preemptive repeat identical (PRI) M/G/1 queuing network model to characterize spectrum handoff behaviors with general service time distribution of both primary and secondary connections, multiple interruptions and transmission delay resulting from the appearance of primary connections. Then, we derive the close-expression of the extended data delivery and the system sojourn time in both staying and changing scenarios. In addition, based on analysis of spectrum handoff behaviors resulting from multiple interruptions caused by the appearance of the primary connections, we investigate the traffic-adaptive policy, by which the considered SU will optimally adjust its handoff spectrum policy. Moreover, we investigate the admissible region and provide the reference for designing the admission control rule for the arriving secondary connection requests. Finally, simulation results verify that our proposed analytical framework is reasonable and can provide the reference for executing the optimal spectrum handoff strategy and designing the admission control rule for the SU in cognitive radio networks.

Quantification of length-bias in screening trials with covariate-dependent test sensitivity.

Length-biased sampling exists in screening programs where longer duration disease is detected during the preclinical stage because a longer sojourn time (preclinical duration) has a higher probability of being screen detected. By modeling the course of disease, we quantify the effect of length-biased sampling on clinical duration when cases are subject to periodic screening with variable test sensitivity. We use the highly flexible bivariate lognormal density to jointly model preclinical and clinical durations, and we model screening test sensitivity as a function of the sojourn time and number of previous false negative screens. We show that the mean clinical duration among screen-detected cases can be up to 40% higher, with shrinking standard deviation, than those among nonscreen-detected cases, due to biased sampling alone, irrespective of any possible benefit (increased survival time arising from earlier detection or reduction in mortality). These findings will aid in the design and interpretation of screening trials.

Tumor Doubling Time and Screening Interval.

The goal of screening is to detect breast cancers when still curable to decrease breast cancer-specific mortality. Breast cancer screening in the United States is routinely performed with digital mammography and digital breast tomosynthesis. This article reviews breast cancer doubling time by tumor subtype and examines the impact of doubling time on breast cancer screening intervals. By the article's end, the reader will be better equipped to have informed discussions with patients and medical professionals regarding the benefits and disadvantages of the currently recommended screening mammography intervals.

A note on the catch-up time method for estimating lead or sojourn time in prostate cancer screening.

Models of cancer screening assume that cancers are detectable by screening before being diagnosed clinically through symptoms. The duration of this preclinical phase is called sojourn time, and it determines how much diagnosis might be advanced in time by the screening test (lead time). In the catch-up time method, mean sojourn time or lead time are estimated as the time needed for cumulative incidence in an unscreened population to catch up with the detection rate (prevalence) at a first screening test. The method has been proposed as a substitute of the prevalence/incidence ratio in the case of prostate cancer where incidence cannot be treated as a constant. A model is proposed to justify this estimator. It is shown that this model is different from classic Markov-type models developed for breast cancer screening. In both models, the catch-up time method results in biased estimates of mean sojourn time.

Estimation with Right-Censored Observations Under A Semi-Markov Model.

The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability of the semi-Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi-Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women.

PURPOSE: High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women. MATERIALS AND METHODS: We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System. RESULTS: Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times). CONCLUSION: This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Cervical change times during induction in nulliparas using vaginal or buccal misoprostol.

AIM: To determine if the time to cervical change and time to active labor were different when misoprostol was administered by a vaginal or buccal route for cervical ripening in nulliparas undergoing labor induction at term. METHODS: This was a secondary analysis of nulliparous participants in the IMPROVE Study-A comparison of vaginal versus buccal misoprostol for cervical ripening for labor induction at term: a triple-masked randomized controlled trial (NCT02408315). The parent study was a non-inferiority randomized controlled trial in which patients beginning induction with a modified Bishop score ≤6 received either vaginal or buccal misoprostol and simultaneous placebo via the opposite route. The primary outcome of the parent study was time to delivery. Primary outcomes for this secondary analysis were the time to active labor (at least 6 cm dilated) and time to change in cervical dilation. Kaplan-Meier analysis was used to compare routes for time to active labor and multistate Markov modeling was used to compare sojourn times at each cervical dilation. RESULTS: Of the 300 participants enrolled in the parent trial, 124 (41.3%) were nulliparous; 59 (47.6%) nulliparous participants underwent induction with vaginal misoprostol and 65 (52.4%) received buccal dosing. Nulliparas receiving vaginal dosing required fewer doses of misoprostol to reach active labor (median 2 vs 3, p = .003). However, this did not result in shorter time to active labor (median vaginal 23.1 h, 95% CI = [21.6, 27.2 h]; buccal 25.6 h [21.5, 29.3 h], p = .45) or higher rate of vaginal delivery within 24 h; (33.9% vs 35.4%, p = .86). There was also no significant difference in time to active labor after adjusting for covariates (adjusted HR for dose route (buccal vs vaginal) = 0.91 [0.61, 1.36], p = .649). Among people that delivered vaginally, the mean sojourn times, measuring cervical dilation state change, were not significantly different, with mean duration to active labor of 20.5 [17.6, 24.5] h for buccal and 21.8 [17.7, 28.2] h for vaginal dosing (p = .092). Satisfaction and preference for dosing routes were not different between groups. CONCLUSION: Buccal and vaginal dosing of misoprostol for cervical ripening in nulliparas appear to have similar times to active labor and progression of cervical change during ripening.

When to initiate cancer screening exam?

A probability method is developed to decide when to initiate cancer screening for asymptomatic individuals. The probability of incidence is a function of screening sensitivity, time duration in the disease-free state and sojourn time in the preclinical state; and it is monotonically increasing as time increases, given a person's current age. So a unique solution of the first screening time can be found by limiting this probability to a small value, such as 10% or 20%. That is, with 90% or 80% probability, one will not be a clinical incident case before the first exam. After this age is found, we can further estimate the lead time distribution and probability of over-diagnosis if one would be diagnosed with cancer at the first exam. Simulations were carried out under different scenarios; and the method was applied to two heavy smoker cohorts in the National Lung Screening Trial using low-dose computerized tomography. The method is applicable to other kinds of cancer screening. The predictive information can be used by physicians or individuals at risk to make informed decisions on when to initiate screening.

Estimation of Preclinical State Onset Age and Sojourn Time for Heavy Smokers in Lung Cancer.

Estimation of the three key parameters: onset age of the preclinical state, sojourn time and screening sensitivity is critical in cancer screening, since all other terms are functions of the three. A novel link function to connect sensitivity with time in the preclinical state and the likelihood method were used in this project; since sensitivity depends on how long one has entered the preclinical state relative to the total sojourn time. Simulations using Markov Chain Monte Carlo and maximum likelihood estimate were carried out to estimate the key parameters for male and female heavy smokers separately in the low-dose computed tomography group of the National Lung Screening Trial. Sensitivity for male and female heavy smokers were 0.883 and 0.915 respectively at the onset of the preclinical state, and increased to 0.972 and 0.981 at the end. The mean age to make the transition into the preclinical state was 70.94 or 71.15 for male and female heavy smokers respectively, and 90% of heavy smokers at risk for lung cancer would enter the preclinical state in age interval (55.7, 85.8) for males and (54.2, 87.7) for females, and the transition peaked around age 69 for both genders. The mean sojourn time in the preclinical state was 1.43 and 1.49 years, and the 99% credible intervals for the sojourn time were (0.21, 2.96) and (0.37, 2.69) years for male and female heavy smokers correspondingly. Based on the result, low-dose CT should be started at age 55 and ended before 85 for heavy smokers. This provided important information to policy makers.

Inference of Onset Age of Preclinical State and Sojourn Time for Breast Cancer.

Aims: Accurate estimation of the three key parameters (sensitivity, time duration in disease-free state and sojourn time in preclinical state) in cancer screening are critical. Likelihood method with a new link function was applied to the Health Insurance Plan of Greater New York (HIP) breast cancer screening data, to estimate the onset age of preclinical state and the sojourn time in the preclinical state for breast cancer. Materials and Methods: A new link function to model sensitivity as a function of time in the preclinical state and the sojourn time was adopted. Markov Chain Monte Carlo simulations were used to obtain posterior samples and make inference on the three key parameters. Maximum likelihood estimate was also used for comparison. Results: The onset age of the preclinical state has a wide range for breast cancer; the peak onset age was 65.07 years (95% credible interval [C.I.], 55.76 to 73.02). The mean sojourn time was 2.00 years (95% C.I., 0.85 to 2.95). The 95 % C.I. for the sojourn time was 0.16 to 5.53 years. Sensitivity at onset of the preclinical state was 0.75 (95% C.I., 0.54 to 0.88); and sensitivity at the end of the preclinical state was 0.84 (95% C.I., 0.67 to 0.88). Conclusion: The HIP study was the oldest breast cancer mass screening. The estimates reflect key parameters in those days with lower screening sensitivity. However, it is helpful to know other parameters in the planning for future breast cancer screening.

Quantifying the duration of the preclinical detectable phase in cancer screening: a systematic review.

OBJECTIVES: The aim of this study was to provide an overview of published mathematical estimation approaches to quantify the duration of the preclinical detectable phase (PCDP) using data from cancer screening programs. METHODS: A systematic search of PubMed and Embase was conducted for original studies presenting mathematical approaches using screening data. The studies were categorized by mathematical approach, data source, and assumptions made. Furthermore, estimates of the duration of the PCDP of breast and colorectal cancer were reported per study population. RESULTS: From 689 publications, 34 estimation methods were included. Five distinct types of mathematical estimation approaches were identified: prevalence-to-incidence ratio (n=8), maximum likelihood estimation (n=16), expectation-maximization algorithm (n=1), regression of observed on expected (n=6) and Bayesian Markov-chain Monte Carlo estimation (n=5). Fourteen studies used data from both screened and unscreened populations, whereas 19 studies included only information from a screened population. Estimates of the duration of the PCDP varied between 2 years and 7 years for breast cancer in the Health Insurance Plan study (annual mammography and clinical breast examinations in women aged 40-64 years) and 2 years and 5 years for colorectal cancer in the Calvados study (a guaiac fecal occult blood test in men and women aged 45-74 years). CONCLUSIONS: Different types of mathematical approaches lead to different estimates of the PCDP duration. We advise researchers to use the method that matches the data available, and to use multiple methods for estimation when possible, since no method is perfect.

Estimation of semi-Markov multi-state models: a comparison of the sojourn times and transition intensities approaches.

Semi-Markov models are widely used for survival analysis and reliability analysis. In general, there are two competing parameterizations and each entails its own interpretation and inference properties. On the one hand, a semi-Markov process can be defined based on the distribution of sojourn times, often via hazard rates, together with transition probabilities of an embedded Markov chain. On the other hand, intensity transition functions may be used, often referred to as the hazard rates of the semi-Markov process. We summarize and contrast these two parameterizations both from a probabilistic and an inference perspective, and we highlight relationships between the two approaches. In general, the intensity transition based approach allows the likelihood to be split into likelihoods of two-state models having fewer parameters, allowing efficient computation and usage of many survival analysis tools. Nevertheless, in certain cases the sojourn time based approach is natural and has been exploited extensively in applications. In contrasting the two approaches and contemporary relevant R packages used for inference, we use two real datasets highlighting the probabilistic and inference properties of each approach. This analysis is accompanied by an R vignette.

Multistate Models for Examining the Progression of Intermittently Measured Patient-Reported Symptoms Among Patients With Cancer: The Importance of Accounting for Interval Censoring.

CONTEXT: Patients with cancer in Ontario, Canada, receive symptom monitoring in a standardized fashion using the Edmonton Symptom Assessment System (ESAS). These measurements can be used to understand symptom progression during the cancer trajectory. OBJECTIVES: This study demonstrates the implementation of multistate models for examining symptom progression, while appropriately accounting for intermittent observation. We also compare the estimates when the panel nature of the data is ignored. METHODS: This was a population-based retrospective cohort study using linked administrative health-care databases. The cohort consisted of patients who were newly diagnosed with a primary cancer and had at least one ESAS assessment completed between 2007 and 2015 in Ontario, Canada. A 5-state model was developed to examine the progression of symptom severity, where estimation was conducted with and without accommodating for the panel nature of the symptom data. RESULTS: The study cohort consisted of 212,615 patients diagnosed with cancer, collectively having 1,006,360 ESAS assessments within the first year after diagnosis. The median (interquartile range) of the number of ESAS assessments per patient was 3 (1-6), and the average gap time between consecutive assessments was approximately three months. The estimated mean sojourn time in each state was consistently and significantly greater when ignoring interval censoring than when accounting for it. This held true for all states and symptoms. CONCLUSION: Our work demonstrates the use of multistate models and the importance of accommodating for intermittent observation when examining symptom progression using ESAS among patients with cancer. This work serves as a methodological guide for applied researchers interested in modeling disease progression under the presence of intermittent observation.