Xiaomei Liao

Physical activity and risk of inflammatory bowel disease: prospective study from the Nurses’ Health Study cohorts

Objective To examine the association between physical activity and risk of ulcerative colitis and Crohn’s disease. Design Prospective cohort study. Setting Nurses’ Health Study and Nurses’ Health Study II. Participants 194 711 women enrolled in the Nurses’ Health Study and Nurses’ Health Study II who provided data on physical activity and other risk factors every two to four years since 1984 in the Nurses’ Health Study and 1989 in the Nurses’ Health Study II and followed up through 2010. Main outcome measure Incident ulcerative colitis and Crohn’s disease. Results During 3 421 972 person years of follow-up, we documented 284 cases of Crohn’s disease and 363 cases of ulcerative colitis. The risk of Crohn’s disease was inversely associated with physical activity (P for trend 0.02). Compared with women in the lowest fifth of physical activity, the multivariate adjusted hazard ratio of Crohn’s disease among women in the highest fifth of physical activity was 0.64 (95% confidence interval 0.44 to 0.94). Active women with at least 27 metabolic equivalent task (MET) hours per week of physical activity had a 44% reduction (hazard ratio 0.56, 95% confidence interval 0.37 to 0.84) in risk of developing Crohn’s disease compared with sedentary women with <3 MET h/wk. Physical activity was not associated with risk of ulcerative colitis (P for trend 0.46). The absolute risk of ulcerative colitis and Crohn’s disease among women in the highest fifth of physical activity was 8 and 6 events per 100 000 person years compared with 11 and 16 events per 100 000 person years among women in the lowest fifth of physical activity, respectively. Age, smoking, body mass index, and cohort did not significantly modify the association between physical activity and risk of ulcerative colitis or Crohn’s disease (all P for interaction >0.35). Conclusion In two large prospective cohorts of US women, physical activity was inversely associated with risk of Crohn’s disease but not of ulcerative colitis.

Low-carbohydrate diet scores and risk of type 2 diabetes in men.

BACKGROUND Fat and protein sources may influence whether low-carbohydrate diets are associated with type 2 diabetes (T2D). OBJECTIVE The objective was to compare the associations of 3 low-carbohydrate diet scores with incident T2D. DESIGN A prospective cohort study was conducted in participants from the Health Professionals Follow-Up Study who were free of T2D, cardiovascular disease, or cancer at baseline (n = 40,475) for up to 20 y. Cumulative averages of 3 low-carbohydrate diet scores (high total protein and fat, high animal protein and fat, and high vegetable protein and fat) were calculated every 4 y from food-frequency questionnaires and were associated with incident T2D by using Cox models. RESULTS We documented 2689 cases of T2D during follow-up. After adjustments for age, smoking, physical activity, coffee intake, alcohol intake, family history of T2D, total energy intake, and body mass index, the score for high animal protein and fat was associated with an increased risk of T2D [top compared with bottom quintile; hazard ratio (HR): 1.37; 95% CI: 1.20, 1.58; P for trend < 0.01]. Adjustment for red and processed meat attenuated this association (HR: 1.11; 95% CI: 0.95, 1.30; P for trend = 0.20). A high score for vegetable protein and fat was not significantly associated with the risk of T2D overall but was inversely associated with T2D in men aged <65 y (HR: 0.78; 95% CI: 0.66, 0.92; P for trend = 0.01, P for interaction = 0.01). CONCLUSIONS A score representing a low-carbohydrate diet high in animal protein and fat was positively associated with the risk of T2D in men. Low-carbohydrate diets should obtain protein and fat from foods other than red and processed meat.

Survival analysis with error-prone time-varying covariates: a risk set calibration approach

Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration (ORC) approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By recalibrating the measurement error model within each risk set, a risk set regression calibration (RRC) method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvards Health Professionals Follow-up Study (HPFS).

Power and sample size calculations for longitudinal studies estimating a main effect of a time-varying exposure:

Existing study design formulas for longitudinal studies assume that the exposure is time invariant or that it varies in a manner that is controlled by design. However, in observational studies, the investigator does not control how exposure varies within subjects over time. Typically, a large number of exposure patterns are observed, with differences in the number of exposed periods per participant and with changes in the cross-sectional mean of exposure over time. This article provides formulas for study design calculations that incorporate these features for studies with a continuous outcome and a time-varying exposure, for cases where the effect of exposure on the response is assumed to be constant over time. We show that incorrectly using the formulas for time-invariant exposure can produce substantial overestimation of the required sample size. It is shown that the exposure mean, variance and intraclass correlation are the only additional parameters needed for exact solutions for the required sample size, if compound symmetry of residuals can be assumed, or to a good approximation if residuals follow a damped exponential correlation structure. The methods are applied to several examples. A publicly available programme to perform the calculations is provided.

A maximum likelihood approach to power calculations for stepped wedge designs of binary outcomes

In stepped wedge designs (SWD), clusters are randomized to the time period during which new patients will receive the intervention under study in a sequential rollout over time. By the studys end, patients at all clusters receive the intervention, eliminating ethical concerns related to withholding potentially efficacious treatments. This is a practical option in many large-scale public health implementation settings. Little statistical theory for these designs exists for binary outcomes. To address this, we utilized a maximum likelihood approach and developed numerical methods to determine the asymptotic power of the SWD for binary outcomes. We studied how the power of a SWD for detecting risk differences varies as a function of the number of clusters, cluster size, the baseline risk, the intervention effect, the intra-cluster correlation coefficient, and the time effect. We studied the robustness of power to the assumed form of the distribution of the cluster random effects, as well as how power is affected by variable cluster size. % SWD power is sensitive to neither, in contrast to the parallel cluster randomized design which is highly sensitive to variable cluster size. We also found that the approximate weighted least square approach of Hussey and Hughes (2007, Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials 28, 182-191) for binary outcomes under-estimates the power in some regions of the parameter spaces, and over-estimates it in others. The new method was applied to the design of a large-scale intervention program on post-partum intra-uterine device insertion services for preventing unintended pregnancy in the first 1.5 years following childbirth in Tanzania, where it was found that the previously available method under-estimated the power.

Can efficiency be gained by correcting for misclassification

This paper considers 2×2 tables arising from case-control studies in which the binary exposure may be misclassified. We found circumstances under which the inverse matrix method provides a more efficient odds ratio estimator than the naive estimator. We provide some intuition for the findings, and also provide a formula for obtaining the minimum size of a validation study such that the variance of the odds ratio estimator from the inverse matrix method is smaller than that of the naive estimator, thereby ensuring an advantage for the misclassification corrected result. As a corollary of this result, we show that correcting for misclassification does not necessarily lead to a widening of the confidence intervals, but, rather, in addition to producing a consistent estimate, can also produce one that is more efficient.