Regression Models in Clinical Studies: Determining Relationships Between Predictors and Response

Fe, Harrell; Kl, Lee; Bg, Pollock

doi:10.1093/jnci/80.15.1198

Cited by 801 publications

(520 citation statements)

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“…In dose-response analysis, we used the method proposed by Greenland and Longnecker (Greenland et al, 1992) and Orsini (Orsini et al, 2006) to compute the trend from the correlated Log HR estimates across categories of sleep duration. We examined a potential nonlinear doseresponse relationship between sleep durations and cancer risk by modeling sleep durations using restricted cubic splines with 3 knots at percentiles 25%, 50%, and 75% of the distribution (Harrell et al, 1988). Heterogeneity of HRs across studies was tested by Q-statistic (P<0.05 was considered indicative of statistically significant heterogeneity) and quantified by the I 2 statistic (values of 25%, 50%, and 75% were considered to represent low, medium, and high heterogeneity, respectively) (Higgins et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

Sleep Duration and Cancer Risk: a Systematic Review and Meta-analysis of Prospective Studies

Zhao¹,

Yin²,

Yang³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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To assess the risk of cancers associated with sleep duration using meta-analysis of published cohort studies, we performed a comprehensive search using PubMed, Embase and Web of Science through October 2013. We combined hazard ratios (HRs) from individual studies using meta-analysis approaches. A random effect dose-response analysis was used to evaluate the relationship between sleep duration and cancer risk. Subgroup analyses and sensitivity analyses were also performed. Publication bias was evaluated using Funnel plots and Begg's test. A total of 13 cohorts from 12 studies were included in this meta-analysis, which included 723, 337 participants with 15, 156 reported cancer outcomes during a follow-up period ranging from 7.5 to 22 years. The pooled adjusted HRs were 1.06 (95% CI: 0.92, 1.23; P for heterogeneity =0.003) for short sleep duration, 0.91 (95% CI: 0.78, 1.07; P for heterogeneity <0.0001) for long sleep duration. In subgroup analyses stratified by cancer type, long duration of sleep showed an inverse relation with hormone-related cancer (HR=0.79; 95% CI: 0.65, 0.97; P for heterogeneity =0.009) and a greater risk of colorectal cancer (HR=1.29; 95% CI: 1.09, 1.52; P for heterogeneity =0.346). Further meta-analysis on dose-response relationships showed that the relative risks of cancer were 1.00 (95% CI: 0.99, 1.01; P for linear trend=0.9151) for one hour of sleep increment per day, and 1.00 (95% CI: 0.98, 1.01; P for linear trend=0.7749) for one hour of sleep increment per night. No significant dose-response relationship between sleep duration and cancer was found on non-linearity testing (P=0.5053). Our meta-analysis suggests a positive association between long sleep duration and colorectal cancer, and an inverse association with incidence of hormone related cancers like those in the breast. Studies with larger sample size, longer follow-up times, more cancer types and detailed measure of sleep duration are warranted to confirm these results.

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Section: Discussionmentioning

confidence: 99%

Sleep Duration and Cancer Risk: a Systematic Review and Meta-analysis of Prospective Studies

Zhao¹,

Yin²,

Yang³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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“…The second model assumed a linear effect of the exposure (i.e., as a continuous variable based on the estimated milligrams of prescribed glucocorticoids, with 'no prescriptions before the index date' as reference). The third model was fitted to test for a nonlinear effect by treating the exposure as a restricted cubic spline (Harre et al, 1988). Trend tests were used to evaluate the statistical significance of the effect of increasing amounts of prescribed oral glucocorticoids on the risk of these cancers (dose -response relationship).…”

Section: Discussionmentioning

confidence: 99%

Use of oral glucocorticoids and risk of skin cancer and non-Hodgkin's lymphoma: a population-based case–control study

et al. 2008

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In North Jutland County, Denmark, we investigated whether use of oral glucocorticoids was associated with an increased risk of developing basal cell carcinoma (BCC), squamous cell carcinoma (SCC), malignant melanoma (MM), and non-Hodgkin's lymphoma (NHL). From the Danish Cancer Registry we identified 5422 BCC, 935 SCC, 983 MM, and 481 NHL cases during 1989 -2003. Using risk-set sampling we selected four age-and gender-matched population controls for each case from the Civil Registration System. Prescriptions for oral glucocorticoids before diagnosis were obtained from the Prescription Database of North Jutland County on the basis of National Health Service data. We used conditional logistic regression to estimate incidence rate ratios (IRRs), adjusting for chronic medical diseases (information about these were obtained from the National Patient Registry) and use of other immunosuppressants. We found slightly elevated risk estimates for BCC (IRR, 1.15 (95% CI: 1.07 -1.25)), SCC (IRR, 1.14 (95% CI: 0.94 -1.39)), MM (IRR, 1.15 (95% CI: 0.94 -1.41), and NHL (IRR, 1.11 (95% CI: 0.85 -1.46)) among users of oral glucocorticoids. Our study supports an overall association between glucocorticoid use and risk of BCC that cannot be explained by the presence of chronic diseases or concomitant use of other immunosuppressants.

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“…A more informative analysis would be the Cox proportional hazards regression analysis (19), using the development of arthritis as a time-dependent covariate and the CD4 counts and stage of HIV infection included as baseline fixed covariates. Unfortunately, the size of the cohort and the limited number of uncensored observations (i.e., AIDS or death) make the application of this model problematic (20,21). Both of these statistical limitations will, we hope, be overcome with enlargement of the study cohort and extension of the observation period.…”

Section: Discussionmentioning

confidence: 99%

Rheumatic symptoms and human immunodeficiency virus infection. The influence of clinical and laboratory variables in a longitudinal cohort study

Calabrese

Kelley

Myers

et al. 1991

Arthritis & Rheumatism

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The present study was designed to prospectively evaluate the frequency of rheumatic symptoms in a cohort of patients infected with the human immunodeficiency virus (HIV), to examine the relationship between such findings and a variety of clinical and epidemiologic variables, and to evaluate the impact of rheumatic symptoms on the natural history of the HIV infection. One hundred seventeen patients were evaluated over a mean of 24.6 months (range 0.5–85 months). Cumulatively, 1.7% had Reiter's syndrome, 1.7% had psoriatic arthritis, and 11.1% had various forms of oligoarticular/monarticular or polyarticular arthritis. The majority of the rheumatic symptoms developed during the longitudinal evaluation and predominantly affected patients with clinically advanced HIV infection. Patients with articular disease tended to have more progressive HIV infection and were more likely to experience disease progression to clinical acquired immunodeficiency syndrome or death. Our data suggest that the occurrence of rheumatic symptoms in the presence of HIV infection is not uncommon and tends to develop over time, in the setting of clinically advanced retroviral infection. Furthermore, the presence of rheumatic symptoms may be a sign of a poor prognosis for patients with HIV infection.

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Regression Models in Clinical Studies: Determining Relationships Between Predictors and Response

Cited by 801 publications

References 0 publications

Sleep Duration and Cancer Risk: a Systematic Review and Meta-analysis of Prospective Studies

Sleep Duration and Cancer Risk: a Systematic Review and Meta-analysis of Prospective Studies

Use of oral glucocorticoids and risk of skin cancer and non-Hodgkin's lymphoma: a population-based case–control study

Rheumatic symptoms and human immunodeficiency virus infection. The influence of clinical and laboratory variables in a longitudinal cohort study

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