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Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials.

ABSTRACT: When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess surrogacy. We apply the method to data from a large collection of colon cancer trials in which S and T are binary. We obtain the trial-specific causal measures among S, T, and Z, as well as their overall population-level counterparts that are invariant across trials. The method allows for information sharing across trials and reduces the nonidentifiability problem. We examine the frequentist properties of our model estimates and the impact of the monotonicity assumption using simulations. We also illustrate the challenges in evaluating surrogacy in the counterfactual framework that result from nonidentifiability.

SUBMITTER: Li Y

PROVIDER: S-EPMC3114655 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials.

Li Yun Y Taylor Jeremy M G JM Elliott Michael R MR Sargent Daniel J DJ

Biostatistics (Oxford, England) 20110120 3

When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess ...[more]

PMID: 21252079

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Project description:Background Chinese herbal medicines reportedly increase efficacy and minimize toxicity of chemotherapy; however, little attention has been paid to how poor study quality can bias outcomes. Methods We systematically searched MEDLINE, TCMLARS, EMBASE, and Cochrane Library for randomized controlled trials of Chinese herbal medicines combined with fluorouracil-based chemotherapy compared with the same chemotherapy alone. We screened for eligibility, extracted data, and pooled data with random-effects meta-analysis. Outcome measures were survival, toxicity, tumor response, performance status, quality of life, and Cochrane Risk of Bias (ROB) criteria to critically evaluate the quality of reporting in the randomized trials included in the meta-analysis. Results We found 36 potentially eligible studies, with only 3 (those with low ROB) qualifying for meta-analysis. Two reported chemotherapy-related diarrhea reduced by 57% (relative risk [RR] = 0.43; 95% CI = 0.19-1.01; I(2) test for variation in RR due to heterogeneity = 0.0%), with nonsignificant results. Two reported white blood cell toxicity reduced by 66% (RR = 0.34; 95% CI = 0.16-0.72; I(2) test for variation in RR due to heterogeneity = 0.0%), with statistically significant results. Stratifying analysis by studies with high versus low ROB, we found substantial overestimation of benefit: Studies with high ROB overestimated by nearly 2-fold reduction of platelet toxicity by Chinese herbal medicines (RR = 0.35, 95% CI = 0.15-0.84 vs RR = 0.65, 95% CI = 0.11-3.92). Studies with high ROB overestimated by nearly 2-fold reduction of vomiting toxicity (RR = 0.45, 95% CI = 0.33-0.61 vs RR = 0.87, 95% CI = 0.48-1.58). And, studies with high ROB overestimated by 21% the reduction in diarrhea toxicity (RR = 0.34, 95% CI = 0.20-0.58 vs RR = 0.43, 95% CI = 0.19-1.01). Studies with high ROB also overestimated by 16% improvement in tumor response (RR = 1.39, 95% CI = 1.18-1.63 vs RR = 1.20; 95% CI = 0.81-1.79). Not accounting for ROB would have exaggerated evidence of benefit and failed to detect nonsignificance of results. Conclusions In the present analysis, involving 36 studies, 2593 patients, 20 outcomes, 36 medical institutions, and 271 named research authors, 92% of the data points were from studies at high ROB. Given the poor quality of the data in studies identified, it cannot be concluded whether combining Chinese herbs with chemotherapy reduces toxicity of chemotherapy.

Project description:Because of the time and expense required to obtain clinical outcomes of interest, such as functional limitations or death, clinical trials often focus the effects of treatment on earlier and more easily obtained surrogate markers. Preliminary work to define surrogates focused on the fraction of a treatment effect "explained" by a marker in a regression model, but as notions of causality have been formalized in the statistical setting, formal definitions of high-quality surrogate markers have been developed in the causal inference framework, using either the "causal effect" or "causal association" settings. In the causal effect setting, high-quality surrogate markers have a large fraction of the total treatment effect explained by the effect of the treatment on the marker net of the treatment on the outcome. In the causal association setting, high-quality surrogate markers have large treatment effects on the outcome when there are large treatment effects on the marker, and small effects on the outcome when there are small effects on the marker. A particularly important feature of a surrogate marker is that the direction of a treatment effect be the same for both the marker and the outcome. Settings in which the marker and outcome are positively associated but the marker and outcome have beneficial and harmful or harmful and beneficial treatment effects, respectively, have been referred to as "surrogate paradoxes". If this outcome always occurs, it is not problematic; however, as correlations among the outcome, marker, and their treatment effects weaken, it may occur for some trials and not for others, leading to potentially incorrect conclusions, and real-life examples that shortened thousands of lives are unfortunately available. We propose measures for assessing the risk of the surrogate paradox using the meta-analytic causal association framework, which allows us to focus on the probability that a given treatment will yield treatment effect in different directions between the marker and the outcome, and to determine the size of a beneficial effect of the treatment on the marker required to minimize the risk of a harmful effect of the treatment on the outcome. We provide simulations and consider two applications.

Project description:Physicians and investigators have sought to determine the relationship between body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) and colorectal cancer (CRC) outcomes, but methodologic limitations including sampling selection bias, reverse causality, and collider bias have prevented the ability to draw definitive conclusions.To evaluate the association of BMI at the time of, and following, colorectal cancer (CRC) diagnosis with mortality in a complete population using causal diagrams.This retrospective observational study with prospectively collected data included a cohort of 3408 men and women, ages 18 to 80 years, from the Kaiser Permanente Northern California population, who were diagnosed with stage I to III CRC between 2006 and 2011 and who also had surgery.Body mass index at diagnosis and 15 months following diagnosis.Hazard ratios (HRs) for all-cause mortality and CRC-specific mortality compared with normal-weight patients, adjusted for sociodemographics, disease severity, treatment, and prediagnosis BMI.This study investigated a cohort of 3408 men and women ages 18 to 80 years diagnosed with stage I to III CRC between 2006 and 2011 who also had surgery. At-diagnosis BMI was associated with all-cause mortality in a nonlinear fashion, with patients who were underweight (BMI <18.5; HR, 2.65; 95% CI, 1.63-4.31) and patients who were class II or III obese (BMI ?35; HR, 1.33; 95% CI, 0.89-1.98) exhibiting elevated mortality risks, compared with patients who were low-normal weight (BMI 18.5 to <23). In contrast, patients who were high-normal weight (BMI 23 to <25; HR, 0.77; 95% CI, 0.56-1.06), low-overweight (BMI 25 to <28; HR, 0.75; 95% CI, 0.55-1.04), and high-overweight (BMI 28 to <30; HR, 0.52; 95% CI, 0.35-0.77) had lower mortality risks, and patients who were class I obese (BMI 30 to <35) showed no difference in risk. Spline analysis confirmed a U-shaped relationship in participants with lowest mortality at a BMI of 28. Associations with CRC-specific mortality were similar. Associations of postdiagnosis BMI and mortality were also similar, but patients who were class I obese had significantly lower all-cause and cancer-specific mortality risks.In this study, body mass index at the time of diagnosis and following diagnosis of CRC was associated with mortality risk. Though evidence shows that exercise in patients with cancer should be encouraged, findings suggest that recommendations for weight loss in the immediate postdiagnosis period among patients with CRC who are overweight may be unwarranted.

Dataset Information

Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials.

Publications

Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials.

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