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Evaluation and comparison of predictive individual-level general surrogates.

ABSTRACT: An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.

SUBMITTER: Gabriel EE

PROVIDER: S-EPMC5991262 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Evaluation and comparison of predictive individual-level general surrogates.

Gabriel Erin E EE Sachs Michael C MC Halloran M Elizabeth ME

Biostatistics (Oxford, England) 20180701 3

An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have ...[more]

PMID: 28968890

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Project description:BackgroundSeveral studies have investigated the association of insulin resistance (IR) surrogates and the risk of hypertension. However, it is unclear whether there exist differences between different IR surrogates and hypertension risk. Therefore, this study aimed to explore the association of four IR surrogates (triglyceride-glucose index (TyG index), triglyceride-glucose index with body mass index (TyG-BMI), triglycerides/high-density lipoprotein cholesterol ratio (TG/HDL-c), and metabolic score for IR (METS-IR)) with the prevalence of hypertension.MethodsThis is a cross-sectional study with a total of 117,056 participants. Data were extracted from a computerized database established by Rich Healthcare Group in China, which included all medical records of participants who received a health check-up from 2010 to 2016. IR surrogates were grouped into quartiles as continuous variables, and multivariate logistic regression was performed to estimate the association between different IR surrogate levels and the prevalence of hypertension. Results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Missing data were accounted by multiple imputation. These analyses were considered as the sensitivity analysis. Meanwhile, the Bayesian network (BN) model was constructed to further evaluate the relationship between baseline characteristics and the four IR surrogates and the prevalence of hypertension, as well as the importance of every single variable for the prevalence of hypertension.ResultsMultivariate logistic regression analysis revealed that TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension that increased significantly with increasing TyG-BMI and METS-IR (p for trend < 0.001). The area under the TyG-BMI curve (AUC) was 0.681 [95% CI: 0.677-0.685], and the cut-off value was 199.5, with a sensitivity and specificity of 65.57% and 61.18%, respectively. While the area under the METS-IR curve (AUC) was 0.679 [95% CI: 0.674-0.683], and the cut-off value was 33.61, with a sensitivity and specificity of 69.67% and 56.67%, respectively. The BN model presented that among these four IR surrogates and related variables, TyG-BMI was the most important predictor of hypertension prevalence, with a significance of 34%. The results before and after multiple imputation were similar.ConclusionTyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension. TyG-BMI and METS-IR had good predictive value for the prevalence of hypertension, and TyG-BMI was superior to METS-IR.

Project description:Rapid social change has given rise to a general increase in psychological pressure, which has led to more and more Chinese people suffering from depression over the past 30 years. Depression was influenced not only by individual factors but also by social factors, such as economy, culture, politics, etc. These social factors were measured at the national, provincial, or community levels. However, little literature reported the influence of province-level factors on the depression of Chinese. This study examined the effects of province-level and individual-level factors on depression of Chinese respondents aged 16-97 years. We conducted a multilevel analysis of the 2018 wave survey of the Chinese Family Panel Studies (CFPS), with 19,072 respondents nested within the 25 Chinese provinces. Data for the province-level were extracted from the National Bureau of Statistics of China, including three predictors: gross regional product (GRP) per capita, expenditure for social security and employment (ESSE), and rural and urban household income inequality. Depression was measured with the eight-item short version of the Center for Epidemiologic Studies Depression Scale (CES-D8). The study found that respondents who were female, 30-59 years, divorced or widowed, less educated, rural residents, less body mass index (BMI), or had lower household income tended to report higher levels of depressive symptoms. After adjustment for individual-level features, a significant effect of provinces still survived. The respondents who lived in a province with higher GRP, higher ESSE, or smaller rural and urban household income inequality reported lower depressive symptoms. Our results demonstrated that individual features did not fully explain depression. Economic and social factors appeared to impact depression and have to be considered when the government planned for improved public depression. Meanwhile, our research also provided a suggestion for the government of some provinces to investigate and improve depression.

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Evaluation and comparison of predictive individual-level general surrogates.

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Evaluation and comparison of predictive individual-level general surrogates.

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