Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Prognostic utility of the breast cancer index and comparison to Adjuvant! Online in a clinical case series of early breast cancer

Introduction\ud Breast Cancer Index (BCI) combines two independent biomarkers, HOXB13:IL17BR (H:I) and the 5-gene molecular grade index (MGI), that assess estrogen-mediated signalling and tumor grade, respectively. BCI stratifies early-stage estrogen-receptor positive (ER+), lymph-node negative (LN-) breast cancer patients into three risk groups and provides a continuous assessment of individual risk of distant recurrence. Objectives of the current study were to validate BCI in a clinical case series and to compare the prognostic utility of BCI and Adjuvant!Online (AO).\ud \ud Methods\ud Tumor samples from 265 ER+LN- tamoxifen-treated patients were identified from a single academic institution's cancer research registry. The BCI assay was performed and scores were assigned based on a pre-determined risk model. Risk was assessed by BCI and AO and correlated to clinical outcomes in the patient cohort.\ud \ud Results\ud BCI was a significant predictor of outcome in a cohort of 265 ER+LN- patients (median age: 56-y; median follow-up: 10.3-y), treated with adjuvant tamoxifen alone or tamoxifen with chemotherapy (32%). BCI categorized 55%, 21%, and 24% of patients as low, intermediate and high-risk, respectively. The 10-year rates of distant recurrence were 6.6%, 12.1% and 31.9% and of breast cancer-specific mortality were 3.8%, 3.6% and 22.1% in low, intermediate, and high-risk groups, respectively. In a multivariate analysis including clinicopathological factors, BCI was a significant predictor of distant recurrence (HR for 5-unit increase = 5.32 [CI 2.18-13.01; P = 0.0002]) and breast cancer-specific mortality (HR for a 5-unit increase = 9.60 [CI 3.20-28.80; P < 0.0001]). AO was significantly associated with risk of recurrence. In a separate multivariate analysis, both BCI and AO were significantly predictive of outcome. In a time-dependent (10-y) ROC curve accuracy analysis of recurrence risk, the addition of BCI+AO increased predictive accuracy in all patients from 66% (AO only) to 76% (AO+BCI) and in tamoxifen-only treated patients from 65% to 81%.\ud \ud Conclusions\ud This study validates the prognostic performance of BCI in ER+LN- patients. In this characteristically low-risk cohort, BCI classified high versus low-risk groups with ~5-fold difference in 10-year risk of distant recurrence and breast cancer-specific death. BCI and AO are independent predictors with BCI having additive utility beyond standard of care parameters that are encompassed in AO

Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Introduction: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. Methods: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curves and compared it to nested Cox models obtained by robust backward selection procedures. Results: A prognostic index derived from of a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), the most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. Conclusions: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.This work was supported by a grant from the Susan G Komen Foundation (to YK)

INvestigational Vertebroplasty Efficacy and Safety Trial (INVEST): a randomized controlled trial of percutaneous vertebroplasty

Background: The treatment of painful osteoporotic vertebral compression fractures has historically been limited to several weeks of bed rest, anti-inflammatory and analgesic medications, calcitonin injections, or external bracing. Percutaneous vertebroplasty (the injection of bone cement into the fractured vertebral body) is a relatively new procedure used to treat these fractures. There is increasing interest to examine the efficacy and safety of percutaneous vertebroplasty and to study the possibility of a placebo effect or whether the pain relief is from local anesthetics placed directly on the bone during the vertebroplasty procedure. Methods/Designs: Our goal is to test the hypothesis that patients with painful osteoporotic vertebral compression fractures who undergo vertebroplasty have less disability and pain at 1 month than patients who undergo a control intervention. The control intervention is placement of local anesthesia near the fracture, without placement of cement. One hundred sixty-six patients with painful osteoporotic vertebral compression fractures will be recruited over 5 years from US and foreign sites performing the vertebroplasty procedure. We will exclude patients with malignant tumor deposit (multiple myeloma), tumor mass or tumor extension into the epidural space at the level of the fracture. We will randomly assign participants to receive either vertebroplasty or the control intervention. Subjects will complete a battery of validated, standardized measures of pain, functional disability, and health related quality of life at baseline and at post-randomization time points (days 1, 2, 3, and 14, and months 1, 3, 6, and 12). Both subjects and research interviewers performing the follow-up assessments will be blinded to the randomization assignment. Subjects will have a clinic visit at months 1 and 12. Spine X-rays will be obtained at the end of the study (month 12) to determine subsequent fracture rates. Our co-primary outcomes are the modified Roland score and pain numerical rating scale at 1 month. Discussion: Although extensively utilized throughout North America for palliation of pain, vertebroplasty still has not undergone rigorous study. The study outlined above represents the first randomized, controlled study that can account for a placebo effect in the setting of vertebroplasty. Trial Registration: Current Controlled Trials ISRCTN81871888.The source of funding for the study and all authors for this publication was National Institutes of Health (NIH)/National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)

Average Household Exposure to Newspaper Coverage about the Harmful Effects of Hormone Therapy and Population-Based Declines in Hormone Therapy Use

BACKGROUND: The news media facilitated the rapid dissemination of the findings from the estrogen plus progestin therapy arm of the Women’s Health Initiative (EPT-WHI). OBJECTIVE: To examine the relationship between the potential exposure to newspaper coverage and subsequent hormone therapy (HT) use. DESIGN/POPULATION: Population-based cohort of women receiving mammography at 7 sites (327,144 postmenopausal women). MEASUREMENTS: The outcome was the monthly prevalence of self-reported HT use. Circulation data for local, regional, and national newspapers was used to create zip-code level measures of the estimated average household exposure to newspaper coverage that reported the harmful effects of HT in July 2002. RESULTS: Women had an average potential household exposure of 1.4 articles. There was substantial variation in the level of average household exposure to newspaper coverage; women from rural sites received less than women from urban sites. Use of HT declined for all average potential exposure groups after the publication of the EPT-WHI. HT prevalence among women who lived in areas where there was an average household exposure of at least 3 articles declined significantly more (45 to 27%) compared to women who lived in areas with <1 article (43 to 31%) during each of the subsequent 5 months (relative risks 0.86–0.92; p < .006 for all). CONCLUSIONS: Greater average household exposure to newspaper coverage about the harms associated with HT was associated with a large population-based decline in HT use. Further studies should examine whether media coverage directly influences the health behavior of individual women

Two-marker protein profile predicts poor prognosis in patients with early rectal cancer

The aim of this study was to establish an immunohistochemical protein profile to complement preoperative staging and identify rectal cancer patients at high-risk of adverse outcome. Immunohistochemistry was performed on a tissue microarray including 482 rectal cancers for APAF-1, EphB2, MST1, Ki67, p53, RHAMM, RKIP and CD8+ tumour infiltrating lymphocytes (TILs). After resampling of the data and multivariable analysis, the most reproducible markers were combined and prognosis evaluated as stratified by pT and pN status. In multivariable analysis, only positive RHAMM (P<0.001; HR=1.94 (1.44–2.61)) and loss of CD8+ TILs (P=0.006; HR=0.63 (0.45–0.88)) were independent prognostic factors. The 5-year cancer-specific survival rate for RHAMM+/TIL− patients was 30% (95% CI 21–40%) compared to 76% (95% CI: 66–84%) for RHAMM−/TIL+ patients (P<0.001). The 5-year cancer-specific survival of T1/T2/RHAMM+/TIL− patients was 48% (20–72%) and significantly worse compared to T3/T4/RHAMM−/TIL+ patients (71% 95% CI 56–82%); P=0.039). Stratifying by nodal status, only N+/RHAMM+/TIL− patients demonstrated a significantly worse prognosis than N0/RHAMM+/TIL− patients (P=0.005). Loss of CD8+ TILs was predictive of local recurrence in RHAMM+ tumours (P=0.009) only. RHAMM and CD8+ TILs may assist in identifying early stage rectal cancer patients facing a particularly poor prognosis and who may derive a benefit from preoperative therapy

Imputation strategies for missing binary outcomes in cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study.</p> <p>Method</p> <p>We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC) method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE) logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT) which has complete data, we designed a simulation study to investigate the performance of above MI strategies.</p> <p>Results</p> <p>The estimated treatment effect and its 95% confidence interval (CI) from generalized estimating equations (GEE) model based on the CHAT complete dataset are 1.14 (0.76 1.70). When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75) if complete case analysis is used, 1.12 (0.72 1.73) if within-cluster MCMC method is used, 1.21 (0.80 1.81) if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64) if standard logistic regression which does not account for clustering is used.</p> <p>Conclusion</p> <p>When the percentage of missing data is low or intra-cluster correlation coefficient is small, different approaches for handling missing binary outcome data generate quite similar results. When the percentage of missing data is large, standard MI strategies, which do not take into account the intra-cluster correlation, underestimate the variance of the treatment effect. Within-cluster and across-cluster MI strategies (except for random-effects logistic regression MI strategy), which take the intra-cluster correlation into account, seem to be more appropriate to handle the missing outcome from CRTs. Under the same imputation strategy and percentage of missingness, the estimates of the treatment effect from GEE and RE logistic regression models are similar.</p