基于C语言实现列条件递归计算及特定时间区间划分需求

Got it, let's work through this problem step by step. Based on your description, we're dealing with survival analysis data (time-to-event with censoring) where we need to:

1. Generate time intervals that end at each death event (status=1)
2. Implement a conditional recursive calculation using an additional column in your dataset

First, let's clarify the data structure (I'll fill in the data_t struct with the extra column you mentioned for recursion—adjust the name/type to match your actual data):

1. Data Structure Definition #

#include <stdio.h>
#include <stdlib.h>

typedef struct {
    double time;       // Column 0: min(failure/censoring time, already sorted ascending)
    int status;        // Column 1: 1 = death (event), 0 = censored
    double covariate;  // Your additional column for recursive calculations
} data_t;

2. Generate Time Intervals Ending at Death Events #

We'll iterate through the sorted data, creating intervals only when we hit a death event. We skip duplicate death times to avoid redundant intervals:

// Generates intervals [prev_death_time, current_death_time] for each unique death event
void generate_death_intervals(data_t data[], int total_records, double intervals[][2], int *interval_count) {
    *interval_count = 0;
    double previous_time = 0.0;  // Start at time 0 for the first interval

    for (int i = 0; i < total_records; i++) {
        if (data[i].status != 1) continue;  // Skip censored records

        // Skip duplicate death times to avoid identical intervals
        if (*interval_count > 0 && data[i].time == intervals[*interval_count - 1][1]) {
            continue;
        }

        intervals[*interval_count][0] = previous_time;
        intervals[*interval_count][1] = data[i].time;
        (*interval_count)++;
        previous_time = data[i].time;
    }
}

3. Conditional Recursive Calculation #

Let's use a common survival analysis example: calculating adjusted survival probabilities (like Kaplan-Meier, but with a covariate adjustment). This is a recursive calculation where each step depends on the previous result and current event data. Adjust the logic to match your specific recursive need:

// Recursively calculates survival probabilities, using the covariate to adjust hazard
void recursive_survival_calc(data_t data[], int total_records, double survival_probs[], double *final_survival) {
    survival_probs[0] = 1.0;  // Base case: survival at time 0 is 100%
    int at_risk = total_records;  // Initial number of subjects at risk

    for (int i = 0; i < total_records; ) {
        double current_time = data[i].time;
        int deaths = 0;
        int same_time_count = 0;
        double covar_sum = 0.0;

        // Group all records at the same time point
        while (i < total_records && data[i].time == current_time) {
            same_time_count++;
            covar_sum += data[i].covariate;
            if (data[i].status == 1) deaths++;
            i++;
        }

        // Calculate average covariate for this time point (adjust this logic to your needs)
        double avg_covar = covar_sum / same_time_count;
        // Hazard = (deaths / at_risk) * covariate adjustment factor
        double hazard = (double)deaths / at_risk * avg_covar;

        // Recursive update: current survival = previous survival * (1 - hazard)
        if (i == same_time_count) {
            survival_probs[i-1] = 1 - hazard;
        } else {
            survival_probs[i-1] = survival_probs[i - same_time_count - 1] * (1 - hazard);
        }

        // Update number of subjects at risk (subtract everyone who left at this time)
        at_risk -= same_time_count;
    }

    *final_survival = survival_probs[total_records - 1];
}

4. Full Example Usage #

Here's how to tie it all together, including loading data from Temp.dat (uncomment the file loading code to use your actual data):

int main() {
    // Example test data (replace with your actual data from Temp.dat)
    data_t data[100] = {
        {1.2, 0, 0.8}, {2.5, 1, 1.1}, {3.0, 1, 0.9},
        {3.0, 0, 1.0}, {4.7, 1, 1.2}, {5.3, 0, 0.7}
    };
    int record_count = 6;

    // Load data from Temp.dat (uncomment this block for your actual data)
    // FILE *file = fopen("Temp.dat", "r");
    // if (!file) {
    //     perror("Failed to open Temp.dat");
    //     return 1;
    // }
    // int idx = 0;
    // while (fscanf(file, "%lf %d %lf", &data[idx].time, &data[idx].status, &data[idx].covariate) != EOF) {
    //     idx++;
    // }
    // fclose(file);
    // record_count = idx;

    // Generate and print death intervals
    double intervals[100][2];
    int interval_count;
    generate_death_intervals(data, record_count, intervals, &interval_count);

    printf("Generated Time Intervals (Ending at Death Events):\n");
    for (int i = 0; i < interval_count; i++) {
        printf("[%.2f, %.2f]\n", intervals[i][0], intervals[i][1]);
    }

    // Perform recursive calculation and print results
    double survival_probs[100];
    double final_survival;
    recursive_survival_calc(data, record_count, survival_probs, &final_survival);

    printf("\nRecursive Survival Probabilities:\n");
    for (int i = 0; i < record_count; i++) {
        printf("Time %.2f: %.4f\n", data[i].time, survival_probs[i]);
    }
    printf("Final Survival Probability: %.4f\n", final_survival);

    return 0;
}

Key Notes: #

• Interval Generation: We skip duplicate death times to avoid creating identical back-to-back intervals. If you need to include every death even with the same time, remove the duplicate check.
• Recursive Calculation: The example uses a covariate-adjusted hazard, but you can modify the logic to fit your specific recursive rule (e.g., cumulative risk, sum of conditional values, etc.).
• Data Loading: The commented block shows how to read your Temp.dat file—make sure the format matches your actual dataset columns.

内容的提问来源于stack exchange，提问作者user177196