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J M Robins

Publications and source records attributed to J M Robins.

At least 37 records · Page 2Linked to original sources
PubMed1995

An analytic method for randomized trials with informative censoring: Part 1.

Consider a randomized trial in which time to the occurrence of a particular disease, say pneumocystis 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 censoring time is observed for all study subjects, including failures. 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. The goals of this paper are to specify 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. In a companion paper (Robins, 1995), we provide consistent and reasonably efficient semiparametric estimators for the treatment effect under these assumptions. In this paper we largely restrict attention to testing. We propose tests that, 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 extend our methods to studies of the effect of a treatment on the evolution over time of the mean of a repeated measures outcome, such as CD-4 count.

Causality↗
PubMed1993

Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time model.

Estimating the causal effect of quitting smoking on time to death or first myocardial infarction requires that one control for the differences in risk factors between individuals who elect to quite at each time t versus those who elect to continue smoking at time t. In this paper we examine the limitations of standard time varying Cox proportional hazards models to yield tests and estimates of this effect. Implementing the method of G-estimation proposed by Robins, we perform an observational analysis of data from the Multiple Risk Factor Intervention Trial (MRFIT) and estimate the causal effect of cigarette cessation while controlling for such time varying confounders as angina. We reject the null hypothesis of no effect of quitting on time to failure, and estimate that by quitting smoking, an individual increases by 50 per cent his time to death or first myocardial infarction (MI).

Adult↗
PubMed1993

A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial.

The standard approach to analyzing randomized trials ignores information on postrandomization compliance. Application of these methods results in estimates that may lack the desired causal interpretation. We employ a new method of estimation and analyze data from the Multiple Risk Factor Intervention Trial (MRFIT) to estimate the causal effect of quitting cigarette smoking. Our procedure utilizes a method proposed by Robins and Tsiatis and allows us to take advantage of postrandomization smoking history without requiring untenable assumptions about the comparability of compliers and noncompliers. We contrast the performance of our method and the standard intent-to-treat analysis in the MRFIT data and in simulated data in which compliance rates are varied.

Adult↗
PubMed1992

Identifiability and exchangeability for direct and indirect effects.

We consider the problem of separating the direct effects of an exposure from effects relayed through an intermediate variable (indirect effects). We show that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased. We also show that even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions; in other words, direct and indirect effects are not separately identifiable when only exposure is randomized. If the exposure and intermediate never interact to cause disease and if intermediate effects can be controlled, that is, blocked by a suitable intervention, then a trial randomizing both exposure and the intervention can separate direct from indirect effects. Nonetheless, the estimation must be carried out using the G-computation algorithm. Conventional adjustment methods remain biased. When exposure and the intermediate interact to cause disease, direct and indirect effects will not be separable even in a trial in which both the exposure and the intervention blocking intermediate effects are randomly assigned. Nonetheless, in such a trial, one can still estimate the fraction of exposure-induced disease that could be prevented by control of the intermediate. Even in the absence of an intervention blocking the intermediate effect, the fraction of exposure-induced disease that could be prevented by control of the intermediate can be estimated with the G-computation algorithm if data are obtained on additional confounding variables.

Algorithms↗
PubMed1992

G-estimation of the effect of prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of AIDS patients.

AIDS Clinical Trial Group Randomized Trial 002 compared the effect of high-dose with low-dose 3-azido-3-deoxythymidine (AZT) on the survival of AIDS patients. Embedded within the trial was an essentially uncontrolled observational study of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on survival. In this paper, we estimate the causal effect of prophylaxis therapy on survival by using the method of G-estimation to estimate the parameters of a structural nested failure time model (SNFTM). Our SNFTM relates a subject's observed time of death and observed prophylaxis history to the time the subject would have died if, possibly contrary to fact, prophylaxis therapy had been withheld. We find that, under our assumptions, the data are consistent with prophylaxis therapy increasing survival by 16% or decreasing survival by 18% at the alpha = 0.05 level. The analytic approach proposed in this paper will be necessary to control bias in any epidemiologic study in which there exists a time-dependent risk factor for death, such as pneumocystis carinii pneumonia history, that (A1) influences subsequent exposure to the agent under study, for example, prophylaxis therapy, and (A2) is itself influenced by past exposure to the study agent. Conditions A1 and A2 will be true whenever there exists a time-dependent risk factor that is simultaneously a confounder and an intermediate variable.

Acquired Immunodeficiency Syndrome↗
PubMed1992

Estimating exposure effects by modelling the expectation of exposure conditional on confounders.

In order to estimate the causal effects of one or more exposures or treatments on an outcome of interest, one has to account for the effect of "confounding factors" which both covary with the exposures or treatments and are independent predictors of the outcome. In this paper we present regression methods which, in contrast to standard methods, adjust for the confounding effect of multiple continuous or discrete covariates by modelling the conditional expectation of the exposures or treatments given the confounders. In the special case of a univariate dichotomous exposure or treatment, this conditional expectation is identical to what Rosenbaum and Rubin have called the propensity score. They have also proposed methods to estimate causal effects by modelling the propensity score. Our methods generalize those of Rosenbaum and Rubin in several ways. First, our approach straightforwardly allows for multivariate exposures or treatments, each of which may be continuous, ordinal, or discrete. Second, even in the case of a single dichotomous exposure, our approach does not require subclassification or matching on the propensity score so that the potential for "residual confounding," i.e., bias, due to incomplete matching is avoided. Third, our approach allows a rather general formalization of the idea that it is better to use the "estimated propensity score" than the true propensity score even when the true score is known. The additional power of our approach derives from the fact that we assume the causal effects of the exposures or treatments can be described by the parametric component of a semiparametric regression model. To illustrate our methods, we reanalyze the effect of current cigarette smoking on the level of forced expiratory volume in one second in a cohort of 2,713 adult white males. We compare the results with those obtained using standard methods.

Analysis of Variance↗
PubMed1991

Empirical-Bayes adjustments for multiple comparisons are sometimes useful.

Rothman (Epidemiology 1990;1:43-46) recommends against adjustments for multiple comparisons. Implicit in his recommendation, however, is an assumption that the sole objective of the data analysis is to report and scientifically interpret the data. We concur with his recommendation when this assumption is correct and one is willing to abandon frequentist interpretations of the summary statistics. Nevertheless, there are situations in which an additional or even primary goal of analysis is to reach a set of decisions based on the data. In such situations, Bayes and empirical-Bayes adjustments can provide a better basis for the decisions than conventional procedures.

Bayes Theorem↗
PubMed1990

Adjusting for early treatment termination in comparative clinical trials.

In clinical trials of long-term therapies, patients often terminate their treatments earlier than planned. When analysing time-to-failure data, one approach to account for early treatment termination censors failure at the time of termination of therapy. In general, however, this does not produce valid inferences about the distribution of time to failure that would have occurred had treatment not been terminated. In contrast, intent-to-treat analyses, which are based on time to failure regardless of whether and when treatment is terminated, always produce valid inferences about the unconditional distribution of time to failure. Early treatment termination does not distort the size (type I error rate) of intent-to-treat tests but can cause a loss in power. Modifications to ordinary logrank tests can be used to recover some of the lost power without affecting test size, and can be most useful when the proportion of at-risk patients still taking their treatment changes substantially during periods when failures are observed. Extensions of the modified test to include strata are straightforward, although important design questions require further research.

Data Interpretation, Statistical↗
PubMed1990

The behaviour of common measures of association used to assess a vaccination programme under complex disease transmission patterns--a computer simulation study of malaria vaccines.

Case-control studies have been evoked as important alternatives to randomized clinical trials in the evaluation of infectious disease interventions. Using computer simulations, we compare the behaviour of common measures of association derived from case-control studies in the context of a malaria vaccine programme administered under complex transmission conditions. Several simplifying assumptions of previous workers have been relaxed and the simulated conditions are endemic rather than epidemic. The common estimators of association used in case-control studies remain unbiased only in limited circumstances. The term dependent happenings, first defined by Ross in 1916, is resurrected. Since the number of people becoming infected is dependent on the number of people already infected, control programmes in infectious diseases produce direct as well as indirect effects. Three different study designs with different pairs of comparison populations are defined. The choice of comparison population can be used to differentiate direct from indirect effects. In order to clarify the direct effects of a vaccination programme the comparison groups must be subjected to identical transmission intensities. In contrast, the referent group must remain unaffected by consequences of the intervention to determine indirect effects.

Brazil↗
PubMed1989

Estimability and estimation of excess and etiologic fractions.

This paper describes conditions under which epidemiologic data can provide estimates of the excess fraction (proportionate increase in caseload due to an exposure) and the etiologic fraction (fraction of cases caused by exposure). The excess fraction can be estimated under essentially the same conditions often cited for general study validity. In contrast, estimation of the etiologic fraction will usually require very specific non-identifiable assumptions about exposure action and interactions, although one can derive simple lower and upper bounds for the fraction from survival comparisons. Since the etiologic fraction is equivalent to the probability of causation, our results have implications for injury compensation in lawsuits involving the probability of causation.

Epidemiologic Methods↗
PubMed1989

Work related decrement in pulmonary function in silicon carbide production workers.

The relation between pulmonary function, cigarette smoking, and exposure to mixed respirable dust containing silicon carbide (SiC), hydrocarbons, and small quantities of quartz, cristobalite, and graphite was evaluated in 156SiC production workers using linear regression models on the difference between measured and predicted FEV1 and FVC. Workers had an average of 16 (range 2-41) years of employment and 9.5 (range 0.6-39.7) mg-year/m3 cumulative respirable dust exposure; average dust exposure while employed was 0.63 (range 0.18-1.42) mg/m3. Occasional, low level (less than or equal to 1.5 ppm) sulphur dioxide (SO2) exposure also occurred. Significant decrements in FEV1 (8.2 ml; p less than 0.03) and FVC (9.4 ml; p less than 0.01) were related to each year of employment for the entire group. Never smokers lost 17.8 ml (p less than 0.02) of FEV1 and 17.0 (p less than 0.05) of FVC a year, whereas corresponding decrements of 9.1 ml (p = 0.12) in FEV1 and 14.4 ml (p less than 0.02) in FVC were found in current smokers. Similar losses in FEV1 and FVC were related to each mg-year/m3 of cumulative dust exposure for 138 workers with complete exposure information; these findings, however, were generally not significant owing to the smaller cohort and greater variability in this exposure measure. Never smokers had large decrements in FEV1 (40.7 ml; p less than 0.02) and FVC (32.9 ml; p = 0.08) per mg-year/m3 of cumulative dust exposure and non-significant decrements were found in current smokers (FEV1: -7.1 ml; FVC: -11.7 ml). A non-significant decrement in lung function was also related to average dust exposure while employed. No changes were associated with SO(2) exposure or and SO(2) dust interaction. These findings suggest that employment in SiC production is associated with an excessive decrement in pulmonary function and that current permissible exposure limits for dusts occurring in this industry may not adequately protect workers from developing chronic pulmonary disease.

Adult↗
PubMed1989

Respiratory symptoms associated with low level sulphur dioxide exposure in silicon carbide production workers.

Relations between pulmonary symptoms and exposure to respirable dust and sulphur dioxide (SO2) were evaluated for 145 silicon carbide (SiC) production workers with an average of 13.9 (range 3-41) years of experience in this industry. Eight hour time weighted average exposures to SO2 were 1.5 ppm or less with momentary peaks up to 4 ppm. Cumulative SO2 exposure averaged 1.94 (range 0.02-19.5) ppm-years. Low level respirable dust exposures also occurred (0.63 +/- 0.26 mg/m3). After adjusting for age and current smoking status in multiple logistic regression models, highly significant, positive, dose dependent relations were found between cumulative and average exposure to SO2, and symptoms of usual and chronic phlegm, usual and chronic wheeze, and mild exertional dyspnoea. Mild and moderate dyspnoea were also associated with most recent exposure to SO2. Cough was not associated with SO2. No pulmonary symptoms were associated with exposure to respirable dust nor were any symptoms attributable to an interaction between dust and SO2. Cigarette smoking was strongly associated with cough, phlegm, and wheezing, but not dyspnoea. A greater than additive (synergistic) effect between smoking and exposure to SO2 was present for most symptoms. These findings suggest that long term, variable exposure to SO2 at 1.5 ppm or less was associated with significantly raised rates of phlegm, wheezing, and mild dyspnoea in SiC production workers, and that current threshold limits for SO2 may not adequately protect workers in this industry.

Adult↗
PubMed1989

Designs for synthetic case-control studies in open cohorts.

Several designs are proposed for case-control studies within cohorts when the cohort is open to late entry. These and previously proposed designs are examined with respect to consistency and efficiency of relative risk parameter estimation, and a small simulation study is reported. If study costs increase in proportion to the total number of "at-risk" controls, the most efficient design, Design C, is as follows. For a case failing at time t, controls are selected at random (and without regard to "at-risk" status) from among cohort members who are (i) known not to have failed prior to t and (ii) have not been previously selected as controls. At each t, control sampling proceeds until a prespecified number of controls who are "at risk" at t have been obtained. The efficiency advantage of Design C over that of the standard case-control design proposed by Thomas (in Appendix to Liddell, McDonald, and Thomas, 1977, Journal of the Royal Statistical Society, Series B 140, 469-490) will often be small. If, on the other hand, the costs increase in proportion to the number of distinct "at-risk" controls, Design C is no longer the most efficient design. In this case, several alternative designs are proposed.

Biometry↗
PubMed1988

Confidence intervals for causal parameters.

Consider an unbiased follow-up study designed to investigate the causal effect of a dichotomous exposure on a dichotomous disease outcome. Under a deterministic outcome model, a standard '95 per cent binomial confidence interval' may fail to cover the causal parameter of interest at the nominal rate when we take the causal parameter to be a parameter associated with the observed study population (regardless of whether the observed study population was sampled from a larger superpopulation). I propose new interval estimators that, in this setting, improve upon the performance of the standard 'binomial confidence interval.'

Biometry↗
PubMed1988

Substitution of magnetic resonance imaging for computed tomography. An exploratory study.

Despite the importance of understanding factors related to physician adoption and use of diagnostic technologies, relatively few studies have been published. Results of a two-year study of the adoption of magnetic resonance imaging (MRI) and its substitution for computed tomography scanning (CT) are presented. The literature on physician adoption and use of technology is used to provide a framework for this study. Differences in adoption and substitution among medical specialties, early versus late adopters, and high versus low users of MRI are examined. Results show that neurologists and internists more rapidly adopt MRI and substitute it for CT than do orthopedists and other surgical specialists. Referral of higher numbers of patients is the best predictor of more rapid substitution. Physicians who were late adopters more quickly substituted MRI for CT. The cost and social implications of empirical versus "ideal" substitution rates are discussed along with how various regulatory, technology assessment, and financial strategies influence substitution. The role of individual physicians, radiologists, and specialty societies in determining substitution rates is also discussed.

California↗