Dataset Information

Lagging exposure information in cumulative exposure-response analyses.

ABSTRACT: Lagging exposure information is often undertaken to allow for a latency period in cumulative exposure-disease analyses. The authors first consider bias and confidence interval coverage when using the standard approaches of fitting models under several lag assumptions and selecting the lag that maximizes either the effect estimate or model goodness of fit. Next, they consider bias that occurs when the assumption that the latency period is a fixed constant does not hold. Expressions were derived for bias due to misspecification of lag assumptions, and simulations were conducted. Finally, the authors describe a method for joint estimation of parameters describing an exposure-response association and the latency distribution. Analyses of associations between cumulative asbestos exposure and lung cancer mortality among textile workers illustrate this approach. Selecting the lag that maximizes the effect estimate may lead to bias away from the null; selecting the lag that maximizes model goodness of fit may lead to confidence intervals that are too narrow. These problems tend to increase as the within-person exposure variation diminishes. Lagging exposure assignment by a constant will lead to bias toward the null if the distribution of latency periods is not a fixed constant. Direct estimation of latency periods can minimize bias and improve confidence interval coverage.

SUBMITTER: Richardson DB

PROVIDER: S-EPMC3276301 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Lagging exposure information in cumulative exposure-response analyses.

Richardson David B DB Cole Stephen R SR Chu Haitao H Langholz Bryan B

American journal of epidemiology 20111101 12

Lagging exposure information is often undertaken to allow for a latency period in cumulative exposure-disease analyses. The authors first consider bias and confidence interval coverage when using the standard approaches of fitting models under several lag assumptions and selecting the lag that maximizes either the effect estimate or model goodness of fit. Next, they consider bias that occurs when the assumption that the latency period is a fixed constant does not hold. Expressions were derived f ...[more]

PMID: 22047823

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Project description:AimIn the registration trial, cabozantinib exposure ≥ 750 ng/mL correlated to improved tumor size reduction, response rate and progression free survival (PFS) in patients with metastatic renal cell cancer (mRCC). Because patients in routine care often differ from patients in clinical trials, we explored the cabozantinib exposure-response relationship in patients with mRCC treated in routine care.MethodsCabozantinib trough concentrations (Cmin) were collected and average exposure was calculated per individual. Exposure-response analyses were performed using the earlier identified target of Cmin > 750 ng/mL and median Cmin. In addition, the effect of dose reductions on response was explored. PFS was used as measure of response.ResultsIn total, 59 patients were included:10% were classified as favourable, 61% as intermediate and 29% as poor IMDC risk group, respectively. Median number of prior treatment lines was 2 (0-5). Starting dose was 60 mg in 46%, 40 mg in 42% and 20 mg in 12% of patients. Dose reductions were needed in 58% of patients. Median Cmin was 572 ng/mL (IQR: 496-701). Only 17% of patients had an average Cmin ≥ 750 ng/mL. Median PFS was 52 weeks (95% CI: 40-64). No improved PFS was observed for patients with Cmin ≥ 750 ng/mL or ≥ 572 ng/ml. A longer PFS was observed for patients with a dose reduction vs. those without (65 vs. 31 weeks, p = .001). After incorporating known covariates (IMDC risk group and prior treatment lines (< 2 vs. ≥ 2)) in the multivariable analysis, the need for dose reduction remained significantly associated with improved PFS (HR 0.32, 95% CI:0.14-0.70, p = .004).ConclusionIn these explorative analyses, no clear relationship between increased cabozantinib exposure and improved PFS was observed. Average cabozantinib exposure was below the previously proposed target in 83% of patients. Future studies should focus on validating the cabozantinib exposure required for long term efficacy.

Dataset Information

Lagging exposure information in cumulative exposure-response analyses.

Publications

Lagging exposure information in cumulative exposure-response analyses.

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