Converting Data into Evidence

DeMaris, Alfred; Selman, Steven H.

doi:10.1007/978-1-4614-7792-1

Cited by 7 publications

(5 citation statements)

References 48 publications

(81 reference statements)

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“…The positive interaction suggested that being enrolled in college was more protective for males compared to females. For ease of interpretation, the models were run separately for males and females, and I calculated the percent change in odds (100 × (OR-1)) (Demaris & Selman, 2013). Males enrolled in college (OR = 0.36; p < .000) had lower odds of an STI compared to their male non-college enrolled counterpart.…”

Section: Resultsmentioning

confidence: 99%

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

Lyons

2016

International Journal of Sexual Health

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Casual sex is common during the emerging adult life course stage, but little research has taken a person-centered approach to investigate if casual sexual behavior influences STI rates. Using a nationally representative sample and latent class analysis, results showed three distinctive latent classes. Abstainers were the least likely to have an STI, followed by the casual sex experienced, and then the casual sex risk-takers. Once other covariates were included in the model, there was no significant difference between the abstainers and casual sex experienced classes. These results highlight the need for future research to include diverse samples of emerging adults.

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Section: Resultsmentioning

confidence: 99%

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

Lyons

2016

International Journal of Sexual Health

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“…In fact, conventional C-Index estimators are dependent on the distribution of truncation times, meaning the resulting C-Index values may not solely reflect a model's ability to discriminate risk under left-truncation. Because the C-Index continues to be used heavily for scrutinizing survival models, this limitation has important consequences (DeMaris and Selman, 2013). Analogous to the performance of Uno's C-Index on right-censored survival data, our IPW approach for lefttruncation was highly effective at removing the C-Index's dependence on the left-truncation distribution.…”

Section: Discussionmentioning

confidence: 99%

“…The C-Index examines pairs of subjects from a sample to determine how often the order of the observed failure times is concordant with the order of the predicted risk scores (Harrell et al, 1982(Harrell et al, , 1996. In practice, the C-Index value is frequently used to decide whether a survival model performs well enough at risk discrimination to be published or implemented in practice (DeMaris and Selman, 2013).…”

Section: Introductionmentioning

confidence: 99%

Concordance Indices with Left-Truncated and Right-Censored Data

Hartman

Kim

et al. 2022

Biometrics

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In the context of time‐to‐event analysis, a primary objective is to model the risk of experiencing a particular event in relation to a set of observed predictors. The Concordance Index (C‐Index) is a statistic frequently used in practice to assess how well such models discriminate between various risk levels in a population. However, the properties of conventional C‐Index estimators when applied to left‐truncated time‐to‐event data have not been well studied, despite the fact that left‐truncation is commonly encountered in observational studies. We show that the limiting values of the conventional C‐Index estimators depend on the underlying distribution of truncation times, which is similar to the situation with right‐censoring as discussed in Uno et al. (2011) [On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30(10), 1105–1117]. We develop a new C‐Index estimator based on inverse probability weighting (IPW) that corrects for this limitation, and we generalize this estimator to settings with left‐truncated and right‐censored data. The proposed IPW estimators are highly robust to the underlying truncation distribution and often outperform the conventional methods in terms of bias, mean squared error, and coverage probability. We apply these estimators to evaluate a predictive survival model for mortality among patients with end‐stage renal disease.

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“…The C-index is a goodness-of-fit measurement commonly used for survival analysis models with censored data, analogous to the area under the ROC curve (AUC) for more classic predictive models and diagnostic tests [43,44]. Based on the existing literature, the C-index for a survival analysis model should be at least 0.7 to adequately discriminate between risk profiles [45][46][47]. For all simulation runs, assumed vaccine efficacy was 60% and the trial follow-up period was 1.5 years, following the work of Page et al [2].…”

Section: Evaluating Predictee Hcv Vaccine Trial In Chicago Pwid Popul...mentioning

confidence: 99%

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs

Chiu,

Tatara,

Mackesy-Amiti

et al. 2024

Healthcare

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Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate’s probability of HCV infection during the trial. The decision to recruit considers both the candidate’s predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

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Converting Data into Evidence

Cited by 7 publications

References 48 publications

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

Heterosexual Casual Sex and STI Diagnosis: A Latent Class Analysis

Concordance Indices with Left-Truncated and Right-Censored Data

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs

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