Rosuvastatin Adherence App Study in China: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Rosuvastatin Adherence App Study in China (NCT02433288), a randomized na study. The primary outcome is Duration of Treatment.

2. Clinical Question

3. Trial Design

The registry record describes a randomized, two-arm, open-label, parallel study of a smartphone-based patient support tool for patients prescribed rosuvastatin in China. The trial is described as Phase IV in its brief summary, although the phase field is NA. The sponsor is AstraZeneca, and the status is completed. Enrollment is listed as 885. The recorded study start is 2015-07-20, and primary completion is 2016-10-28.

Both groups used smartphone applications. The active application included the patient support tool and clinical evaluation questions. The control application prompted patients with the evaluation questions and was described as being for data collection only. Because masking is listed as none, the trial was open-label; the registry record does not report blinding of participants or investigators.

The primary outcome was duration of treatment in days, with a timeframe limited to 169 days. The reported primary groups were the Active Group, with 431 participants, and the Control Group, with 423 participants. The primary outcome population is identified as FAS. The posted arm means were 156.7 days for the Active Group and 146.0 days for the Control Group, each reported with a standard error.

Secondary outcomes included the percentage of fully adherent patients, treatment adherence, and percent change in LDL-C from baseline. The reported participant counts vary across outcomes. The registry record does not report why the outcome counts differ, how missing observations were handled, the original sample-size justification, or the trial's prespecified statistical analysis plan.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: A total of 885 patients were randomized (1:1) to two groups: an active group which received the smart phone-based patient support tool and a control application used for data collection only.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: FAS

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Population: The baseline characteristics were summarized based on the FAS, which comprised all randomized patients who received the study application successfully.

6. Primary Endpoint

The registry lists the primary outcome as Duration of Treatment, measured over limited to 169 days.

The primary endpoint, Duration of Treatment, measures how long patients remained covered by a rosuvastatin prescription after randomization, using a registry-defined combination of the last patient-reported doctor visit for a prescription and the days of medicine provided by that prescription. The endpoint is measured in days and is limited to 169 days. Its population is listed as FAS.

This endpoint addresses the trial's central question: whether adding a patient support tool to the smartphone application was associated with longer treatment duration than using an application for data collection alone. Treatment duration is a practical outcome for a study concerned with persistence on prescribed therapy. It captures time reflected in prescription-seeking and medicine posted, rather than a direct measure of whether every dose was taken.

That distinction matters when interpreting the result. Treatment duration and adherence are related concepts, but they are not interchangeable. A longer prescription-based duration does not by itself establish that medication was taken as directed throughout that period. The registry record also lists separate secondary measures of fully adherent patients and treatment adherence, which capture different aspects of medication use.

The endpoint description defines the measurement procedure, but does not report further details such as rules for incomplete follow-up or how a last prescription near the endpoint limit was processed. Those details are not reported in the registry record.

7. Results Posted in the Registry

• Active Group: mean 156.70 versus 146.00 on control; difference 10.70 (95% CI 3.84 to 17.56), standardized effect d = 0.21; z-test p = 0.002.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Percentage of Fully Adherent Patients

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Active Group: 4.4% (19/431) versus 9.9% (42/423) on control; risk difference -5.5 percentage points (95% CI -9.0 to -2.1), risk ratio 0.44 (0.26–0.75); unadjusted two-proportion z-test p = 0.002.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Active Group than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Treatment Adherence

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Active Group: mean 37.95 versus 58.19 on control; difference -20.24 (95% CI -26.86 to -13.62), standardized effect d = -0.58; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Active Group than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percent Change in Low-Density Lipoprotein-Cholesterol (LDL-C) From Baseline

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Active Group: mean -21.09 versus -21.72 on control; difference 0.63 (95% CI -3.25 to 4.51), standardized effect d = 0.03; z-test p = 0.750.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Active Group and the control. The interval gives the range of effects that remain compatible with the data.

Across the secondary outcomes, the pattern is mixed in relation to the primary treatment-duration result. The outcomes differ in what they measure: a proportion meeting a full-adherence definition, a mean adherence percentage, and change in LDL-C from baseline. Their directions and statistical evidence do not form a single uniform signal, and they should be understood as complementary measures rather than alternative versions of the primary endpoint.

This distinction helps place the primary finding in context. Prescription-based duration can increase without a corresponding increase in every measure of medication-taking behavior. Conversely, a laboratory outcome may be influenced by factors beyond the support tool and does not directly measure how long a prescription was continued. The registry record does not establish a mechanism linking the intervention to any particular secondary result.

Secondary outcomes usually carry less evidential weight when many outcomes are examined without a stated adjustment for multiple comparisons. Each additional test creates another opportunity for a result to appear statistically notable by chance. The registry record lists multiple outcome measures and analyses, but a prespecified hierarchy, multiplicity strategy, or correction is not reported in the registry record. Consequently, individual secondary p-values should be treated as exploratory or supportive rather than as stand-alone confirmation.

The registry reports different participant counts for the secondary outcomes. For example, the adherence measure and LDL-C outcome do not use the same reported group counts as the primary endpoint. This may reflect differences in available data or outcome-specific analysis populations, but the reason is not reported in the registry record. Such differences limit direct comparisons across outcomes and make missing-data handling important to the interpretation.

The overall secondary pattern therefore neither simply confirms nor cancels the primary result. It suggests that conclusions depend on the outcome definition, the participants with available measurements, and the analysis used. The registry record does not report enough detail to determine whether the secondary analyses were planned as confirmatory, how missing observations were treated, or whether the outcome definitions and analysis populations were fixed before results were known.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Percentage of Fully Adherent Patients
• Treatment Adherence
• Percent Change in Low-Density Lipoprotein-Cholesterol (LDL-C) From Baseline

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is a continuous measure reported as a mean number of days. A mean summarizes the average observed value in each group. The registry reports a mean of 156.7 days for the Active Group and 146.0 days for the Control Group, with standard errors of 2.24 and 2.69, respectively. A standard error describes the precision of an estimated mean; it is not the spread of individual participants' treatment durations.

What does the mean difference express?

The unadjusted recalculation contrasts the Active Group mean with the Control Group mean. The estimated mean difference is 10.7 days. The positive direction means that the observed average was higher in the Active Group. This is a simple comparison of the posted summary statistics, not an adjusted estimate accounting for other variables or the trial's full analysis procedures. Whether the sponsor's analysis used other adjustments is not reported in the registry record.

What does the confidence interval add?

A confidence interval expresses uncertainty around an estimate. For the recalculated primary mean difference, the 95% confidence interval runs from 3.839 to 17.56 days. It gives a range of values compatible with the data and the calculation's assumptions; it is not a guarantee that the true effect lies within that range. Because it does not include zero, the interval is consistent with a positive mean difference under this unadjusted comparison. The interval's width also matters: it shows that the exact size of the difference is less certain than its direction in this calculation.

Why can analyses differ?

The registry reports a Log Rank analysis for the primary outcome, while the recalculation is a mean comparison. A Log Rank test is commonly used to compare time-to-event patterns, whereas a mean comparison summarizes average values. These methods can answer related but not identical statistical questions and rely on different representations of the data. The registry record does not give enough detail about the Log Rank analysis to reconstruct its effect estimate or compare its assumptions with the recalculation. The reported Log Rank p-value is 0.002; the recalculated mean comparison has p = 0.002.

Why can over-dispersion matter?

General statistical explanation: over-dispersion occurs when observed variability is greater than a model or test assumes. If that extra variability is ignored in an analysis, uncertainty may be understated and significance may appear stronger than warranted. This concern is especially relevant to outcomes analyzed as counts or proportions when the statistical model assumes more uniformity than the data support. The registry record does not report an over-dispersion assessment for these outcomes, so it is not possible to say whether it was present or how it was addressed.

For the binary secondary outcome, the registry reports percentages of fully adherent patients and a Chi-squared analysis. A risk difference expresses the difference between the two group proportions in percentage-point terms when represented as a proportion; its sign depends on the group order used. The recalculated estimate is -0.055, with a 95% confidence interval from -0.090 to -0.021. The reported analysis gives p = 0.002. This is an unadjusted secondary comparison, and the registry record does not report a multiplicity adjustment.

The treatment-adherence outcome is reported as a mean percentage with standard deviations, a measure of participant-level variability. Its recalculated mean difference is -20.24 percentage points, with a 95% confidence interval from -26.86 to -13.62. The registry reports a two-sided t-test, a 95% confidence interval from -26.91 to -13.57, and p < 0.001. The recalculated and sponsor-reported intervals are close but not identical; the sponsor's reported analysis takes precedence for the sponsor's own result.

For LDL-C percent change, the groups' posted mean values were -21.09% and -21.72%. The recalculated mean difference is 0.63 percentage points, with a 95% confidence interval from -3.251 to 4.511, and p = 0.75. The registry reports ANCOVA with an estimate of 0.63, a 95% confidence interval from -3.27 to 4.53, and p = 0.751. ANCOVA is a regression-based method that can compare groups while accounting for covariates; the specific covariates are not reported in the registry record.

All recalculations are simple, unadjusted calculations from posted summary statistics. They are useful for an independent descriptive comparison, but cannot reproduce analyses that use individual-level data, covariate adjustment, different endpoint handling, or other model details. General statistical explanation: tests and intervals depend on assumptions about the data and analysis. The registry record does not report enough detail to verify every assumption behind the sponsor's analyses.

11. Confidence Intervals and Precision

The primary recalculated mean difference is 10.7 days, with a 95% confidence interval from 3.839 to 17.56 days. This interval communicates both the estimated direction and uncertainty about the magnitude. Its lower and upper limits are positive, so the unadjusted summary is compatible with a positive difference. The interval does not imply that every participant experienced a longer treatment duration, nor does it show the distribution of individual outcomes.

Confidence intervals are more informative about precision than a p-value alone because they describe a range of effect sizes compatible with the analysis. The primary interval still spans values that differ meaningfully in size, so the precise magnitude is uncertain even though the estimate is positive. The registry's Log Rank result reports p = 0.002 but gives no effect estimate or confidence interval, limiting what can be said about the precision of that particular analysis.

The registry also reports intervals for secondary comparisons. The adherence outcomes have intervals on one side of zero, while the LDL-C comparison interval includes zero and covers differences in either direction. These intervals concern different outcomes and have their own analysis populations and assumptions; they should not be read as one combined measure of certainty.

General statistical explanation: a confidence interval is a procedure that, across repeated use under its assumptions, captures the true parameter at its stated rate. It is not a probability statement about the fixed true value after a particular interval has been calculated. Precision also depends on the quality and completeness of outcome data, the analysis method, and the variability in the measure. The registry record does not report enough detail about missing data or model assumptions to evaluate their contribution to uncertainty.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are participants per arm for the continuous and binary outcomes in this study; they are not total event counts. The planning illustration uses a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It considers effects equal to 100%, 75%, and 50% of the observed effect. The original trial's own sample-size assumptions and justification are not reported in the registry record.

The 80% power column indicates the number of participants per arm that the planning calculation estimates would be needed to have an 80% chance of detecting the specified effect under its assumptions, if that effect were the true one. The 90% power column expresses the analogous planning target for a 90% chance. These are design illustrations, not descriptions of the probability that the completed trial found a real effect. This is not observed power.

Read each effect scenario as a separate planning question. Planning for the full observed effect uses one assumed effect size; planning for 75% or 50% of that effect assumes a smaller difference to detect. Smaller effects generally require more participants to distinguish them from random variation, which is why the planning requirements rise as the assumed fraction falls.

The planning estimates are based on the observed effects in this trial and are not a retrospective validation of the original design. In particular, a small observed effect can produce very large illustrative sample requirements, while an unusually large observed effect can make the full-effect scenario appear comparatively modest. The assumptions also simplify the design as equal allocation and a normal approximation. They do not account for any unreported features of the original protocol or analysis plan.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 10.700; pooled standard deviation σ = 51.064.
2. n = 2 × (1.96 + 0.842)² × 51.064² / 10.700² = 357.5, rounded up to 358 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- 10.7                      # observed difference in means
sigma <- 51.0637900173789                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(2433288)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 358 (80% power), 479 (90% power)
 75% of observed effect: n per arm = 636 (80% power), 851 (90% power)
 50% of observed effect: n per arm = 1,431 (80% power), 1,915 (90% power)
power.t.test: 358.5 
Simulated power at n = 358 per arm: 0.803 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Analyses Reported by the Sponsor

The registry reports a Log Rank analysis for the primary outcome, with p = 0.002. The recalculated summary compares the posted group means and standard errors, giving a mean difference of 10.7 days, a 95% confidence interval from 3.839 to 17.56 days, and p = 0.002. These are not identical analyses: one is the sponsor-reported test, and the other is a simple unadjusted recalculation from group summaries. The registry record does not provide a sponsor-reported primary effect estimate or interval for the Log Rank analysis.

For secondary outcomes, the sponsor's reported values are close to the unadjusted recalculations. For treatment adherence, the recalculated and reported estimates are both -20.24, while their confidence limits differ slightly. For LDL-C change, the reported estimate is 0.63 and the recalculated estimate is also 0.63, with slightly different interval limits and p-values. The fully adherent outcome has a recalculated risk-difference estimate and a reported Chi-squared p-value.

The sponsor's analysis is the authoritative record of the sponsor's own analysis. Recalculated results are supplementary checks from posted summary statistics and may not reproduce the exact analysis method, covariate adjustment, or data handling. The discrepancy flag is false, but that does not make different methods interchangeable. Where the registry reports an analysis, it should be described as reported rather than replaced by a recalculation.

15. Multiplicity, Missing Data and Analysis Populations

The registry record lists multiple outcome measures and reported analyses, including the primary duration outcome and several secondary outcomes. It does not report a multiplicity adjustment, a prespecified hierarchy among secondary outcomes, or a strategy for controlling the overall chance of false-positive findings. Without that information, the reported p-values for secondary outcomes should be interpreted individually and with caution rather than as a unified confirmatory test.

Missing-data procedures are not reported in the registry record. The primary endpoint is identified as using the FAS population, but the record does not define that population or describe how participants with incomplete follow-up were handled. It also does not report whether analyses used imputation, excluded incomplete observations, or applied any sensitivity analyses. The variation in reported group counts across outcomes makes this missing information relevant, but does not by itself establish why the counts differ.

The registry record does not report the original statistical analysis plan, the sample-size justification, interim analyses, or specific rules for censoring and endpoint derivation. The primary outcome description includes a limit of 169 days and defines the prescription-based duration measure, but does not describe how all possible incomplete or unusual records were processed.

The trial is open-label, and the primary endpoint incorporates patient-reported visits and prescription information. The registry record does not report the degree of independent outcome verification or whether those assessing outcomes were masked. These features could matter to measurement and interpretation, but the record does not establish that they biased the results.

These omissions do not invalidate the posted statistics. They do constrain how fully the analyses can be audited and how confidently the reported results can be generalized beyond the defined outcome and available analysis populations. Additional protocol and analysis-plan details would be needed to assess those issues.

16. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: coronary heart disease (1.2%), coronary atherosclerotic heart disease (0.9%), cardiac neurosis (0.5%), pneumonia (0.5%), coronary atherosclerosis (0.2%).

The serious adverse event figures list 23 affected participants in the Active Group, among 431 at risk, and 11 in the Control Group, among 423 at risk. These are group-level counts reported by the registry. The figures describe participants with serious adverse events, not necessarily the total number of event episodes. The registry lists no other adverse events in the corresponding fields, and deaths are not reported in the registry record.

The counts alone do not establish that the application caused or prevented adverse events. The study compared smartphone application approaches among patients prescribed rosuvastatin, and the registry record does not report a causal analysis of serious adverse events or adjustment for differences in exposure and follow-up. The difference in counts should therefore be read descriptively, not as proof of a safety effect.

The listed serious-event terms and percentages include coronary heart disease (1.2%), coronary atherosclerotic heart disease (0.9%), cardiac neurosis (0.5%), pneumonia (0.5%), coronary atherosclerosis (0.2%), and persistent atrial fibrillation (0.2%). These entries identify reported terms and percentages; they do not provide enough detail to determine severity patterns, relatedness to the intervention, or whether terms overlap within participants. Such details are not reported in the registry record.

17. What the Result Does, and Does Not, Mean

The primary posted data show a higher mean prescription-based treatment duration in the Active Group than in the Control Group. The unadjusted recalculated difference is 10.7 days, with a 95% confidence interval from 3.839 to 17.56 days. The registry's reported primary analysis is a Log Rank test with p = 0.002. Together, these results indicate statistical evidence of a group difference under the reported analyses, while the sponsor's reported test does not provide an effect estimate or interval in the registry record.

This finding does not establish that the smartphone support tool improved every aspect of medication use. Duration, adherence, and LDL-C change are distinct outcomes. The secondary results have a mixed pattern, and the registry record does not report a multiplicity plan. The primary endpoint reflects a prescription-based definition and does not directly demonstrate that medication was taken on every intended day.

The result also does not establish a clinically important benefit. The registry record does not state a threshold for a meaningful difference in treatment duration, and statistical significance alone cannot define such a threshold. The confidence interval describes uncertainty in the unadjusted estimate, not the value of the outcome to patients or clinicians.

Because the study was open-label and conducted in the described Chinese setting among patients prescribed rosuvastatin, the result should be interpreted in that context. It does not establish how the same intervention would perform in other populations, health systems, or medication settings. The registry record does not report enough information about missing data, implementation, or outcome verification to resolve those questions.

In short, the posted results support a difference in the primary duration measure between the randomized groups, but they do not answer every question about adherence, biological response, mechanism, safety, or generalizability. Those conclusions require evidence beyond the primary statistical comparison.

18. Why This Trial Matters Statistically

This trial illustrates why the outcome definition matters. Treatment duration was derived from the timing of a last reported doctor visit for a prescription and the medicine posted by that prescription. That measure addresses persistence in a practical, prescription-related way, but it is not identical to taking medication as directed. The study also measured adherence separately, and those outcomes do not move uniformly with the primary result.

It also illustrates the value of distinguishing an estimate from a test. The recalculated mean difference of 10.7 days gives a scale and direction for the group contrast, while its confidence interval from 3.839 to 17.56 days conveys uncertainty about its size. The sponsor-reported Log Rank p-value offers evidence from a different analysis, but without an effect estimate or interval it does not describe the magnitude of that comparison.

The different secondary outcomes show how a trial can generate several related but non-equivalent statistical questions. Adherence proportions, mean adherence, and LDL-C change require different interpretations and have different analysis counts. When the record does not report multiplicity handling or missing-data procedures, the evidence from secondary tests should be weighted accordingly.

Finally, the planning illustrations distinguish the size of an observed effect from the sample size needed to detect smaller effects under stated assumptions. They are not evidence of the study's original design or of observed power. This distinction helps prevent post hoc planning calculations from being mistaken for features of the trial protocol.

19. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The trial was open-label, with no masking listed. Awareness of application assignment may affect behavior or reporting, particularly when outcomes involve patient-reported visits and prescription information. The registry record does not describe independent verification of those data or whether outcome assessors were masked.

The primary endpoint is limited to 169 days and uses a specific prescription-based definition. It may not capture medication use outside that definition, and it does not itself verify day-to-day dosing. The record does not report additional endpoint rules for incomplete follow-up or the handling of atypical prescription records.

The analysis populations differ across outcomes, and the reason for those differences is not reported in the registry record. The primary population is identified as FAS, but the acronym is not defined in the record. Missing-data methods, imputation, sensitivity analyses, and outcome-specific eligibility rules are not reported. This limits assessment of whether missing observations could affect comparisons.

The registry reports a Log Rank primary analysis but does not provide its effect estimate or confidence interval. The simple recalculation is based on posted group summaries and is not a substitute for an individual-level or adjusted analysis. The registry record does not report the full statistical analysis plan, multiplicity adjustment, original sample-size justification, or interim analyses.

Finally, the results concern a smartphone support tool in the setting and population described by the registry. Details about implementation, engagement with the application, and the extent to which patients used the support functions are not reported in the registry record. The findings therefore do not establish how similar tools would perform under different implementation conditions.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Rosuvastatin Adherence App Study in China — NCT02433288.
• Registry record last updated 2019-01-31; status last verified 2018-08.
• Results first posted 2019-01-31.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

The registry describes a completed randomized, two-arm, open-label, parallel study of a smartphone-based patient support tool for patients prescribed rosuvastatin in China. The active application included patient support and evaluation prompts; the control application was for evaluation prompts and data collection. Enrollment is listed as 885, and the primary outcome population is identified as FAS.

The primary measure was treatment duration in days, limited to 169 days. Posted means were 156.7 days in the Active Group and 146.0 days in the Control Group. The unadjusted recalculated mean difference was 10.7 days, with a 95% confidence interval from 3.839 to 17.56 days. The registry reports a Log Rank p-value of 0.002.

Secondary outcomes covered full adherence, treatment adherence, and LDL-C change. Their findings are not uniform, and multiplicity handling, missing-data procedures, and the original statistical analysis plan are not reported in the registry record.