Pre-launch calibration of the HIRDLS instrument

Report about the aims for the calibration of the HIRDLS instrument

Fructan and its relationship to abiotic stress tolerance in plants

Numerous studies have been published that attempted to correlate fructan concentrations with freezing and drought tolerance. Studies investigating the effect of fructan on liposomes indicated that a direct interaction between membranes and fructan was possible. This new area of research began to move fructan and its association with stress beyond mere correlation by confirming that fructan has the capacity to stabilize membranes during drying by inserting at least part of the polysaccharide into the lipid headgroup region of the membrane. This helps prevent leakage when water is removed from the system either during freezing or drought. When plants were transformed with the ability to synthesize fructan, a concomitant increase in drought and/or freezing tolerance was confirmed. These experiments indicate that besides an indirect effect of supplying tissues with hexose sugars, fructan has a direct protective effect that can be demonstrated by both model systems and genetic transformation

Analysis of multivariate failure-time data from HIV clinical trials.

We illustrate the use of marginal methods for the analysis of multivariate failure-time data using a large trial in HIV infection in which the composite endpoint of AIDS or death incorporates more than 20 events with varying severity. Multivariate failure-time methods are required to investigate whether treatment delays development of new AIDS events. AIDS events can be grouped and treatment effects estimated using only the first event to occur in each group for each individual. Alternatively, all events can be included by fitting a separate baseline hazard for development of each event, and restricting treatment effects to be common within groups of events. In either case, model-based or minimum-variance estimates of the overall effect of treatment can be constructed. The covariance matrix for the treatment-effect estimates can be used in multiple testing procedures. Results from the Delta trial suggest that combination antiretroviral therapy with AZT plus either ddI or ddC may delay progression to more severe AIDS events compared to AZT monotherapy. These late events are generally untreatable and prophylaxis is not available. Trials are not generally powered to detect treatment effects on individual events making up a composite endpoint, and therefore all analyses are exploratory rather than providing definitive evidence. However, marginal multivariate models provide an easily available approach for modeling the effect of covariates on multiple disease processes, and allow the likely effects of treatment to be presented in a manner which is easily understood. They can be used in a variety of ways to explore different patterns of treatment effects and are also useful for testing multiple hypotheses regarding treatment effects on several different composite endpoints

Issues in the design and analysis of therapeutic trials in human immunodeficiency virus infection

The first randomized trial of antiviral therapy in human immunodeficiency virus (HIV) disease included 282 patients with acquired immune deficiency syndrome (AIDS) or AIDS-related complex and was stopped in 1986 after an average follow-up of 4 months because of a substantial reduction in mortality in the group who received zidovudine (AZT). The era of anti-HIV treatment had begun This paper discusses some of the difficulties which have emerged over the subsequent 10 years as new anti-HIV drugs have been developed requiring evaluation in clinical trials. The trials in which the British Medical Research Council has played a major role (the Concorde, Alpha and Delta trials) and some of the key trials conducted by the AIDS Clinical Trials Group (ACTG) (the ACTG 019 and ACTG 175 trials) and the Community Programs for Clinical Research on AIDS (CPCRA) (the CPCRA 007 trial) in the USA will be used to illustrate some of the issues faced by clinical trialists and governmental regulatory agencies in the evaluation of therapies for a disease which, in spite of advances in therapy, still has a high mortality

Issues in the design and analysis of therapeutic trials in human immunodeficiency virus infection

The first randomized trial of antiviral therapy in human immunodeficiency virus (HIV) disease included 282 patients with acquired immune deficiency syndrome (AIDS) or AIDS-related complex and was stopped in 1986 after an average follow-up of 4 months because of a substantial reduction in mortality in the group who received zidovudine (AZT). The era of anti-HIV treatment had begun This paper discusses some of the difficulties which have emerged over the subsequent 10 years as new anti-HIV drugs have been developed requiring evaluation in clinical trials. The trials in which the British Medical Research Council has played a major role (the Concorde, Alpha and Delta trials) and some of the key trials conducted by the AIDS Clinical Trials Group (ACTG) (the ACTG 019 and ACTG 175 trials) and the Community Programs for Clinical Research on AIDS (CPCRA) (the CPCRA 007 trial) in the USA will be used to illustrate some of the issues faced by clinical trialists and governmental regulatory agencies in the evaluation of therapies for a disease which, in spite of advances in therapy, still has a high mortality

Impact of missing data due to selective dropouts in cohort studies and clinical trials

Background. Many cohort studies and clinical trials use repeated measurements of laboratory markers to track disease progression and to evaluate new therapies. A major problem in the analysis of such studies is that market data are censored in some patients owing to withdrawal, loss to follow-up, or death. The objective of this paper is to evaluate the impact of selective dropouts attributable to death or disease progression on the estimates of marker change among different groups. Methods. Data on CD4 cell count in human immunodeficiency virus 1-infected individuals from a clinical trial and a cohort study are used to illustrate this problem and a possible solution. Simulation studies are also presented. Results. When the rate of dropout is greater in subjects whose marker status is declining rapidly, commonly used methods, like random effects models, that ignore informative dropouts lead to overoptimistic statements about the marker trends in all compared groups, because subjects with steeper marker drops tend to have shorter follow-up times and hence are weighted less in the estimation of the group rate of the average marker decline. Conclusions. The potential biases attributable to incomplete data require greater recognition in longitudinal studies. Sensitivity analyses to assess the effect of dropouts are important

Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study

Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary, least squares (OLSE) of individual rates of change (UWLS); weighted average of OLSE (WLS); conditional linear model estimates (CLE), a covariate type estimates proposed by Wu and Bailey (1989); random effect (RE), and joint multivariate RE (JMRE) estimates. The latter method combines a linear RE model for the underlying pattern of the marker with a log-normal survival model for informative drop-out process. The performance of these methods in the presence of missing data completely at random (MCAR), at random (MAR) and non-ignorable (NIM) were compared in simulation studies. Data for the disease marker were generated under the linear random effects model with parameter values derived from realistic examples in HIV infection. Rates of drop-out, assumed to increase over time, were allowed to be independent of marker values or to depend either only on previous marker values or on both previous and current marker values. Under MACR all six methods yielded unbiased estimates of both group mean rates and between-group difference. However, the cross-sectional view of the data in the GEE method resulted in seriously biased estimates under MAR and NIM drop-out process. The bias in the estimates ranged from 30 per cent to 50 per cent. The degree of bias in the GEE estimates increases with the severity of non-randomness and with the proportion of MAR data. Under MCAR and MAR all the other five methods performed relatively well. RE and JMRE estimates were more efficient (that is, had smaller variance) than UWLS, WLS and CL estimates. Under NIM, WLS and particularly RE estimates tended to underestimate the average rate of marker change (bias approximate to 10 per, cent). Under NIM, UWLS, CL and JMRE performed better in terms of bias (3-5 per cent) with the JMRE giving the most efficient estimates. Given that markers are key variables related to disease progression, missing marker data are likely to be at least MAR. Thus, the GEE method may not be appropriate for analysing such longitudinal marker data. The potential biases due to incomplete data require greater recognition in reports of longitudinal studies. Sensitivity analyses to assess the effect of drop-outs on inferences about the target parameters are important. Copyright (C) 2001 John Wiley & Sons, Ltd

Impact of treatment changes on the interpretation of the Concorde trial.

BACKGROUND: The Concorde trial compared two policies of therapy with zidovudine (ZVD) in individuals with asymptomatic HIV infection: immediate or deferred ZDV. Participants in both groups could stop their blinded trial therapy for several reasons and/or could start open-label ZDV. The difference in survival and disease progression between the two groups was estimated allowing for treatment changes. METHODS: The relationship between latest CD4 count, treatment changes and time to AIDS-related complex (ARC), AIDS or death was investigated using time-updated proportional hazards models, but these models gave seriously biased estimates of the effect of ZDV. Therefore, a method based on the comparison of the randomized groups was used. A model relating a participant's events times to the treatment actually received was used to estimate what would have been observed if the deferred group had not received ZDV before ARS or AIDS, and to explore alternative policies for starting Pneumocystis carinii pneumonia (PCP) prophylaxis. RESULTS: The major treatment changes during the trial were the termination of blinded therapy because of adverse events or personal reasons (575 out of 1749 participants), starting open-label ZDV (745 participants), and starting PCP prophylaxis (613 participants). Starting open-label ZDV and PCP prophylaxis were strongly related to latest CD4 count. The uncorrected hazard ratios for immediate compared with deferred groups were 0.89 for time to ARC, AIDS or death [95% confidence interval (CI), 0.75-1.05], 1.01 for time to AIDS or death (95% CI, 0.82-1.24), and 1.26 for time to death (95% CI, 0.93-1.70). After correction for treatment changes, these hazard ratios were 0.79 (95% CI, 0.57-1.11), 1.01 (95% CI, 0.81-1.26), and 1.37 (95% CI, 0.91-2.08), respectively. Correction for PCP prophylaxis made little difference to the results. CONCLUSIONS: Open-label ZDV before ARC or AIDS in the deferred group was likely to have diluted any differences between the immediate and deferred groups. After correction for this dilution, both the estimated benefit of immediate treatment in delaying progression to ARC, AIDS or death and the estimated disadvantage of immediate treatment in accelerating death were somewhat increased, but both remained consistent with chance alone. This study demonstrated the large potential bias inherent in non-randomization-based methods of analysis of clinical trials

Randomization-based methods for correcting for treatment changes: examples from the Concorde trial.

We develop analysis methods for clinical trials with time-to-event outcomes which correct for treatment changes during follow-up, yet are based on comparisons of randomized groups and not of selected groups. A causal model relating observed event times to event times that would have been observed under other treatment scenarios is fitted using the semi-parametric approach of Robins and Tsiatis (avoiding assumptions about the relationship between treatment changes and prognosis). The methods are applied to the Concorde trial of immediate versus deferred zidovudine, to investigate how the results would have differed if no participant randomized to deferred zidovudine had started treatment before reaching ARC or AIDS. We consider issues relating to model choice, non-constant treatment effects and censoring

Randomization-based methods for correcting for treatment changes: examples from the Concorde trial.

We develop analysis methods for clinical trials with time-to-event outcomes which correct for treatment changes during follow-up, yet are based on comparisons of randomized groups and not of selected groups. A causal model relating observed event times to event times that would have been observed under other treatment scenarios is fitted using the semi-parametric approach of Robins and Tsiatis (avoiding assumptions about the relationship between treatment changes and prognosis). The methods are applied to the Concorde trial of immediate versus deferred zidovudine, to investigate how the results would have differed if no participant randomized to deferred zidovudine had started treatment before reaching ARC or AIDS. We consider issues relating to model choice, non-constant treatment effects and censoring