Precision Of Survival Estimates Research Articles

Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab.

Therapy optimization remains an important challenge in the treatment of advanced non‐small cell lung cancer (NSCLC). We investigated tumor size (sum of the longest diameters (SLD) of target lesions) and neutrophil‐to‐lymphocyte ratio (NLR) as longitudinal biomarkers for survival prediction. Data sets from 335 patients with NSCLC from study NCT02087423 and 202 patients with NSCLC from study NCT01693562 of durvalumab were used for model qualification and validation, respectively. Nonlinear Bayesian joint models were designed to assess the impact of longitudinal measurements of SLD and NLR on patient subgrouping (by Response Evaluation Criteria in Solid Tumors 1.1 criteria at 3 months after therapy start), long‐term survival, and precision of survival predictions. Various validation scenarios were investigated. We determined a more distinct patient subgrouping and a substantial increase in the precision of survival estimates after the incorporation of longitudinal measurements. The highest performance was achieved using a multivariate SLD and NLR model, which enabled predictions of NSCLC clinical outcomes.

Designing a Monitoring Program to Estimate Estuarine Survival of Anadromous Salmon Smolts: Simulating the Effect of Sample Design on Inference.

A number of researchers have attempted to estimate salmonid smolt survival during outmigration through an estuary. However, it is currently unclear how the design of such studies influences the accuracy and precision of survival estimates. In this simulation study we consider four patterns of smolt survival probability in the estuary, and test the performance of several different sampling strategies for estimating estuarine survival assuming perfect detection. The four survival probability patterns each incorporate a systematic component (constant, linearly increasing, increasing and then decreasing, and two pulses) and a random component to reflect daily fluctuations in survival probability. Generally, spreading sampling effort (tagging) across the season resulted in more accurate estimates of survival. All sampling designs in this simulation tended to under-estimate the variation in the survival estimates because seasonal and daily variation in survival probability are not incorporated in the estimation procedure. This under-estimation results in poorer performance of estimates from larger samples. Thus, tagging more fish may not result in better estimates of survival if important components of variation are not accounted for. The results of our simulation incorporate survival probabilities and run distribution data from previous studies to help illustrate the tradeoffs among sampling strategies in terms of the number of tags needed and distribution of tagging effort. This information will assist researchers in developing improved monitoring programs and encourage discussion regarding issues that should be addressed prior to implementation of any telemetry-based monitoring plan. We believe implementation of an effective estuary survival monitoring program will strengthen the robustness of life cycle models used in recovery plans by providing missing data on where and how much mortality occurs in the riverine and estuarine portions of smolt migration. These data could result in better informed management decisions and assist in guidance for more effective estuarine restoration projects.

Estimating survival of rock lobsters from long-term tagging programmes: how survey number and interval influence estimates

Abstract A long-term tagging dataset on southern rock lobster (Jasus edwardsii) conducted at the Crayfish Point Scientific Reserve near Hobart, Tasmania, Australia, was used to determine how survey number and survey duration affected the precision of survival estimates of male and female lobsters to ensure sustainable exploitation of the population. Tagging surveys were undertaken twice yearly during 2000–2003 with unequal time-intervals between surveys and then once a year with equal time-intervals during 2004–2012 during the January–February period. The most parsimonious Cormack–Jolly–Seber model for estimating survival of lobsters was dependent on (i) timing of the tagging and recapture surveys, (ii) time between consecutive recapture surveys, and (iii) gender. The number of surveys required to provide a precise survival probability varied with gender and time between recapture surveys. For surveys where there was unequal time between recapture surveys, seven and five surveys were required for female and male lobsters, respectively, whereas only five surveys were required when annual surveys were undertaken. Thus, lobster tagging projects using annual surveys should ideally extend to at least 5 years, which is beyond the 3-year project duration common in marine science.

Effects of radio-tag characteristics and sample size on estimates of apparent survival

Background: Radio-tracking is increasingly used to assess key characteristics of population dynamics. Since in many species re-encounter rates are frequently below 1.0 and vary with time and/or life-history stages, known-fate approaches to analyses may not apply. Cormack-Jolly-Seber (CJS) models estimate apparent survival on the basis of individual encounter histories. These models allow for complex re-encounter models and constitute an ideal tool to estimate apparent survival where re-encounter rates vary. However, the implications of radio-tag characteristics and sample size on the precision of survival estimates and the potential to determine temporal variation in survival have rarely been investigated. Here we analyze radio-tracking data from juvenile barn swallows (Hirundo rustica )t hat were instrumented with four types of transmitters similar in mass but differing in radiated power (n=560, 132 broods). Results: For all transmitter types re-encounter probabilities varied between 0.2 and 0.75, depending on radiated power and bird age. Higher-power transmitters significantly improved the re-encounter rates and thus, the precision of survival estimates. Apparent survival varied with age, with a minimum around the break-up of families. Later on survival increased again and approached the rate of adults. Analyses of random sub-samples revealed that sample size strongly affected the variance in survival estimates, and thus the power in discerning temporal or between-group variation in survival. Small samples substantially underestimated the survival to independence. In small subsamples the standard errors of estimates were particularly large in later re-encounters. Consequently, model selection results of different survival models on the basis of small random sub-samples were highly inconsistent. Conclusions: Investigating population processes requires modeling of time- and cohort-dependent survival rates, often for short time periods. We show that CJS estimates of apparent survival from small samples revealed rough patterns in barn swallow survival with samples of c. 50 individuals. However, small samples underestimated the number of survivors reaching independence. Inference on underlying mechanistic models based on Akaike’s Information Criterion (AIC) model selection was unreliable with sample sizes below 200 individuals. As samples are often limited for practical reasons, maximizing re-encounter rates by optimal choice of telemetry hardware and field logistics is a way to increase the precision of survival estimates.

Improving Inferences from Fisheries Capture‐Recapture Studies through Remote Detection of PIT Tags

Models for capture‐recapture data are commonly used in analyses of the dynamics of fish and wildlife populations, especially for estimating vital parameters such as survival. Capture‐recapture methods provide more reliable inferences than other methods commonly used in fisheries studies. However, for rare or elusive fish species, parameter estimation is often hampered by small probabilities of re‐encountering tagged fish when encounters are obtained through traditional sampling methods. We present a case study that demonstrates how remote antennas for passive integrated transponder (PIT) tags can increase encounter probabilities and the precision of survival estimates from capture‐recapture models. Between 1999 and 2007, trammel nets were used to capture and tag over 8,400 endangered adult Lost River suckers (Deltistes luxatus) during the spawning season in Upper Klamath Lake, Oregon. Despite intensive sampling at relatively discrete spawning areas, encounter probabilities from Cormack‐Jolly‐Seber models were consistently low (< 0.2) and the precision of apparent annual survival estimates was poor. Beginning in 2005, remote PIT tag antennas were deployed at known spawning locations to increase the probability of re‐encountering tagged fish. We compare results based only on physical recaptures with results based on both physical recaptures and remote detections to demonstrate the substantial improvement in estimates of encounter probabilities (approaching 100%) and apparent annual survival provided by the remote detections. The richer encounter histories provided robust inferences about the dynamics of annual survival and have made it possible to explore more realistic models and hypotheses about factors affecting the conservation and recovery of this endangered species. Recent advances in technology related to PIT tags have paved the way for creative implementation of large‐scale tagging studies in systems where they were previously considered impracticable.

Period estimates of cancer patient survival are more up-to-date than complete estimates even at comparable levels of precision

Background and Objective Period analysis provides more up-to-date estimates of cancer patient survival than traditional methods, but there is a trade-off between up-to-dateness and precision. Our objective was to compare the performance of period and complete analysis in terms of up-to-dateness and precision of survival estimates. Study Design and Setting Five-year relative survival data actually observed for patients diagnosed with 1 of 20 common forms of cancer in Finland in 36 overlapping 5-year periods between 1958–1962 and 1993–1997 were compared with period estimates and various variants of complete estimates of 5-year relative survival potentially available during these periods. Results At comparable levels of up-to-dateness, survival estimates from period analysis were more precise than survival estimates from complete analysis. At comparable levels of precision, period analysis provided more up-to-date survival estimates than did complete analysis. Conclusion These results further encourage more widespread use of period analysis as a standard tool for up-to-date monitoring of cancer patient survival by population-based cancer registries.

WINTER SURVIVAL OF LESSER SCAUP IN EAST-CENTRAL FLORIDA

The North American continental population of lesser scaup (Aythya affinis) has been declining since the mid-1980s. Seasonal survival estimates may provide insights about the ecological basis for this decline, but such data are not available. We estimated post-harvest winter survival of lesser scaup in east-central Florida, USA, where 62% of the Atlantic Flyway population winters. The Kaplan-Meier survival estimate from 11 January to 14 March 2002 was 0.95 ± 0.04 (SE) for females and 0.90 ± 0.09 for males. These estimates were not different (P = 0.64), and pooled survival was 0.93 ± 0.04. Temporary emigration (movement out of and return to the study area) was exhibited by 25% of the birds during survey periods, but absences were short and were believed to have had little effect on precision of survival estimates. Our findings suggested that natural mortality at Merritt Island National Wildlife Refuge (MINWR) and surrounding estuarine areas was relatively low. Our results also indicate that habitat quality in this portion of east-central Florida was sufficient to meet overwintering requirements and likely contributed to the reported survival rates. Estimating survival during other stages of the annual cycle, as well as an overall winter estimate reflecting harvest mortality, is necessary to determine whether low survival rates are responsible for continental population declines.

ESTIMATING SURVIVAL AND TEMPORARY EMIGRATION IN THE MULTISTATE CAPTURE–RECAPTURE FRAMEWORK

The open population Cormack-Jolly-Seber (CJS) capture–mark–recapture model for estimating survival allows for random temporary emigration from the sampling area, but Markovian temporary emigration can bias estimates of survival. We explore a multistate capture–recapture model that has been proposed for coping with Markovian temporary emigration. We provide a comprehensive assessment of the performance of this model using computer algebra and simulation. We found that most model parameters were identifiable unless survival, emigration, and immigration were all time dependent. Simulation results showed that intrinsically identifiable parameters were estimated without bias and that precision of survival estimates was always high. When temporary emigration was Markovian, precision of estimates of emigration, immigration, and recapture probabilities was acceptable; otherwise it was not. Test component 2.Ct of the goodness-of-fit test for the CJS model had good power to detect Markovian temporary emigration. We conclude that the multistate model works well when temporary emigration is Markovian (i.e., when the CJS model should not be used) and when survival and recapture probabilities are high.

Prerelease Stratification in Tag-Recovery Models with Time Dependence

A statistical model is developed for estimating time-dependent survival and recovery rates of tagged populations that are stratified into groups at release. For populations of fish, the groups might include different sexes or stocks, or they might be distinguished by the location or method of capture. In the model developed here, survival and recovery rates are formulated as linear combinations of parameters that include the effects of time, group, and time–group interactions. Methods of testing the statistical significance of these potential sources of variation are described and illustrated with the recoveries of different stocks of anadromous striped bass (Morone saxatilis). For the striped bass data, elimination of time- and stock-specific sources of variation substantially improved the precision of survival estimates without introducing important bias. During 1988–89 the average survival of both stocks was 0.78 (SE = 0.04). Calculations of statistical power indicate that stock-specific differences in survival of ± 0.2 could have been detected about 50% of the time, given the low rates of recovery and numbers of fish released.