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Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria.

ABSTRACT: Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome.Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data.Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET.The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

SUBMITTER: Foy M

PROVIDER: S-EPMC3112196 | biostudies-other | 2011 May

REPOSITORIES: biostudies-other

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Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria.

Foy Millennia M Chen Xing X Kimmel Marek M Gorlova Olga Y OY

BMC medical research methodology 20110511

<h4>Background</h4>Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, ...[more]

PMID: 21569346

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Project description:ObjectivesTo develop a prognostic model for predicting mortality at time of extracorporeal membrane oxygenation initiation for children which is important for determining center-specific risk-adjusted outcomes.DesignMultivariable logistic regression using a large national cohort of pediatric extracorporeal membrane oxygenation patients.SettingThe ICUs of the eight tertiary care children's hospitals of the Collaborative Pediatric Critical Care Research Network.PatientsFive-hundred fourteen children (< 19 yr old), enrolled with an initial extracorporeal membrane oxygenation run for any indication between January 2012 and September 2014.InterventionsNone.Measurements and main resultsA total of 514 first extracorporeal membrane oxygenation runs were analyzed with an overall mortality of 45% (n = 232). Weighted logistic regression was used for model selection and internal validation was performed using cross validation. The variables included in the Pediatric Extracorporeal Membrane Oxygenation Prediction model were age (pre-term neonate, full-term neonate, infant, child, and adolescent), indication for extracorporeal membrane oxygenation (extracorporeal cardiopulmonary resuscitation, cardiac, or respiratory), meconium aspiration, congenital diaphragmatic hernia, documented blood stream infection, arterial blood pH, partial thromboplastin time, and international normalized ratio. The highest risk of mortality was associated with the presence of a documented blood stream infection (odds ratio, 5.26; CI, 1.90-14.57) followed by extracorporeal cardiopulmonary resuscitation (odds ratio, 4.36; CI, 2.23-8.51). The C-statistic was 0.75 (95% CI, 0.70-0.80).ConclusionsThe Pediatric Extracorporeal Membrane Oxygenation Prediction model represents a model for predicting in-hospital mortality among children receiving extracorporeal membrane oxygenation support for any indication. Consequently, it holds promise as the first comprehensive pediatric extracorporeal membrane oxygenation risk stratification model which is important for benchmarking extracorporeal membrane oxygenation outcomes across many centers.

Project description:BackgroundBreast cancer (BC) is the most common cancer type in women. The purpose of this study was to assess the eligibility criteria in recent clinical trials in BC, especially those that can limit the enrollment of older patients as well as those with comorbidities and poor performance status.MethodsData on clinical trials in BC were extracted from ClinicalTrials.gov. Co-primary outcomes were proportions of trials with different types of the eligibility criteria. Associations between trial characteristics and the presence of certain types of these criteria (binary variable) were determined with univariate and multivariate logistic regression.ResultsOur analysis included 522 trials of systemic anticancer treatments started between 2020 and 2022. Upper age limits, strict exclusion criteria pertaining to comorbidities, and those referring to inadequate performance status of the patient were used in 204 (39%), 404 (77%), and 360 (69%) trials, respectively. Overall, 493 trials (94%) had at least one of these criteria. The odds of the presence of each type of the exclusion criteria were significantly associated with investigational site location and trial phase. We also showed that the odds of the upper age limits and the exclusion criteria involving the performance status were significantly higher in the cohort of recent trials compared with cohort of 309 trials started between 2010 and 2012 (39% vs 19% and 69% vs 46%, respectively; p < 0.001 for univariate and multivariate analysis in both comparisons). The proportion of trials with strict exclusion criteria was comparable between the two cohorts (p > 0.05). Only three of recent trials (1%) enrolled solely patients aged 65 or 70 and older.ConclusionsMany recent clinical trials in BC exclude large groups of patients, especially older adults, individuals with different comorbidities, and those with poor performance status. Careful modification of some of the eligibility criteria in these trials should be considered to allow investigators to assess the benefits and harms of investigational treatments in participants with characteristics typically encountered in clinical practice.

Dataset Information

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria.

Publications

Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria.

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