Browsing by Author "Li, Kan"
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Item Open Access A Bayesian approach for individual-level drug benefit-risk assessment.(Statistics in medicine, 2019-07) Li, Kan; Luo, Sheng; Yuan, Sammy; Mt-Isa, ShahrulIn existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually identified and defined separately based on aggregated clinical data and therefore ignore the individual-level differences as well as the association among the criteria. We proposed a Bayesian multicriteria decision-making method for BRA of drugs using individual-level data. We used a multidimensional latent trait model to account for the heterogeneity of treatment effects with latent variables introducing the dependencies among outcomes. We then applied the stochastic multicriteria acceptability analysis approach for BRA incorporating imprecise and heterogeneous patient preference information. We adopted an efficient Markov chain Monte Carlo algorithm when implementing the proposed method. We applied our method to a case study to illustrate how individual-level benefit-risk profiles could inform decision-making.Item Open Access A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data.(Alzheimer's & dementia : the journal of the Alzheimer's Association, 2018-05) Li, Kan; O'Brien, Richard; Lutz, Michael; Luo, Sheng; Alzheimer's Disease Neuroimaging InitiativeINTRODUCTION:Characterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression-free survival in patients with mild cognitive impairment. METHODS:The information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross-validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative-1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative-2. RESULTS:Model comparison yielded a prognostic index computed as the weighted combination of historical information of five neurocognitive longitudinal markers that are routinely collected in observational studies. The comprehensive validity analysis provided solid evidence of the usefulness of the model for predicting Alzheimer's disease progression. DISCUSSION:The prognostic model was improved by incorporating multiple longitudinal markers. It is useful for monitoring disease and identifying patients for clinical trial recruitment.Item Open Access Adjuvant Chemotherapy Versus Observation Following Resection for Patients With Nonmetastatic Poorly Differentiated Colorectal Neuroendocrine Carcinomas.(Annals of surgery, 2019-08-30) Mao, Rui; Li, Kan; Cai, Jian-Qiang; Luo, Sheng; Turner, Megan; Blazer, Dan; Zhao, HongOBJECTIVE:The aim of this study was to determine whether adjuvant chemotherapy (AC) provides a survival benefit in patients with nonmetastatic poorly differentiated colorectal neuroendocrine carcinomas (CRNECs) following resection. BACKGROUND:There is little evidence to support the association between use of AC and improved overall survival (OS) in patients with CRNECs. METHODS:Patients with resected non-metastatic CRNECs were identified in the National Cancer Database (2004-2014). Inverse probability of treatment weighting (IPTW) method was used to reduce the selection bias. IPTW-adjusted Kaplan-Meier curves and Cox proportional hazards models were used to compare OS of patients in different treatment groups. RESULTS:A total of 806 patients diagnosed between 2004 and 2014 met the study entry criteria. Of these, 394 patients (48.9%) received AC. IPTW-adjusted Kaplan-Meier curves showed that median OS was significantly longer for AC versus observation [57.4 (interquartile range, IQR, 14.8-153.8) vs 38.2 (IQR, 10.4-125.4) months; P = 0.007]. In IPTW-adjusted Cox proportional hazards regression analysis, AC was associated with a significant OS benefit [hazard ratio (HR) = 0.73, 95% confidence interval (CI) 0.64-0.84; P < 0.001]. The results were consistent across subgroups stratified by pathologic T stage, pathologic N stage, and surgical margin status. Subgroup analysis according to tumor location demonstrated improved OS in the adjuvant therapy cohort among patients with left-sided neuroendocrine carcinomas (HR, 0.55; 95% CI, 0.44-0.68), but not in those with right-sided disease (HR, 0.89; 95% CI, 0.74-1.07). CONCLUSIONS:Patients with nonmetastatic CRNECs may derive survival benefit from AC. These findings support current guidelines recommending AC in patients with poorly differentiated neuroendocrine carcinomas in the colon and rectum. Efforts in education and adherence to national guidelines for NECs are needed.Item Open Access Appropriate dose of regorafenib based on body weight of colorectal cancer patients: a retrospective cohort study.(BMC Cancer, 2023-12-21) Nakashima, Masayuki; Li, Kan; Chen, Qichen; de Silva, Sajith; Li, Hal; Kawakami, Koji; Wei, Qingyi; Luo, Sheng; Zhao, HongPURPOSE: Previous randomized studies have shown a survival benefit of using regorafenib but a high rate of adverse events in unresectable colorectal cancer patients. To reduce these adverse events and improve the tolerability, we examined the appropriate dose of regorafenib based on body weight. METHODS: We used a nationwide claims database in Japan and examined the efficacy and safety of regorafenib for patients with metastatic colorectal cancer between groups divided by body weight (60 kg) and median average dose (120 mg) between 2013 and 2018. We also assessed overall survival (OS) and adverse events between these groups. RESULTS: We identified 2530 Japanese patients (heavy weight/high dose: 513, light weight/low dose: 921, heavy weight/low dose: 452, and light weight/high dose: 644). There was no significant difference in the adverse events and OS after inverse probability treatment weighting (IPTW) adjustment between heavy weight/high dose group and light weight/low dose group (hazard ratio, HR=0.97). Among the light-weight patients, higher average dose was associated with shorter OS (IPTW adjusted HR=1.21, 95% CI 1.05 - 1.39, Table 3) while among the heavy-weight patients, there was no significant difference in OS between high and low dose groups (IPTW adjusted HR=1.14, 95% CI 0.95 - 1.37). CONCLUSION: The findings suggest that a low dose of regorafenib for light-weight patients may be as safe and effective as high doses for heavy-weight patients. Further studies should be conducted to identify an appropriate dose based on each patient's physique and condition.Item Open Access Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data.(Computational statistics & data analysis, 2019-01) Li, Kan; Luo, ShengA multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (functional and scalar) longitudinal and survival processes. This data structure is increasingly common across medical studies of neurodegenerative diseases and is exemplified by the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study, in which serial brain imaging, clinical and neuropsychological assessments are collected to measure the progression of Alzheimer's disease (AD). The proposed functional joint model consists of a longitudinal function-on-scalar submodel, a regular longitudinal submodel, and a survival submodel which allows time-dependent functional and scalar covariates. A Bayesian approach is adopted for parameter estimation and a dynamic prediction framework is introduced for predicting the subjects' future health outcomes and risk of AD conversion. The proposed model is evaluated by a simulation study and is applied to the motivating ADNI study.Item Open Access Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.(Statistics in medicine, 2021-10-14) Zou, Haotian; Li, Kan; Zeng, Donglin; Luo, Sheng; Alzheimer's Disease Neuroimaging InitiativeAlzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.Item Open Access Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.(Statistics in medicine, 2019-10) Li, Kan; Luo, ShengThis paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.Item Open Access Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.(Statistical methods in medical research, 2020-07-29) Lin, Jeffrey; Li, Kan; Luo, ShengThe random survival forest (RSF) is a non-parametric alternative to the Cox proportional hazards model in modeling time-to-event data. In this article, we developed a modeling framework to incorporate multivariate longitudinal data in the model building process to enhance the predictive performance of RSF. To extract the essential features of the multivariate longitudinal outcomes, two methods were adopted and compared: multivariate functional principal component analysis and multivariate fast covariance estimation for sparse functional data. These resulting features, which capture the trajectories of the multiple longitudinal outcomes, are then included as time-independent predictors in the subsequent RSF model. This non-parametric modeling framework, denoted as functional survival forests, is better at capturing the various trends in both the longitudinal outcomes and the survival model which may be difficult to model using only parametric approaches. These advantages are demonstrated through simulations and applications to the Alzheimer's Disease Neuroimaging Initiative.Item Open Access Impact of HER2-low status for patients with early-stage breast cancer and non-pCR after neoadjuvant chemotherapy: a National Cancer Database Analysis.(Breast cancer research and treatment, 2023-12) Li, Huiyue; Plichta, Jennifer K; Li, Kan; Jin, Yizi; Thomas, Samantha M; Ma, Fei; Tang, Li; Wei, Qingyi; He, You-Wen; Chen, Qichen; Guo, Yuanyuan; Liu, Yueping; Zhang, Jian; Luo, ShengPurpose
To investigate potential differences in pathological complete response (pCR) rates and overall survival (OS) between HER2-low and HER2-zero patients with early-stage hormone receptor (HR)-positive and triple-negative breast cancer (TNBC), in the neoadjuvant chemotherapy setting.Methods
We identified early-stage invasive HER2-negative BC patients who received neoadjuvant chemotherapy diagnosed between 2010 and 2018 in the National Cancer Database. HER2-low was defined by immunohistochemistry (IHC) 1+ or 2+ with negative in situ hybridization, and HER2-zero by IHC0. All the methods were applied separately in the HR-positive and TNBC cohorts. Logistic regression was used to estimate the association of HER2 status with pCR (i.e. ypT0/Tis and ypN0). Kaplan-Meier method and Cox proportional hazards model were applied to estimate the association of HER2 status with OS. Inverse probability weighting and/or multivariable regression were applied to all analyses.Results
For HR-positive patients, 70.9% (n = 17,934) were HER2-low, whereas 51.1% (n = 10,238) of TNBC patients were HER2-low. For both HR-positive and TNBC cohorts, HER2-low status was significantly associated with lower pCR rates [HR-positive: 5.0% vs. 6.7%; weighted odds ratio (OR) = 0.81 (95% CI: 0.72-0.91), p < 0.001; TNBC: 21.6% vs. 24.4%; weighted OR = 0.91 (95% CI: 0.85-0.98), p = 0.007] and improved OS [HR-positive: weighted hazard ratio = 0.85 (95% CI: 0.79-0.91), p < 0.001; TNBC: weighted hazard ratio = 0.91 (95% CI: 0.86-0.96), p < 0.001]. HER2-low status was associated with favorable OS among patients not achieving pCR [HR-positive: adjusted hazard ratio = 0.83 (95% CI: 0.77-0.89), p < 0.001; TNBC: adjusted hazard ratio = 0.88 (95% CI 0.83-0.94), p < 0.001], while no significant difference in OS was observed in patients who achieved pCR [HR-positive: adjusted hazard ratio = 1.00 (95% CI: 0.61-1.63), p > 0.99; TNBC: adjusted hazard ratio = 1.11 (95% CI: 0.85-1.45), p = 0.44].Conclusion
In both early-stage HR-positive and TNBC patients, HER2-low status was associated with lower pCR rates. HER2-zero status might be considered an adverse prognostic factor for OS in patients not achieving pCR.Item Open Access Impact of primary tumor resection and metastasectomy among gastroentero-pancreatic neuroendocrine tumors with liver metastases only on survival.(HPB : the official journal of the International Hepato Pancreato Biliary Association, 2023-09) Chen, Qichen; Li, Kan; Rhodin, Kristen E; Bartholomew, Alex J; Lidsky, Michael E; Wei, Qingyi; Cai, Jianqiang; Luo, Sheng; Zhao, HongBackground
Despite recommendations for primary tumor resection (PTR) with or without liver resection (LR) in the patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and isolated liver metastases, there are conflicting data for their impact on overall survival (OS).Methods
2320 patients with GEP-NETs and isolated liver metastases were identified from NCDB. Multiple imputations were used to accommodate missing data, and inverse probability of treatment weighting (IPTW) was conducted to minimize bias.Results
Patients with PTR had a greater OS than those without PTR (3-year rate of 88.6% vs. 69.9%, P < 0.001), which was preserved in the adjusted analysis (IPTW-adjusted HR = 0.387, 95% CI: 0.264-0.567; P < 0.001). Patients with LR had a greater OS than those without LR (3-year rate 87.7% vs. 75.2%, P = 0.003), which was also preserved in adjusted analysis (IPTW-adjusted HR = 0.450, 95% CI: 0.229-0.885; P = 0.021). Patients undergoing both PTR and LR had the greatest survival advantage than those with other surgical interventions (P < 0.001).Conclusions
Either PTR or LR is associated with improved survival for GEP-NET patients with isolated liver metastases. However, there remains significant selection bias in the current study, and caution should be exercised when selecting patients for resection.Item Open Access Impact of surgical approach on short- and long-term outcomes in gastroenteropancreatic neuroendocrine carcinomas.(HPB : the official journal of the International Hepato Pancreato Biliary Association, 2023-06) Chen, Qichen; Rhodin, Kristen E; Li, Kan; Kanu, Elishama; Zani, Sabino; Lidsky, Michael E; Zhao, Jianjun; Wei, Qingyi; Luo, Sheng; Zhao, HongBackground
Literature is lacking on the impact of advancements in minimally invasive surgery (MIS) on outcomes for patients with gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs). Herein, we compared perioperative and oncologic outcomes among patients with GEP-NECs undergoing open, laparoscopic, and robotic resection.Methods
Patients with GEP-NECs diagnosed 2010-2019 were identified from the National Cancer Database (NCDB). We used the inverse probability of treatment weighting method to account for selection bias. Patients were stratified by surgical approach; and pairwise comparisons were conducted by analyzing short- and long-term outcomes.Results
Receipt of MIS increased from 34.2% in 2010 to 67.5 % in 2019. Altogether, 6560 patients met study criteria: 3444 (52.5%) underwent open resection, 2783 (42.4%) underwent laparoscopic resection and 333 (5.1%) underwent robotic resection. Compared with open resection, laparoscopic or robotic resection were associated with shorter post-operative length of stay, reduced 30-day and 90-day post-operative mortality, and prolonged overall survival (OS). Compared with laparoscopic resection, robotic resection was associated with reduced 90-day post-operative mortality, however, there was no significant difference in OS.Conclusion
This NCDB analysis demonstrates that MIS approaches for treating GEP-NECs have become more common, with improved perioperative mortality, shorter post-operative length of stay and favorable OS, compared with open resection.Item Open Access Outcomes of Lymph Node Dissection for Non-metastatic Pancreatic Neuroendocrine Tumors: A Propensity Score-Weighted Analysis of the National Cancer Database.(Annals of surgical oncology, 2019-06-17) Mao, Rui; Zhao, Hong; Li, Kan; Luo, Sheng; Turner, Megan; Cai, Jian-Qiang; Blazer, DanBACKGROUND:Although the National Comprehensive Cancer Network (NCCN) guidelines recommend use of lymph node dissection (LND) in patients with pancreatic neuroendocrine tumors (pNETs) > 2 cm, there is limited evidence to support the association between use of LND and overall survival (OS). METHODS:Patients with resected pNETs were identified in the National Cancer Database (2004-2014). The inverse probability of treatment weighting (IPTW) method was used to reduce the selection bias. IPTW-adjusted Kaplan-Meier curves and Cox proportional hazards models were used to compare OS of patients in different treatment groups. RESULTS:A total of 2664 patients diagnosed met the study entry criteria. Of these, 2132 patients (80.6%) received LND, with a median of nine nodes removed. Positive nodes were identified in 28.0% of patients who underwent LND. IPTW-adjusted Kaplan-Meier analysis showed that median OS was similar between the LND and LND-omitted groups (152.8 vs. 147.3 months; p = 0.61). In IPTW-adjusted Cox proportional hazards regression analysis, LND was not associated with an OS benefit (hazard ratio [HR] 1.15, 95% confidence interval [CI] 0.94-1.42; p = 0.18). The results were consistent across subgroups stratified by clinical T and N stages. Among patients with lymph node metastasis, the number of removed nodes (NRN) above the median was not associated with an improved OS (HR 0.82, 95% CI 0.60-1.13; p = 0.22). CONCLUSIONS:LND had no additional therapeutic benefit among patients undergoing resection for pNETs. The present findings should be considered when managing patients with resectable pNETs.Item Open Access Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis.(Contemporary clinical trials, 2018-04) Li, Kan; Yuan, Shuai Sammy; Wang, William; Wan, Shuyan Sabrina; Ceesay, Paulette; Heyse, Joseph F; Mt-Isa, Shahrul; Luo, ShengBenefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.Item Open Access Predicting the Risk of Huntington's Disease with Multiple Longitudinal Biomarkers.(Journal of Huntington's disease, 2019-06-22) Li, Fan; Li, Kan; Li, Cai; Luo, Sheng; PREDICT-HD and ENROLL-HD Investigators of the Huntington Study GroupBACKGROUND:Huntington's disease (HD) has gradually become a public health threat, and there is a growing interest in developing prognostic models to predict the time for HD diagnosis. OBJECTIVE:This study aims to develop a novel prognostic model that leverages multiple longitudinal biomarkers to inform the risk of HD. METHODS:The multivariate functional principal component analysis was used to summarize the essential information from multiple longitudinal markers and to obtain a set of prognostic scores. The prognostic scores were used as predictors in a Cox model to predict the right-censored time to diagnosis. We used cross-validation to determine the best model in PREDICT-HD (n = 1,039) and ENROLL-HD (n = 1,776); external validation was carried out in ENROLL-HD. RESULTS:We considered six commonly measured longitudinal biomarkers in PREDICT-HD and ENROLL-HD (Total Motor Score, Symbol Digit Modalities Test, Stroop Word Test, Stroop Color Test, Stroop Interference Test, and Total Functional Capacity). The prognostic model utilizing these longitudinal biomarkers significantly improved the predictive performance over the model with baseline biomarker information. A new prognostic index was computed using the proposed model, and can be dynamically updated over time as new biomarker measurements become available. CONCLUSION:Longitudinal measurements of commonly measured clinical biomarkers substantially improve the risk prediction of Huntington's disease diagnosis. Calculation of the prognostic index informs the patient's risk category and facilitates patient selection in future clinical trials.Item Open Access Primary tumor resection improves survival of gastrointestinal neuroendocrine carcinoma patients with nonresected liver metastases.(Journal of surgical oncology, 2023-02) Chen, Qichen; Li, Kan; Rhodin, Kristen E; Masoud, Sabran J; Lidsky, Michael E; Cai, Jianqiang; Wei, Qingyi; Luo, Sheng; Zhao, HongBackground
The role of primary tumor resection (PTR) in the survival of gastrointestinal neuroendocrine carcinoma (GI-NEC) patients with liver metastases only remains poorly defined. Therefore, we investigated the impact of PTR on the survival of GI-NEC patients with nonresected liver metastases.Methods
GI-NEC patients with a liver-confined metastatic disease diagnosed between 2016 and 2018 were identified in the National Cancer Database. Multiple imputations by chained equations were used to account for missing data, and the inverse probability of treatment weighting (IPTW) method was used to eliminate selection bias. Overall survival (OS) was compared by adjusted Kaplan-Meier curves and log-rank test with IPTW.Results
A total of 767 GI-NEC patients with nonresected liver metastases were identified. Among all patients, 177 (23.1%) received PTR and had a significantly favorable OS before (median: 43.6 months [interquartile range, IQR, 10.3-64.4] vs. 8.8 months [IQR, 2.1-23.1], p < 0.001 in log-rank test) and after (median: 25.7 months [IQR, 10.0-64.4] vs. 9.3 months [IQR, 2.2-26.4], p < 0.001 in IPTW-adjusted log-rank test) the IPTW adjustment. Additionally, this survival advantage persisted in an adjusted Cox model (IPTW adjusted hazard ratio = 0.431, 95% confidence interval: 0.332-0.560; p < 0.001). The improved survival persisted in subgroups stratified by primary tumor site, tumor grade, and N stage, even in the complete cohort (excluding patients with missing data).Conclusions
PTR led to improved survival for GI-NEC patients with nonresected liver metastases regardless of primary tumor site, tumor grade, and N stage. However, the decision for PTR should be made on an individualized basis following multidisciplinary evaluation.Item Open Access Quantitative description of residual helical structure for λ-repressor N-terminal domain in the unfolded state(2017) Li, KanProteins can form residual compactness in the unfolded state. Among different types of residual compactness, residual helical structure is an important type of local compactness that can propagate through the formation of helical hydrogen bonds. Residual helicity has been observed for different unfolded state proteins. In order to accurately determine the contributions of individual residues to the overall helicity, accurate determination of residue-specific information and quantitative analysis methods are needed.
The projects in this dissertation aim at quantitatively describing the residual helical conformation in the unfolded state of λ-repressor N-terminal domain. The residue-specific helicity values and backbone amide proton hydrogen bonding populations are analyzed using improved methods based on Bayesian inference. Generally, these values are higher for the helix 1 region in the context of the N-terminal domain than as an isolated peptide. Experimentally determined residue-specific helicity values of unfolded state λ-repressor N-terminal domain show similarity to the theoretical prediction using helix-coil model.
These results show that, in the unfolded state of λ-repressor N-terminal domain, the propagation of residual helicity does not significantly depend on tertiary interactions. The results support the hypothesis that λ-repressor N-terminal domain folds by “diffusion-collision”.