A propensity score-integrated approach for leveraging external data in a randomized controlled trial with time-to-event endpoints.

In a randomized controlled trial with time-to-event endpoint, some commonly used statistical tests to test for various aspects of survival differences, such as survival probability at a fixed time point, survival function up to a specific time point, and restricted mean survival time, may not be directly applicable when external data are leveraged to augment an arm (or both arms) of an RCT. In this paper, we propose a propensity score-integrated approach to extend such tests when external data are leveraged. Simulation studies are conducted to evaluate the operating characteristics of three propensity score-integrated statistical tests, and an illustrative example is given to demonstrate how these proposed procedures can be implemented.

Statistical considerations for some issues in clinical bridging studies evaluating companion diagnostic devices.

An in vitro diagnostic device (IVD) that is essential for the safe and effective use of a corresponding therapeutic product is commonly referred to as companion diagnostic device. Clinical trials using companion diagnostic devices (tests) together with therapies can yield the information necessary to address whether both products are safe and effective. A clinical trial ideally assesses safety and effectiveness of a therapy, where the clinical trial enrolls subjects based on the final market ready companion diagnostic test (CDx). However, such a requirement may be difficult to accomplish or impractical to achieve at the time of the clinical trial enrollment, due to unavailability of the CDx. Instead, clinical trial assay(s) (CTA), which are not the final marketable product, are often used in enrollment of patients in a clinical trial. When CTA is used for subject enrollment, a clinical bridging study provides a mechanism to bridge the clinical efficacy of the therapeutic product from CTA to CDx. This manuscript reviews some issues and challenges commonly associated with clinical bridging studies, including missing data, use of local tests for enrollment, prescreening before enrollment, and evaluation of CDx for low positive rate biomarkers, with particular focus on clinical trials using a binary endpoint and provide alternative statistical methodologies to assess effectiveness of CDx.

Propensity score-incorporated adaptive design approaches when incorporating real-world data.

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

Principled leveraging of external data in the evaluation of diagnostic devices via the propensity score-integrated composite likelihood approach.

In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost needed for the performance evaluation of an investigational diagnostic device. However, the statistical methods currently being used for such leveraging may not clearly separate study design and outcome data analysis, and they may not adequately address possible bias due to differences in clinically relevant characteristics between the subjects constituting the traditional study and those constituting the external data. This paper is intended to draw attention in the field of diagnostics to the recently developed propensity score-integrated composite likelihood approach, which originally focused on therapeutic medical products. This approach applies the outcome-free principle to separate study design and outcome data analysis and can mitigate bias due to imbalance in covariates, thereby increasing the interpretability of study results. While this approach was conceived as a statistical tool for the design and analysis of clinical studies for therapeutic medical products, here, we will show how it can also be applied to the evaluation of sensitivity and specificity of an investigational diagnostic device leveraging external data. We consider two common scenarios for the design of a traditional diagnostic device study consisting of prospectively enrolled subjects, which is to be augmented by external data. The reader will be taken through the process of implementing this approach step-by-step following the outcome-free principle that preserves study integrity.

Propensity score-integrated approach to survival analysis: leveraging external evidence in single-arm studies.

External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.

Complementary and Alternative Medicine for Premature Ovarian Insufficiency: A Review of Utilization and Mechanisms.

Premature ovarian insufficiency (POI) is defined as a decline in ovarian function before the age of 40 and is one of the leading causes of infertility in women. The etiology is complex, and the pathogenesis is not clear. The main treatment is hormone replacement therapy, but a growing body of data confirms that such treatment can increase the risk of endometrial disease and cardiovascular disease. Complementary and alternative medicine (CAM) has been widely used in patients with POI due to its limited adverse reactions and high efficiency. According to literature reports, CAM therapy for POI mainly includes traditional Chinese medicine, acupuncture, psychotherapy, dietary supplements, and exercise therapy. This article reviews the application of CAM in the treatment of POI and attempts to determine the therapeutic effects and the mechanisms behind these effects based on existing clinical and experimental studies in order to provide theoretical support for the treatment of POI.

Estimands in observational studies: Some considerations beyond ICH E9 (R1).

The document ICH E9 (R1) has brought much attention to the concept of estimand in the clinical trials community. ICH stands for International Conference for Harmonization. In this article, we draw attention to one facet of estimand that is not discussed in that document but is crucial in the context of observational studies, namely weighting for covariate balance. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.

Leverage multiple real-world data sources in single-arm medical device clinical studies.

The interest in utilizing real-world data (RWD) has been considerably increasing in medical product development and evaluation. With proper usage and analysis of high-quality real-world data, real-world evidence (RWE) can be generated to inform regulatory and healthcare decision-making. This paper proposes a study design and data analysis approach for a prospective, single-arm clinical study that is supplemented with patients from multiple real-world data sources containing patient-level covariate and outcome data. After the amount of information to be borrowed from each real-world data source is determined, the propensity score-integrated composite likelihood method is applied to obtain an estimate of the parameter of interest based on data from the prospective clinical study and this real-world data source. This method is applied to each real-world data source. The final estimate of the parameter of interest is then obtained by taking a weighted average of all these estimates. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example is presented to illustrate how to implement the proposed approach.

Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches.

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

Propensity score-integrated power prior approach for augmenting the control arm of a randomized controlled trial by incorporating multiple external data sources.

In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.

Evaluation of diagnostic tests for low prevalence diseases: a statistical approach for leveraging real-world data to accelerate the study.

The evaluation of diagnostic tests usually involves statistical inference for its sensitivity. As sensitivity is defined as the probability that the test result will be positive when the target condition is present, the key study design consideration of sample size is the determination of the number of subjects with the target condition such that the estimation has adequate precision, or the hypothesis testing has adequate power. Traditionally, one may rely on prospective screening of subjects to obtain the required sample size, which means that if the prevalence of the disease is very low, a large number of subjects would need to be screened, increasing the study duration and cost. In this paper, we consider the possibility of substantially reducing the length and cost of a clinical study by leveraging subjects from a real-world data (RWD) source, focusing specifically on the diagnostic test for the cancer of interest. Using the propensity score methodology, we developed a procedure which ensures that the real-world subjects being leveraged are similar to their prospectively enrolled counterparts, thereby making the leveraging more justified. The procedure allows the down-weighting of the real-world subjects, which can be achieved by either using a Frequentist's method based on the composite likelihood or a Bayesian method based on the power prior. The proposed approach can be applied to the evaluation of any diagnostic test and it is not limited to the current clinical study regarding a cancer diagnostic test. Notably, this paper is in close alignment with a recently released draft framework by the Medical Device Innovation Consortium (MDIC) on real-world clinical evidence and in vitro diagnostics, being a showcase of appropriately leveraging real-world data in diagnostic test evaluation for diseases with low prevalence to support regulatory decision-making.

Multi-stage transitional models with random effects and their application to the Einstein aging study.

Longitudinal studies of aging often gather repeated observations of cognitive status to describe the development of dementia and to assess the influence of risk factors. Clinical progression to dementia is often conceptualized by a multi-stage model of several transitions that synthesizes time-varying effects. In this study, we assess the influence of risk factors on the transitions among three cognitive status: cognitive stability (normal cognition for age), memory impairment, and clinical dementia. We have developed a shared random effects model that not only links the propensity of transitions and to the probability of informative missingness due to death, but also incorporates heterogeneous transition between subjects. We evaluate four approaches using generalized logit and four using proportional odds models to the first-order Markov transition probabilities as a function of covariates. Random effects were incorporated into these models to account for within-subject correlations. Data from the Einstein Aging Study are used to evaluate the goodness-of-fit of these models using the Akaike information criterion. The best fitting model for each type (generalized logit and proportional odds) is recommended and their results are discussed in more details.

Outcomes in children who underwent transplantation for autoimmune hepatitis.

The outcomes of 113 children with autoimmune hepatitis (AIH), registered with Studies of Pediatric Liver Transplantation and who underwent transplantation between 1995 and 2006, were compared with those who underwent transplantation for other diagnoses (non-AIH). A total of 4.9% of liver transplants were for AIH; 81% of these patients had AIH type 1 and most underwent transplantation for complications of chronic disease (60%), the majority in females (72%). Transplantation for fulminant AIH was more common in males (52.5% versus 47.5% chronic; P = 0.042). Patients with AIH differed from non-AIH patients by: age (13.0 ± 0.4 versus 4.6 ± 0.1 years; P < 0.0001), sex (64.6% female versus 52.9%; P = 0.016), ethnicity (48.7% white versus 58.2%; P < 0.0001), initial immunosuppression (tacrolimus-based: 72.6% versus 62.6%; P = 0.045; mycophenolate mofetil use: 31.0% versus 21.6%; P = 0.02), and immunosuppression at 2 years after transplant (monotherapy: 51.9% versus 17.3%; P < 0.0001). Late (>3 months), but not steroid-resistant or chronic, rejection was more common in AIH (log-rank P = 0.0015). The 5-year posttransplant survival for AIH was 86% (95% confidence interval: 73-93). Patient and graft survival, infectious and metabolic complications, and retransplantation rates did not differ between AIH and non-AIH groups. In conclusion, the higher risk for late acute rejection and greater degree of immunosuppression does not compromise outcomes of liver transplantation for AIH. Children who undergo transplantation for AIH in North America are typically female adolescents with complications of chronic AIH type 1 and include more children of African American or Latino American origin compared to the overall liver transplant population. These observations may inform detection, treatment, and surveillance strategies designed to reduce the progression of autoimmune hepatitis and subsequently, the need for transplantation.

Evaluation of the performance of tests for spatial randomness on prostate cancer data.

BACKGROUND: Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real unadjusted data and data adjusted for individual- and area-level covariates has not been reported previously.We evaluated data on prostate cancer histologic tumor grade and stage of disease at diagnosis for incident cases of prostate cancer reported to the Maryland Cancer Registry during 1992-1997. We analyzed unadjusted data as well as expected counts from models that were adjusted for individual-level covariates (race, age and year of diagnosis) and area-level covariates (census block group median household income and a county-level socioeconomic index). We chose 3 spatial clustering tests that are commonly used to evaluate the geographic distribution of disease: Cuzick-Edwards' k-NN (k-Nearest Neighbors) test, Moran's I and Tango's MEET (Maximized Excess Events Test). RESULTS: For both grade and stage at diagnosis, we found that Cuzick-Edwards' k-NN and Moran's I were very sensitive to the percent of population parameter selected. For stage at diagnosis, all three tests showed that the models with individual- and area-level adjustments reduced clustering the most, but did not reduce it entirely. CONCLUSION: Based on this specific example, results suggest that these tests provide useful tools for evaluating spatial clustering of disease characteristics, both before and after consideration of covariates.

Impact of graft type on outcome in pediatric liver transplantation: a report From Studies of Pediatric Liver Transplantation (SPLIT).

OBJECTIVE: To examine the outcome of technical variant liver transplant techniques relative to whole organ liver transplantation in pediatric liver transplant recipients. BACKGROUND: Technical variant liver transplant techniques comprising split, reduced, and live-donor liver transplantation evolved to address the need for timely and size appropriate grafts for pediatric recipients. METHODS: Analysis of data from the Studies of Pediatric Liver Transplantation (SPLIT) registry, a multicenter database of 44 North American pediatric liver transplant programs. The outcome (morbidity and mortality) of each of the technical variants were compared with that of whole organ recipients. RESULTS: Data were available on 2192 transplant recipients (1183 whole, 261 split, 388 reduced, and 360 live donor). Recipients of all technical variant graft type were significantly younger than whole organ recipients, but on average spent 2.3 months less on the waiting list. Thirty-day post-transplant morbidity was increased for each type of technical variant relative to whole organ (45.1% whole, 66.7% split, 65.5% reduced, 51.9% live-donor). Biliary complications (30 day: 7.5% whole, 18.8% split, 16% reduced, 17.5% live-donor) and portal vein thrombosis (30 day: 3.6% whole, 8% split, 8% reduced, 7.5% live-donor) were more common in all technical variant types. Graft type was an independent predictor of graft loss (death or retransplantation) in a multivariate analysis. Split and reduced (relative risk = 1.74 and 1.77, respectively) grafts had a worse outcome when compared with whole organ recipients. CONCLUSIONS: Technical variant techniques expand the pediatric donor pool and reduce time from listing to transplant, but they are associated with increased morbidity and mortality.

Targeted versus daily naltrexone: secondary analysis of effects on average daily drinking.

BACKGROUND: To extend our previous findings that naltrexone reduced the likelihood of heavy drinking on a given day among problem drinkers, while targeted administration reduced the likelihood of any drinking, we examined the effects of naltrexone and targeted administration on the continuous outcome of drinks/day. Because treatment response may differ by gender, we also compared the effects on this factor. METHODS: In a double-blind, placebo-controlled study, problem drinkers (n=150, 58% men) were randomly assigned to 8 weeks of treatment with naltrexone (50 mg/day) or placebo, either daily or on a targeted schedule. All subjects also received brief coping skills therapy. To complement the traditional regression analysis conducted previously, a zero-inflated Poisson regression model was used to examine the effects of medication, schedule of administration, and gender on the number of standard drinks consumed daily. RESULTS: Targeted naltrexone, and to a lesser extent targeted placebo, yielded a greater reduction in daily drinking than did daily placebo, an effect that did not differ by gender and that was greater than that seen for daily naltrexone treatment. Relative to daily placebo, daily naltrexone reduced the number of drinks/day only among men, at the level of a nonsignificant trend. CONCLUSIONS: Although in both genders, targeted treatments appeared to reduce the volume of drinking, treatment with targeted naltrexone was somewhat better. In contrast, heavy drinking women showed no benefit from daily naltrexone treatment. Further evaluation of the efficacy of targeted treatments and of daily naltrexone and the relationship of these treatments with gender is warranted.

Likelihood based tests for spatial randomness.

Many different methods have been proposed to test the spatial randomness of a point pattern adjusting for an inhomogeneous background population. These tests can be classified into cluster detection tests, concerned with the detection and inference of local clusters, and global clustering tests, which collect evidence for clustering throughout the study region. This paper is mainly concerned about global clustering tests. Some tests for spatial randomness are based on likelihoods, which include the spatial and space-time scan statistics with variable window size and Gangnon and Clayton's weighted average likelihood ratio tests. Both of these tests perform well compared to other tests for cluster detection and global clustering, respectively. In this study, we develop other likelihood based tests for global clustering and we explore the use of different weight functions with these tests. The power of these tests is evaluated using simulated data set and compared with existing methods.

Geographically based investigation of prostate cancer mortality in four U.S. Northern Plain states.

BACKGROUND: Historically, prostate cancer mortality rates have been elevated in the U.S. Northern Plains states. The purpose of this study was to investigate possible contributing factors, especially whether there was any association with crop patterns. METHODS: Prostate cancer mortality rates (1950-2000) in four northern plains states (MN, MT, ND, and SD) were compared to rates for 46 other U.S. states. Within the four states, county rates in urban, less urban, and rural areas also were compared. For additional analysis, urban counties and counties with <10% of county area in crops were excluded. The average percent of county area in total cropland 1930-1950 and 1954-1974 was estimated. Using Poisson regression, we investigated whether the average percentage of county area in total cropland, 1930-1950 and 1954-1974, was associated with prostate cancer mortality rates, 1975-2000, respectively. Poisson regression analyses were also used to evaluate associations between rates and major crops, which included spring and durum wheat, winter wheat, corn, and other crops. Population centroids of the Census 2000 block groups were used to estimate the percentage of males aged 35 and older residing in close proximity to small grains crops. RESULTS: Mortality rates were higher in rural compared to urban counties in 1950-2000 (rate ratio [RR]=1.032; 95% CI=1.001-1.063). Rates in 1950-1974 were significantly associated with production of corn and other crops in 1930-1950 (corn: RR per 10% increase=1.033, 95% CI=1.012-1.054; other crops: RR=1.042, 95% CI=1.021-1.063). Mortality rates in 1975-2000 were significantly associated with spring and durum wheat production in 1954-1974 (RR per 10% increase=1.042, 95% CI=1.017-1.067). Prostate cancer mortality rates increased as the percentage of population living within 500 m of small grains crops increased. CONCLUSIONS: Epidemiologic studies to evaluate agricultural practices are warranted to further evaluate the observed associations.

Cancer map patterns: are they random or not?

BACKGROUND: Maps depicting the geographic variation in cancer incidence, mortality or treatment can be useful tools for developing cancer control and prevention programs, as well as for generating etiologic hypotheses. An important question with every cancer map is whether the geographic pattern seen is due to random fluctuations, as by pure chance there are always some areas with more cases than expected, or whether the map reflects true underlying geographic variation in screening, treatment practices, or etiologic risk factors. METHODS: Nine different tests for spatial randomness are evaluated in very practical settings by applying them to cancer maps for different types of data at different scales of spatial resolution: breast, prostate, and thyroid cancer incidence; breast cancer treatment and prostate cancer stage in Connecticut; and nasopharynx and prostate cancer mortality in the U.S. RESULTS: Tango's MEET, Oden's Ipop, and the spatial scan statistic performed well across all the data sets. Besag-Newell's R, Cuzick-Edwards k-NN, and Turnbull's CEPP often perform well, but the results are highly dependent on the parameter chosen. Moran's I performs poorly for most data sets, whereas Swartz Entropy Test and Whittemore's Test perform well for some data sets but not for other. CONCLUSIONS: When publishing cancer maps we recommend evaluating the spatial patterns observed using Tango's MEET, a global clustering test, and the spatial scan statistic, a cluster detection test.

Tango's maximized excess events test with different weights.

BACKGROUND: Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting for the multiple testing. This means that it has good power to detect a wide range of clustering processes. The test depends on the functional form of a weight function, and it is unknown how sensitive the test is to the choice of this weight function and what function provides optimal power for different clustering processes. In this study, we evaluate the performance of the test for a wide range of weight functions. RESULTS: The power varies greatly with different choice of weight. Tango's original choice for the weight function works very well. There are also other weight functions that provide good power. CONCLUSION: We recommend the use of Tango's MEET to test global disease clustering, either with the original weight or one of the alternate weights that have good power.