PubMed Health⌕ Search

PubMed · 9385113

An analytic method for randomized trials with informative censoring: Part II.

Abstract

Consider a randomized trial in which time to the occurrence of a particular disease, say pneumocystic pneumonia in an AIDS trial or breast cancer in a mammographic screening trial, is the failure time of primary interest. Suppose that time to disease is subject to informative censoring by the minimum of time to death, loss to and end of follow-up. In such a trial, the potential censoring time is observed for all study subjects, including failure. In the presence of informative censoring, it is not possible to consistently estimate the effect of treatment on time to disease without imposing additional non-identifiable assumptions. Robins (1995) specified two non-identifiable assumptions that allow one to test for and estimate an effect of treatment on time to disease in the presence of informative censoring. The goal of this paper is to provide a class of consistent and reasonably efficient semiparametric tests and estimators for the treatment effect under these assumptions. The tests in our class, like standard weighted-log-rank tests, are asymptotically distribution-free alpha-level tests under the null hypothesis of no causal effect of treatment on time to disease whenever the censoring and failure distributions are conditionally independent given treatment arm. However, our tests remain asymptotically distribution-free alpha-level tests in the presence of informative censoring provided either of our assumptions are true. In contrast, a weighted log-rank test will be an alpha-level test in the presence of informative censoring only if (1) one of our two non-identifiable assumptions hold, and (2) the distribution of time to censoring is the same in the two treatment arms. We also study the estimation, in the presence of informative censoring, of the effect of treatment on the evolution over time of the mean of repeated measures outcome such as CD4 count.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J M Robins. 1995. An analytic method for randomized trials with informative censoring: Part II.. https://doi.org/10.1007/bf00985453

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P&#x2005;<&#x2005;.05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio&#x2005;<&#x2005;1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P&#x2005;<&#x2005;5&#x2005;&#xd7;&#x2005;10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

Causality↗

Analysing and interpreting competing risk data.

When competing risks are present, two types of analysis can be performed: modelling the cause specific hazard and modelling the hazard of the subdistribution. This paper contrasts these two methods and presents the benefits of each. The interpretation is specific to the analysis performed. When modelling the cause specific hazard, one performs the analysis under the assumption that the competing risks do not exist. This could be beneficial when, for example, the main interest is whether the treatment works in general. In modelling the hazard of the subdistribution, one incorporates the competing risks in the analysis. This analysis compares the observed incidence of the event of interest between groups. The latter analysis is specific to the structure of the observed data and it can be generalized only to another population with similar competing risks.

Causality↗

Evaluating candidate agents of selective pressure for cystic fibrosis.

Cystic fibrosis is the most common lethal single-gene mutation in people of European descent, with a carrier frequency upwards of 2%. Based upon molecular research, resistances in the heterozygote to cholera and typhoid fever have been proposed to explain the persistence of the mutation. Using a population genetic model parameterized with historical demographic and epidemiological data, we show that neither cholera nor typhoid fever provided enough historical selective pressure to produce the modern incidence of cystic fibrosis. However, we demonstrate that the European tuberculosis pandemic beginning in the seventeenth century would have provided sufficient historical, geographically appropriate selective pressure under conservative assumptions. Tuberculosis has been underappreciated as a possible selective agent in producing cystic fibrosis but has clinical, molecular and now historical, geographical and epidemiological support. Implications for the future trajectory of cystic fibrosis are discussed. Our result supports the importance of novel investigations into the role of arylsulphatase B deficiency in cystic fibrosis and tuberculosis.

Causality↗