Analysis of combined data from heterogeneous study designs: an applied example from the patient navigation research program.
ABSTRACT The Patient Navigation Research Program (PNRP) is a cooperative effort of nine research projects, with similar clinical criteria but with different study designs. To evaluate projects such as PNRP, it is desirable to perform a pooled analysis to increase power relative to the individual projects. There is no agreed-upon prospective methodology, however, for analyzing combined data arising from different study designs. Expert opinions were thus solicited from the members of the PNRP Design and Analysis Committee.
To review possible methodologies for analyzing combined data arising from heterogeneous study designs.
The Design and Analysis Committee critically reviewed the pros and cons of five potential methods for analyzing combined PNRP project data. The conclusions were based on simple consensus. The five approaches reviewed included the following: (1) analyzing and reporting each project separately, (2) combining data from all projects and performing an individual-level analysis, (3) pooling data from projects having similar study designs, (4) analyzing pooled data using a prospective meta-analytic technique, and (5) analyzing pooled data utilizing a novel simulated group-randomized design.
Methodologies varied in their ability to incorporate data from all PNRP projects, to appropriately account for differing study designs, and to accommodate differing project sample sizes.
The conclusions reached were based on expert opinion and not derived from actual analyses performed.
The ability to analyze pooled data arising from differing study designs may provide pertinent information to inform programmatic, budgetary, and policy perspectives. Multisite community-based research may not lend itself well to the more stringent explanatory and pragmatic standards of a randomized controlled trial design. Given our growing interest in community-based population research, the challenges inherent in the analysis of heterogeneous study design are likely to become more salient. Discussion of the analytic issues faced by the PNRP and the methodological approaches we considered may be of value to other prospective community-based research programs.
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ABSTRACT: Background Patient navigation is a promising intervention to address cancer disparities but requires a multisite controlled trial to assess its effectiveness. Methods The Patient Navigation Research Program compared patient navigation with usual care on time to diagnosis or treatment for participants with breast, cervical, colorectal, or prostate screening abnormalities and/or cancers between 2007 and 2010. Patient navigators developed individualized strategies to address barriers to care, with the focus on preventing delays in care. To assess timeliness of diagnostic resolution, we conducted a meta-analysis of center-and cancer-specific adjusted hazard ratios (aHRs) comparing patient navigation vs usual care. To assess initiation of cancer therapy, we calculated a single aHR, pooling data across all centers and cancer types. We conducted a metaregression to evaluate variability across centers. All statistical tests were two-sided. Results The 10 521 participants with abnormal screening tests and 2105 with a cancer or precancer diagnosis were predominantly from racial/ethnic minority groups (73%) and publically insured (40%) or uninsured (31%). There was no benefit during the first 90 days of care, but a benefit of navigation was seen from 91 to 365 days for both diagnostic resolution (aHR = 1.51; 95% confidence interval [CI] = 1.23 to 1.84; P < .001)) and treatment initiation (aHR = 1.43; 95% CI = 1.10 to 1.86; P < .007). Metaregression revealed that navigation had its greatest benefits within centers with the greatest delays in follow-up under usual care. Conclusions Patient navigation demonstrated a moderate benefit in improving timely cancer care. These results support adoption of patient navigation in settings that serve populations at risk of being lost to follow-up.JNCI Journal of the National Cancer Institute 06/2014; 106(6). DOI:10.1093/jnci/dju115 · 15.16 Impact Factor
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ABSTRACT: We evaluated the efficacy of a Chicago-based cancer patient navigation program developed to increase the proportion of patients reaching diagnostic resolution and reduce the time from abnormal screening test to definitive diagnostic resolution. Women with an abnormal breast (n = 352) or cervical (n = 545) cancer screening test were recruited for the quasi-experimental study. Navigation subjects originated from five federally qualified health center sites and one safety net hospital. Records-based concurrent control subjects were selected from 20 sites. Control sites had similar characteristics to the navigated sites in terms of patient volume, racial/ethnic composition, and payor mix. Mixed-effects logistic regression and Cox proportional hazard regression analyses were conducted to compare navigation and control patients reaching diagnostic resolution by 60 days and time to resolution, adjusting for demographic covariates and site. Compared with controls, the breast navigation group had shorter time to diagnostic resolution (aHR = 1.65, CI = 1.20-2.28) and the cervical navigation group had shorter time to diagnostic resolution for those who resolved after 30 days (aHR = 2.31, CI = 1.75-3.06), with no difference before 30 days (aHR = 1.42, CI = 0.83-2.43). Variables significantly associated with longer time to resolution for breast cancer screening abnormalities were being older, never partnered, abnormal mammogram and BI-RADS 3, and being younger and Black for cervical abnormalities. Patient navigation reduces time from abnormal cancer finding to definitive diagnosis in underserved women. Impact: Results support efforts to use patient navigation as a strategy to reduce cancer disparities among socioeconomically disadvantaged women. Cancer Epidemiol Biomarkers Prev; 21(10); 1691-700. ©2012 AACR.Cancer Epidemiology Biomarkers & Prevention 10/2012; 21(10):1691-700. DOI:10.1158/1055-9965.EPI-12-0535 · 4.32 Impact Factor
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ABSTRACT: Purpose Poor and underserved women face barriers in receiving timely and appropriate breast cancer care. Patient navigators help individuals overcome these barriers, but little is known about whether patient navigation improves quality of care. The purpose of this study is to examine whether navigated women with breast cancer are more likely to receive recommended standard breast cancer care. Patients and Methods Women with breast cancer who participated in the national Patient Navigation Research Program were examined to determine whether the care they received included the following: initiation of antiestrogen therapy in patients with hormone receptor-positive breast cancer; initiation of postlumpectomy radiation therapy; and initiation of chemotherapy in women younger than age 70 years with triple-negative tumors more than 1 cm. This is a secondary analysis of a multicenter quasi-experimental study funded by the National Cancer Institute to evaluate patient navigation. Multiple logistic regression was performed to compare differences in receipt of care between navigated and non-navigated participants. Results Among participants eligible for antiestrogen therapy, navigated participants (n = 380) had a statistically significant higher likelihood of receiving antiestrogen therapy compared with non-navigated controls (n = 381; odds ratio [OR], 1.73; P = .004) in a multivariable analysis. Among the participants eligible for radiation therapy after lumpectomy, navigated participants (n = 255) were no more likely to receive radiation (OR, 1.42; P = .22) than control participants (n = 297). Conclusion We demonstrate that navigated participants were more likely than non-navigated participants to receive antiestrogen therapy. Future studies are required to determine the full impact patient navigation may have on ensuring that vulnerable populations receive quality care. (C) 2014 by American Society of Clinical OncologyJournal of Clinical Oncology 07/2014; 32(25). DOI:10.1200/JCO.2013.53.6037 · 17.88 Impact Factor