Post-operative volumes following endoscopic surgery for non-functioning pituitary macroadenomas are predictive of further intervention, but not endocrine outcomes.

BACKGROUND: Transsphenoidal surgery (TSS) remains the treatment of choice for non-functioning pituitary macroadenomas (NFPMA). The value of measuring tumour volumes before and after surgery, and its influence on endocrine outcomes and further treatment of the residual or recurrent tumour are unknown. METHODS: Data from patients who underwent endoscopic TSS for a NFPMA (2009-2018) in a UK tertiary centre were analysed for pre- and post-operative endocrine and surgical outcomes. RESULTS: Of 173 patients with NFPMA, 159 (61% male) were treatment naïve. At presentation, 76.2% (77/101) had ≥1 pituitary axis deficit. Older age (p = 0.002) was an independent predictor for multiple hormonal deficiencies. Preoperative tumour volume did not correlate with degree of hypopituitarism. Postoperative tumour volume and extent of tumour resection were not predictive of new onset hypopituitarism. Hormonal recovery was observed in 16 patients (20.8%) with impaired pituitary function, with the greatest recovery in the hypothalamic-pituitary-adrenal axis (21.2%, 7/33). A larger residual tumour volume was predictive of adjuvant radiotherapy (3.40 vs. 1.24 cm3, p = 0.005) and likelihood for repeat surgery (5.40 vs. 1.67cm3, p = 0.004). CONCLUSION: Pre- and post-operative NFPMA volumes fail to predict the number of pituitary hormone deficits, however, greater post-operative residual volumes increase the likelihood of further intervention to control tumour growth.

Causal models for estimating the effects of weight gain on mortality.

Suppose, in contrast to the fact, in 1950, we had put the cohort of 18-year-old non-smoking American men on a stringent mandatory diet that guaranteed that no one would ever weigh more than their baseline weight established at the age of 18 years. How would the counterfactual mortality of these 18 year olds have compared to their actual observed mortality through 2007? We describe in detail how this counterfactual contrast could be estimated from longitudinal epidemiologic data similar to that stored in the electronic medical records of a large health maintenance organization (HMO) by applying g-estimation to a novel of structural nested model (SNM). Our analytic approach differs from any alternative approach in that, in the absence of model misspecification, it can successfully adjust for (i) measured time-varying confounders such as exercise, hypertension and diabetes that are simultaneously intermediate variables on the causal pathway from weight gain to death and determinants of future weight gain, (ii) unmeasured confounding by undiagnosed preclinical disease (that is, reverse causation) that can cause both poor weight gain and premature mortality (provided an upper bound can be specified for the maximum length of time a subject may suffer from a subclinical illness severe enough to affect his weight without the illness becomes clinically manifest) and (iii) the presence of particular identifiable subgroups, such as those suffering from serious renal, liver, pulmonary and/or cardiac disease, in whom confounding by unmeasured prognostic factors is so severe as to render useless any attempt at direct analytic adjustment. However, (ii) and (iii) limit the ability to empirically test whether the SNM is misspecified. The other two g-methods--the parametric g-computation algorithm and inverse probability of treatment weighted estimation of marginal structural models--can adjust for potential bias due to (i) but not due to (ii) or (iii).

Instrumental variables as bias amplifiers with general outcome and confounding.

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators without adjusting for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder, and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill in this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions. We further show that when the treatment follows an additive or multiplicative model conditional on the instrumental variable and the confounder, these monotonicity assumptions can be interpreted as the signs of the arrows of the causal diagrams.

Multiple robustness in factorized likelihood models.

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.

Depressed thyroid indexes associated with occupational exposure to inorganic lead.

The finding of low values for serum thyroxine and estimated free thyroxine in seven of 12 workers referred because of elevated blood lead levels (greater than 40 mg/L) prompted further investigation. In a cross-sectional study of workers at a small foundry, both measurements were found to regress negatively with blood lead level. In 12 of 47 subjects, both indexes were in the hypothyroid range. Serum thyrotropin and triiodothyronine levels in patients and study subjects with low indexes were all normal. Physical examinations failed to demonstrate the classic features of hypothyroidism. These data are compatible with a central depression of the thyroid axis or an alteration in thyroxine metabolism or binding to proteins. Irrespective of mechanism, the association between low thyroid indexes and elevated lead levels merits attention because of the large number of workers exposed to lead and the similarities between the clinical features of adult lead poisoning and hypothyroidism.

A real-time audit of radiation therapy in a regional cancer center.

PURPOSE: To report the development, structure, and implementation of a real-time clinical radiotherapy audit of the practice of radiation oncology in a regional cancer center. METHODS AND MATERIALS: Radiotherapy treatment plans were audited by a real-time peer-review process over an 8-year period (1989-1996). The overall goal of the audit was to establish a process for quality assurance (QA) of radiotherapy planning and prescription for individual patients. A parallel process was developed to audit the implementation of intervention-specific radiotherapy treatment policies. RESULTS: A total of 3052 treatment plans were audited. Of these, 124 (4.1%) were not approved by the audit due to apparent errors in radiation planning. The majority of the nonapproved plans (79%) were modified prior to initiating treatment; the audit provided important clinical feedback about individual patient care in these instances. Most of the remaining nonapproved plans were deviations from normal practice due to patient-specific considerations. A further 110 (3.6% of all audited plans) were not approved by the audit due to deviations from radiotherapy treatment policy. A minority of these plans (22%) were modified prior to initiating treatment and the remainder provided important feedback for continuous quality improvement of treatment policies. CONCLUSION: A real-time audit of radiotherapy practice in a regional cancer center setting proved feasible and provided important direct and indirect patient benefits.

Estimation of ventilatory capacity in subjects with unacceptable lung function tests.

Based on pulmonary function data collected annually for six years on 540 Vermont granite workers, FEV1 in survey 1 was estimated by extrapolating back from subsequent measurements. The extrapolation method was found to fit the observed data of subjects with reproducible initial values very well (R2 = 0.87). Extrapolated FEV1s for workers unable to perform an adequate pulmonary function test according to the standards of the American Thoracic Society were compared to extrapolated values in the rest of the cohort. After adjusting for confounding, subjects with test failure in survey 1 had a lower extrapolated FEV1 than the rest of the cohort (p = 0.07). The mean extrapolated FEV1 of the 71 workers with an initial test failure was only 95% of a predicted value derived from the group with reproducible data, and the per cent predicted decreased from 98% to 71% as the number of test failures in the follow-up surveys increased (p = 0.0004). The American Thoracic Society and the Epidemiology Standardization Project currently recommend that test failures be excluded from the analysis of epidemiological data. Our findings suggest that alternative strategies for handling non-reproducible lung function may need to be explored in order to avoid selection bias.

Identifiability, exchangeability, and epidemiological confounding.

Non-identifiability of parameters is a well-recognized problem in classical statistics, and Bayesian statisticians have long recognized the importance of exchangeability assumptions in making statistical inferences. A seemingly unrelated problem in epidemiology is that of confounding: bias in estimation of the effects of an exposure on disease risk, due to inherent differences in risk between exposed and unexposed individuals. Using a simple deterministic model for exposure effects, a logical connection is drawn between the concepts of identifiability, exchangeability, and confounding. This connection allows one to view the problem of confounding as arising from problems of identifiability, and reveals the exchangeability assumptions that are implicit in confounder control methods. It also provides further justification for confounder definitions based on comparability of exposure groups, as opposed to collapsibility-based definitions.

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.

Soft tissue disorders in the upper limbs of female garment workers.

In this cross-sectional investigation of female garment workers the prevalence of soft tissue disorders of the hands and arms was studied. The findings were compared with the prevalence of disorders in a group of female hospital employees not required to use repetitive hand motion. One hundred and eighty-eight garment workers and 76 hospital employees were surveyed by questionnaire and physical examination. The prevalences of persistent shoulder, wrist, and hand pain were significantly greater among the garment workers (rate ratio 2, 4, and 3, respectively). In both groups about 60% of the persistent hand pain was consistent with carpal tunnel syndrome (rate ratio 3). These associations held when the comparisons were stratified by age and by length of employment. Workers whose native language was not English were significantly less likely to report symptoms (rate ratio 0.6). Workers in hand sewing and trimming suffered especially high prevalences of persistent pain in all upper limb sites. Stitchers had elevated rates of pain in the shoulders, wrists, and hands. Workers ironing by hand had a significant elevation in elbow pain rates. Garment assembly tasks appear to be associated with cumulative trauma of the hands and wrists; the biomechanical features of these jobs should be studied in greater detail.

Endocrine and reproductive dysfunction in men associated with occupational inorganic lead intoxication.

In an attempt to define a postulated effect of lead on male endocrine function, seven men with symptomatic occupational lead intoxication (maximum whole blood lead levels 66-139 micrograms/dl) underwent in-patient endocrine evaluation at the time of diagnosis. Defects in thyroid function, probably of central origin, were present in three patients. Six patients had subnormal glucocorticoid production measured by 24-hr urinary 17-hydroxycorticosteroids and plasma cortisol responses to vasopressin- and/or insulin-induced hypoglycemia. Although serum testosterone concentration was normal in six patients, five had defects in spermatogenesis, including two with oligospermia and two with azoospermia. Repeat examinations after chelation therapy showed only partial improvement. It is concluded that heavy occupational exposure to lead, sufficient to cause clinical poisoning, may be associated with diffuse disturbances of endocrine and reproductive functions in men which are not rapidly reversible with standard treatment. Since men without overt poisoning have not been studied, these results cannot yet be included as sequelae of low-dose exposures.

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.

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

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.

Data, design, and background knowledge in etiologic inference.

I use two examples to demonstrate that an appropriate etiologic analysis of an epidemiologic study depends as much on study design and background subject-matter knowledge as on the data. The demonstration is facilitated by the use of causal graphs.

Correction for non-compliance in equivalence trials.

In randomized trials comparing a new therapy to standard therapy, the sharp null hypothesis of equivalent therapeutic efficacy does not imply the intent-to-treat null hypothesis of equal outcome distributions in the two-treatment arm if non-compliance is present. As a consequence, the development of analytic methods that adjust for non-compliance is of particular importance in equivalence trials comparing a new therapy to standard therapy. This paper provides, in the context of equivalence trial, a unified overview of various analytic approaches to correct for non-compliance in randomized trials. The overview focuses on comparing and contrasting the plausibility, robustness, and strength of assumptions required by each method and their programming and computational burdens. In addition, several new structural (causal) models are introduced: the coarse structural nested models, the non-nested marginal structural models and the continuous-time structural nested models, and their properties are compared with those of previously proposed structural nested models. The fundamental assumption that allows us to correct for non-compliance is that the decision whether or not to continue to comply with assigned therapy at time t is random (that is, ignorable or explainable) conditional on the history up to t of measured pre- and time-dependent post-randomization prognostic factors. In the final sections of the paper, we consider how the consequences of violations of our assumption of conditionally ignorable non-compliance can be explored through a sensitivity analysis. Finally, the analytic methods described in this paper can also be used to estimate the causal effect of a time-varying treatment from observational data.

Non-response models for the analysis of non-monotone non-ignorable missing data.

I introduce a new class of non-ignorable non-monotone missing data models. These models are useful for investigating the sensitivity of one's estimates to untestable assumptions about the missing data process. I use the new models to analyse data from a case-control study of the effect of radiation on breast cancer.

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.'